Mechatronicsengineeringandelectricalengineering-1

MECHATRONICS ENGINEERING AND ELECTRICAL ENGINEERING

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PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MECHATRONICS ENGINEERING AND ELECTRICAL ENGINEERING (CMEEE 2014), SANYA, HAINAN, P.R. CHINA, 17–19 OCTOBER 2014

Mechatronics Engineering and Electrical Engineering

Editor

Ai Sheng Information Science and Engineering Technology Research Association (ISET), Hong Kong, China

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CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informa business © 2015 Taylor & Francis Group, London, UK Typeset by V Publishing Solutions Pvt Ltd., Chennai, India Printed and bound in Great Britain by Antony Rowe (A CPI-group Company), Chippenham, Wiltshire All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publisher. Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. Published by: CRC Press/Balkema P.O. Box 11320, 2301 EH Leiden, The Netherlands e-mail: [email protected] www.crcpress.com – www.taylorandfrancis.com ISBN: 978-1-138-02719-0 (Hbk) ISBN: 978-1-315-73446-0 (eBook PDF)

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Table of contents

Preface

xi

CMEEE 2014 committee

xiii

Battlefield situation cognition system based on big electronic reconnaissance data processing S.M. Xu, H.B. Yu & F.H. Chu

1

Automatic recognition of waste rock in top coal caving based on digital image processing B.P. Wang, Y.J. Wang & Z.C. Wang

5

The application of Dijkstra algorithm based on multi-weighted distribution in GIS Sh.M. Wang, H.T. Luo & B.W. Wang

9

Design of battery charging and discharging circuit H.Q. Zhan & Z.P. Li

15

Automatic flight control of a certain UAV using LQG design Sh.Y. Zhang

19

Research on online and remote calibration technology of electronic belt scale based on weights superposition method X.P. Shang, H.Y. Chen & Z.W. Huang

23

The application of wavelet filter in static synchronous compensator L. Zhang & N. Liu

27

Fractional-order differential application and research in pavement cracks image enhancement W. Jiang, Y.W. Liu & H. Zhang

31

Molecular algorithm in solving the shortest path problem of the application and research L. Zhang

37

A method about video quality assessment in video phone service over 3G network C. Feng, L.F. Huang & W.J. Xu

43

Analysis on the degree of the international fragment of China’s manufacturing industry based on processing trade Y.H. Yang

49

The voltage-controlled low-pass filter based on FPGA frequency measurement Sh.Q. Ma, L.R. Zheng, J.F. Liu & Y.P. Ji

53

Departure capacity assessment of close staggered parallel runways J.G. Kong, X. Li & W.B. Ding

59

The control design of blast furnace clay gun play mud quantity B.H. Jiang, J. Mei, X. Zhao, D.C. Gao & H. Liu

63

Research and implementation of coal traffic video management system based on the technology of image tracking and recognition S.J. Jin Residents air conditioning load management model studies and impact analysis B. Li, S.W. Li, S.X. Zhang, X.S. Jing, F. Wu & X.J. Weng

67 71

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Multi-objective optimized scheduling for hydro-thermal power system W.J. Liu & P.F. Cheng

77

An optimal space-borne Solid-State Recorder based on domestic chips S. Li, Q. Song, J.W. Song, W. Wang, Y. Zhu & J.S. An

83

Study on additional damping controller for VSC-HVDC to prevent low-frequency oscillation Z.H. Wang, Y. Li & X.Y. He

87

Lightning surge analysis for 220 kV AC double circuit transmission line using LPTL Y. Ma, C.S. Liu, Y. Liu, G. Chen, T.X. Xie, Z.C. Zhou & F.B. Tao

91

Fault analysis of Metal Oxide Varistor (MOV) for series compensation capacitor banks Y. Ma, Y. Liu, P. Li, G. Chen & Z.Z. Zhou

97

Experimental study of the human retinal security by LED light L. Zhao, L. Lu, Sh.Y. Ren, L.Q. Geng & C.T. Li

103

Analysis of the phenomenon in LFO of AC electric locomotive J.J. Ding, Y. Tu & S.B. Gao

109

The prototype of fatigue damage detection by the method of Metal Magnetic Memory Testing X.Y. Yu & X.Y. Fu

115

Rotor composite faults diagnosis of asynchronous motor based on complex power Zh. Wang, Zh.F. Lin, Ch. Li & Zh. Zeng

119

Commissioning and operation of Heze some leather wastewater treatment plant K. Wang, B. Liu, H.D. Zhang & W. Zhao

125

Engineering depth treatment of pharmaceutical wastewater by magnetization catalytic oxidation-A2O-MBR combined process W. Zhao, B. Liu, H.D. Zhuang, K. Wang & H.D. Ji Study of broadband microstrip circulator using YIG single crystal Zh.Q. Cheng, Y. Luan, M.Sh. Jia & X.X. Lian

129 133

Decoupling mechanism for suspension laryngoscopy using a curved-frame trans-oral robotic system X.Y. Fu, X.Y. Yu & J.T. Seo

137

Calculation and analysis on shunt coefficient of short-circuit current inside Fuzhou 1000 kV Ultra-High Voltage substation B. Zhang, B. Tang, W.H. Ma & J.Y. Zou

141

Mechanical analysis of reliability test device of Head-Up Display’s retraction and extension Y.G. Liu & Y.X. Wang

147

Study on electromagnetic loop rejection in Liaoning power grid K. Gao & Zh.H. Wang

151

Study on effect of evaluating corrosion inhibitor for circulating cooling water in dynamic simulation method Y.P. Li, Y. Li & F. Liu

155

Research on the impulse current dispersal characteristics of tower grounding devices Ch.Ch. Zhu, T. Wang, X.F. Tong, P.X. Xing, Zh.Q. Feng, H.L. Lu & L. Lan

159

Innovative design of centrifugal oil press C.Q. Zhong & Y.L. Zhang

163

Analysis of the centrifugal oil press vibration C.Q. Zhong & Y.L. Zhang

167

HPLC method for determination of concentration of Nifekalant in human plasma X. Xie

171

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Research of over-voltage in air-core reactor using Waveform Relaxation based on Lanczos L.M. Bo, B. Bao & Y. Xu The design of automatic sorting and staking system based on PLC S7-200 L.Y. Fang, X.Y. Zhou, F.G. Wu & X. Zhang Study of dynamic optimized allocation policy for AGC regulation power based on fuzzy-Q learning algorithm H. Qian, L. Zhou, W.X. Jin & J.B. Luo Fault diagnosis of rolling bearing based on Fisher Discriminant Analysis W.B. Zhang, Ch.G. Liang & G.Ch. Li

175 181

187 195

The voltage coordinated control strategy of the power grid which includes large scale wind power R. Shi, R. Jiao, Z.J. Chi & X.N. Kang

199

Research on predict Direct Capacitor Power Control of voltage source PWM rectifier, applied to electric vehicle charging and discharging field Ch. Gong, L.F. Ma, Zh.J. Chi, W. Li, B.Q. Zhang & Y.T. Zhao

203

Development of 2500V SMB-seagull SiC JBS diodes G. Chen, Q.M. Zhang, S. Bai, A. Liu, L. Wang, R.H. Huang, D.H. Li & Y.N. Li

209

Feature-based intelligent machining method based on NX X.H. Zhan & X.D. Li

213

The application of Analyze Formability-One-Step in progressive die strip design X.D. Li & X.H. Zhan

217

The research on the signal reconstruction of the angular rate sensor L.Y. Yuan, W.G. Zhang & X.X. Liu

221

Research on the application of Beidou and GPS dual-mode timing system in the space flight tracking ship F. Zhang, Y.B. Ren & Y. Zhou

225

Study of the wind-thermal allocation ratio for wind and thermal bundled power as a source to participate in power planning X.M. Cao, T.Q. Liu, X.T. Hu, Z.H. Chen, F.J. Wang & T.Y. Guan

229

A design method of inductance-capacitance filter circuit for reducing current harmonics of high-speed motor Y.Q. Mo & P.J. Dong

235

Burr detection algorithm based on machine vision Zh. Shi, Ch.L. Xi, H.L. Li, F.Sh. Tan & J.F. Yan

243

Numerical simulation and experimental study on the anti-overload ability of cylindrical roller bearing in a short time Y.G. Ni, Y. Li, S.E. Deng & X.F. Li

249

Numerical simulation and analysis of wavefront reconstruction iterative method in radial shearing interference Y.F. Wang & Z.S. Da

255

Minimization of stator loss for high speed permanent magnet motor X.Q. Liu Iterative Adaptive Algorithm based on the cross covariance matrix of acoustic pressure and particle velocity C.R. Zhang, J.F. Cheng & B.L. Ma A method to establish a continuous operational reference station in urban districts S.B. Wang, X.J. Du, H.J. Li & W.P. Xu

259

265 271

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The research on the leak repairing method of the recoil mechanism N. Li, W. Jiang, H.P. Guo, S. Wang & L.M. Chen

275

Image retrieval research based on feature points and affine invariant moments B.Q. He & Z.M. Wang

279

Research on Electric Vehicle development in Beijing L. Zhang, M.Y. Pan, Z.J. Chi, Y.X. Chen & X.N. Kang

285

Lubrication performance analysis of three axial-grooved gas-lubricated journal bearing with micro grooves Y.J. Lu, F.X. Liu, Y.F. Zhang, C. Tian & M. Li

291

A new configuration of current source converter applied in HVDC J.Y. Zhao, F.M. Zhang, F.G. Liu & Y.H. Liu

297

Application of high-order grey forecast model in the short-term load forecasting X.Y. Huang & L. Yang

303

Offshore wind power scale development trends and related policy research Y. Zeng, J.S. Luo, X.L. Wang, P. Song, H.W. Huang & H.L. Bao

309

A power response characteristics equivalent model for the hybrid energy storage system P. Chen, F. Xiao, X.W. Wang, H. Yang, Z.L. Yang & L.J. Wang

313

The research for power allocation strategy of the hybrid energy storage units in distributed generation system Z.L. Yang, P. Chen, L.J. Wang, Y.H. Wang, F. Xiao, X.W. Wang & H. Yang

319

Research on the control of temperature of batch reactors by multi-media in the reactor jacket H.B. Li, D.Y. Feng & H. Liu

325

Three-phase voltage source PWM rectifier based on space-vector algorithm and one-cycle control L.F. Ma, Ch. Gong, Zh.J. Chi, J.M. Cao & L. Zhu

329

Joint positioning method with radar based on wavelet entropy H. Yu, J. Liu, M. Wang, R. Guo & Y. Yang

335

Optimal design of locking pin for surface AUVs launcher under uncertainty S.Q. Yang

341

A low temperature drift and heavy load bandgap reference voltage with adjustable output X. Xu & J. Jiang

345

Analysis of energy coupling between the computer chassis and electromagnetic pulse J. Liu, Y.F. Wang, Z.X. Chen & C.D. Yu

349

Analysis of electromagnetic shielding effectiveness of the chassis with holes under different polarization directions Q.B. Deng, C.D. Yu & Z.P. Lian

353

Analysis of characteristic parameters of pulse impact on the electromagnetic coupling of the chassis C.D. Yu, Z.X. Chen, Y.F. Wang & J. Liu

357

A new type of control method for the variable-speed wind turbines based on the PID neural network T. Li, X.Y. Hou, H.Y. Lin, Q.Y. Liu, L. Zhao & H.J. Liu

361

Research on the control method for the torque of wind generator based on data-driven T. Li, X.Y. Hou, H.Y. Lin, L. Zhao, H.J. Liu & Q.Y. Liu

365

Construction of robustly stable interval polynomial T.A. Ezangina, S.A. Gayvoronskiy & S.V. Efimov

369

A MEMS digital seismometer with new structure J. Guo & S.H. Xu

373

viii

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A type of real-time vibration monitoring system based on Ethernet and RS-232 S.H. Xu, J. Guo & P.P. Li

377

Study on the transient voltage stability of distribution systems considering large-scaled dispersed EV charging Y.X. Chen, Z.J. Chi, P.W. Zheng & X.N. Lin

381

Fault diagnosis and failure rate analysis of power transformer based on cloud relation space model L.J. Guo & S.M. Tao

387

Analysis on technical innovation and regional disparities growth of China J. Xiong & W.W. Zhang

391

Author index

395

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Preface

The 2014 International Conference on Mechatronics Engineering and Electrical Engineering (CMEEE2014) was held October 18–19, 2014 in Sanya, Hainan, China. CMEEE2014 provided a valuable opportunity for researchers, scholars and scientists to exchange their new ideas and application experiences face to face together, to establish business or research relations and to find global partners for future collaboration. CMEEE2014 was a most comprehensive conference focusing on Mechatronics Engineering and Electrical Engineering. The papers in this book are selected from more than 500 papers submitted to the 2014 International Conference on Mechatronics Engineering and Electrical Engineering (CMEEE2014). The book is divided into 4 sections, covering the topics of Mechatronics, Electrical Engineering, Control and Automation and Other Engineering. The conference will promote the development of Mechatronics Engineering and Electrical Engineering, strengthening international academic cooperation and communications. We would like to thank the conference chairs, organization staff, and the members of the International Technological Committees for their hard work. Thanks are also given to CRC Press/Balkema (Taylor & Francis Group). We are looking forward to seeing all of you next year at CMEEE2015. Yizhong Wang Tianjin University of Science and Technology, China

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

CMEEE 2014 committee

ORGANIZER Information Science and Engineering Technology Research Association (ISET), Hong Kong, China CONFERENCE CO-CHAIRS Ai Sheng, Information Science and Engineering Technology Research Association (ISET), China Yizhong Wang, Professor, Tianjin University of Science and Technology, China COMMITTEE Jihe Zhou, Professor, Chengdu Sport University, China Hongmin Gao, Professor, Beijing Institute of Technology, China Chunguang Xu, Professor, Beijing Institute of Technology, China Haitao Li, Professor, Southwest Petroleum University, China Zhiming Liu, Professor, Liao Ning Institute of Science and Technology, China Shanglin Hou, Professor, Lanzhou University of Technology, China N.K. Sharma, Professor, The Glocal University, India Kanglin Wei, Professor, Chongqing University, China Je-Ee Ho, Professor, I-Lan University, Taiwan Chunpeng Li, Professor, Quanzhou Normal University, China

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Battlefield situation cognition system based on big electronic reconnaissance data processing S.M. Xu, H.B. Yu & F.H. Chu Electronic Engineering Institute, Hefei, China

ABSTRACT: Grid electronic reconnaissance system can be concentrated in a certain area in the layout, interconnection and interflow, share collaboration between nodes, the overall performance is high, and the battlefield environment cognitive ability is improved greatly. This paper constructs the network electronic reconnaissance system and situational cognitive system, using big data analysis and processing, to make the data more relevant and more convenient, so that the battlefield is more transparent. Keywords: 1

Electronic Reconnaissance; Big data; Battlefield situation; Cognition 2

INFORMATION BATTLEFIELD

BATTLEFIELD SITUATION COGNITION

Situational cognitive process is a complicated human–machine system, involved in the physical domain, information domain and cognitive domain fields. The battlefield originates in the real environment of battlefield of physical domain. The collection, process and distribution of information occur in the information domain. The cognition for share situation map and decision on this basis forms in the cognitive domain, as shown in Figure 1. Physical domain is the practical condition from different fighting equipment, combat platform, command and control system which carry all kinds of electronic device, such as radio. Physical domain is the place where information comes from and decision result backs feed. Information domain contains the information perception, fusion, storage, transmission of battlefield electromagnetic radiation, and then shares the trend chart to decision maker for decision making.

Since the 21st century, the development of information technology has greatly promoted a revolution in military affairs, information-based war, fighting methods and operational ideas, and leading role of the military war process technology is played by information technology. Battlefield command relies on to the microelectronic technology, communication technology and computer technology as the core of the command and control system. Battlefield reconnaissance, surveillance and evaluation need all kinds of advanced electronic, optical sensing devices. Assault weapons rely more  on a variety of precision-guidance technology supported by information technology. In the information battlefield, all kinds of electronic equipments are playing an increasingly extensive role. For example, all kinds of station of radio communications equipment, loading on a variety of combat platform, always undertake the significant role for combat troops to provide command, control, communication and intelligence. Electronic reconnaissance field is an important means of battlefield situation cognition, especially to the particular area of the battlefield radio source signal to search, intercept, measure, find direction, position, monitor, analyze, recognize and get access to its technical parameters, function, type, location and purpose. Battlefield electron reconnaissance based on the real-time monitoring data and historical data fusion processing further forces the current location and trend of know each other, so as to analyze its military deployment and operational plans, etc., to achieve the enemy and know yourself. Therefore, it is of great military significance.

Figure 1. Principal diagram of battlefield situation cognition progress.

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Figure 2.

Data, information and knowledge hierarchy. Figure 3. Networked electronic reconnaissance system architecture.

Cognitive domain is the domain where decision makers accomplish situation perception, cognition, comprehension, inference, decision, etc. Cognitive domain exists in the decision maker’s mind and it is the source of value, belief and decision. Battlefield situation cognition accomplishes the process from collecting, analyzing and processing of the electronic reconnaissance data to battlefield situation knowledge acquisition. The meaning of data, information and knowledge is different, and also there is a connection on some level. Data is in a formalized way for a kind of fact and concept, suitable for the person or automatic device for communication, interpretation and processing, it is any meaningful or gives the meaning of the expression form, such as characters or numbers. In computing, data are input to the contents of a computer program, they through the processing of arithmetic or logical operations, obtain the result of processing. Here, compared with the word “information”, “data” refers to the source data or raw data, and “information” is defined as the data obtained through the study of the processing of data. In general, the so-called knowledge or cognition is acquired in the practice of transforming the objective world people’s basic concept, knowledge, experience and laws, and it is the foundation of human intelligence activity. From the computer science point of view, knowledge is the result of information integrated processing and, in the process of comprehensive information through mutual comparison, combined into meaningful links. As shown in Figure  2, the bottom is the data, which is the source of information and original information, express information in data can be. Information comes from processed and organized data. After summing up and summarizing, information becomes knowledge. The higher the position in the pyramid, the higher the level of abstraction. Furthermore, with the increasing level, the less the data required to express.

3

GRID ELECTRONIC RECONNAISSANCE SYSTEM

Single electronic reconnaissance equipment although is designed to be powerful, in the use process due to the frequency coverage and limited space covering function, the signal intercept probability is not high, the signal source interactivity is not sufficient, equipment between the data sharing is not strong, and correlation analysis is not enough. Therefore, the battlefield signal reconnaissance and intelligence analysis under complicated electromagnetic environment is very difficult. As shown in Figure  3, networked electronic reconnaissance system using low-cost RF-sensors, can decorate in the densely populated area, forming a grid network, and improve the traditional radio spectrum monitoring “point to surface” monitoring mode, solve the problem of the integration between the information system and realize the data interaction and information sharing. Compared with a single high-performance computer, the grid technology has a much more powerful processing capacity, the degree of information integration and sharing is relatively high. Grid reconnaissance is on the existing network technology to establish a higher level, more comprehensive resource sharing and its computing technology is more advanced. 4

BIG DATA PROCESSING

In network electronic reconnaissance system, as data are obtained continuously from all kinds of sensors, the data quantity is growing at an unprecedented rate, and due to the increasing amount of data and a variety of data format, data organization, analysis and storage have become difficult,

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in the field of networked electronic reconnaissance mainly includes the following: (1) Big data analysis is used for multi-source information fusion. It is the main task of the multi-source information fusion based on mass, multi-source, multi-type data (such as text, images, video and voice), they are related and converted to all kinds of special intelligence (e.g., Communications Intelligence (COMINT), Electronic Intelligence (ELINT), Radar Intelligence (RADINT), remote sensing information (TELINT)). (2) Big data analysis is used for relationship analysis. In grid electronic reconnaissance system, information in the process of intelligence analysis from a single node (i.e., point) is not enough to support decision making and the relationship between each unit (i.e., line and surface) is analyzed. Relationship analysis is one of the big data analysis mainly used in the field of intelligence, and eventually it generates comprehensive information support needed for the decision. (3) Big data analysis is used for situational cognition. The network electronic reconnaissance system of many sensors will produce massive monitoring data, as well as the long-term accumulation of historical data for processing and analysis. Through the combination of perception, cognition and decision support in innovative ways to use huge amounts of data, commanders greatly improve the ability to extract high value information from huge amounts of data and the cognitive ability of the battlefield environment, able to control and make decisions independently of autonomous system, fundamentally change decision model to improve the ability of rapid response.

which also makes it increasingly difficult to get the valuable information quickly. At present, the data of networked electronic reconnaissance system faces several main characteristics: (1) With the full spectrum of information awareness growing demand, information awareness band continuously widened, from long to short wave, from ultra-short wave to microwave, all the way to the millimeter wave, the terahertz. At the same time, various broadband radar, the emergence of broadband communication system, also to instantaneous processing bandwidth requirement enhances unceasingly, causes the AD rate rising, thus the amount of data obtained signals intelligence reconnaissance is growing rapidly. (2) The detection signal is more and more complex, and there are many different kinds of modulation mode. According to the purpose of transmission, transmitting signals can be divided into communication signals, radar, radio fuze signal, guidance signals, navigation, etc. According to the signal, spectrum can be divided into long-wave, mediumwave and short-wave signals, ultrashort-wave signals, microwave, infrared signals, such as the laser signal. According to the way of electromagnetic wave propagation, signals can be divided into surface wave signals, ground wave, the sky wave signals, troposcatter, etc. Therefore, many complex signals make data analysis and intelligent extraction more and more difficult. (3) Huge amounts of data contain a large amount of noise or interference, as well as various signals of their own. Military intelligence reconnaissance need to find out little but useful information in the dense signal in a timely manner. (4) Military intelligence reconnaissance needs high instantaneity, ideally need real-time analytical information content, which require high processing speed. Therefore, networked electronic reconnaissance system has entered the era of big data. The author of Big Data Era, Viktor MayerSchönberger said “the real value of the big data is just like the iceberg floating in the sea, one eye can see only the tip of the iceberg, the vast majority of all hidden under the surface”. If big data is compared to an industry, the key to profit of this industry is to improve the processing capacity of the data and value-added by processing data. Also because of this, many experts say it is the era of the three pillars of data, technology and thinking. We not only need huge amounts of data, but also analyze the data of professional skills to set up innovative thinking in a unique way for the potential value of the depth of mining data. Therefore, the big data technology is applied to network electronic reconnaissance system, in order to realize the extract valuable information from large data and then for the purpose of the auxiliary decision-making. Big data technology application

5

CONCLUSION

A kind of grid electronic reconnaissance system is built on the concept of big data network architecture, and big data technology is used in the data processing and intelligence analysis process with the real-time monitoring data produced by many sensor and the long-term accumulation of historical data, so the cognitive ability of commanders on the battlefield environment is significantly improved through the combination of perception, cognition and decision support. REFERENCES [1] Big data in 2020[EB/OL]. [2012-12-24]. www.emc. com. 2012. [2] TERRY COSTLOW. Big Data Pose Big Challenge for Military Intelligence[Z]. Defense systems. 2012. [3] Wang Fu-rong. The analysis and application of grid radio monitoring and management, Zhejiang university of technology master’s thesis, 2013.03.

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[4] Viktor Mayer-Schnberger. Big Data Age, Zhejiang people’s publishing house, 2013.01. [5] Yang Xiao-niu, Yang Zhi-bang. The next generation of signals intelligence reconnaissance system architecture of the concept of big data applications, Journal of China institute of electronics, 2013, 8(1): 1–7.

[6] Tang Shan-hong, Xu Hong-ru. Big data: power technology in the information age competition big data research and development new areas—the United States, Defense, 2013, 2: 73–77. [7] Wang Shan, Wang Hui-ju. Architecture: big data challenge, the present situation and prospect, Chinese Journal of Computers, 2011, 34(10): 1741–1752.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Automatic recognition of waste rock in top coal caving based on digital image processing B.P. Wang & Y.J. Wang Shandon Jiaotong University, Jinan, Shandong, China

Z.C. Wang School of Mechanical Engineering, Shandong University, Jinan, Shandong, China

ABSTRACT: The recognition of the waste rock in top coal caving was investigated via digital image processing. First, the hydraulic support was processed to install the capture device. Subsequently, the images were pre-processed by median filter. Finally, the histograms were obtained and the parameters were also calculated. Three statistical characteristics which were mean, variance and energy were extracted from the histogram. The results show that the variance can be regarded as a feature to recognize the waste rock. Keywords: 1

coal-rock interface recognition; digital image procession is shown in Figure 1. Because of poor lighting at the site, the image-capture device needs to have its own lighting equipment. The device gets the images and transfers them to the signal-receiving device by the wireless transmitter. Figure  2  shows the connection diagram of the required hardware.

INTRODUCTION

Fully mechanized top coal mining is a high-yield, low-power and efficient technology. It has been popularized. In the top coal mining process, one of the problems encountered is how to monitor the degree of the caving. Now it relies on the artificial visual judgment. So security problems occur because of the dusty conditions. And also manually it is difficult to accurately monitor the top coal caving fall extent, and so sometimes the coal mining did not finish, and sometimes it mined rock. And therefore it inevitably leads to over-caving process and less-caving status. Over-caving status increases the content of waste rock, and leads to high transport costs. Over-caving will lose coal, resulting in reduced recoveries. So there is an urgent need to study the automatic identification of waste rock. Many researchers have done a lot of research on this issue, and have achieved certain results. The γ ray, infrared and radar reflection methods are used to solve the problem[1–3]. However, there is not a reliable method yet. In this paper, a digital method is discussed. 2 2.1

2.2

Image processing

The purpose of pre-processing is to make the image clear. The median filter is an effective method that can suppress isolated noise without blurring sharp edges[4]. When performing median filtering, each pixel is determined by the median value of all pixels

APPROACH Sample image acquisition

In order to obtain clean images, the hydraulic support is processed. The capture device is placed in the back of the tail beam. The location of installation

Figure 1.

The installation of the camera.

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where N is the total pixels and ni is the number of pixels whose grey scale is ri. 3

COAL–ROCK INTERFACE RECOGNITION

The images were collected in Xinglong Zhuang Coal Mine. The median-filtered images of coal and rock are, respectively, shown in Figures  3 and 4. Contrasting the two images, some difference can be found. The coal is black, while the rock is essentially grey. The corresponding histograms are

Figure 2.

The hardware connection diagram.

in a selected neighbourhood (mask, template, window). The median value m of a population (set of pixels in a neighbourhood) is that value in which half of the population has smaller values than m, and the other half has larger values than m. Mathematically speaking, the grey-level histogram is the function of grey-level statistical properties and grey values. It expresses the proportion of areas or pixels of different grey scale in a whole image. It reflects the statistical characteristic of an image and also expresses the proportion of areas or pixels of different grey levels in a whole image[5]. Based on the significant difference in the grey level of the coal and rock, it can be obviously known that the information contained in an image of the coal or rock is very different. Three statistical characteristics,[6] which are mean, variance and energy, from the histogram are extracted as the characteristic parameters to distinguish the coal and the waste rock. Mean: μ = aver(ri)

Figure 3.

The image of coal.

Figure 4.

The image of rock.

(1)

Variance:

σ2

i

μ )2

(2)

Energy: 255

s

∑ (ri −

)3 p( p(ri ) / σ 3

(3)

i =0

A two-dimensional image is set for f(x,y), whose scales range in r0, r1, …, r255. The histogram is: P(ri) = ni /N

(4)

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after reflection. So the variance is large. The waste rock is essentially grey. And the reflection performance is poor. So the variance is smaller than that of the image of the coal. Therefore, the variance can be as used as a feature to distinguish the waste rock from coal. ACKNOWLEDGEMENTS

Figure 5. Table 1.

Coal Rock

This work was supported by the National Natural Science Foundation of China (Grant No. 51174126), Shandong Science and Technology Development Plan (Grant No. 2013YD05005) and the Foundation Shandong Jitong University (Grant No. Z201315).

The histogram of coal.

REFERENCES

Comparison of the statistical characteristics. Mean

Variance

Energy

145.9 153.3

825.9 263.0

6.9 6.1

[1] K.W. Plessmann, B. Dickhaus and S. Scheytt 1993 Control Eng Practice. 11 457. [2] J. Asfahani and M. Borsaru 2007 Applied Radiation and Isotopes. 65 748. [3] Ren Fang, Yang Zhaojian, and Xiong Shibo 2003 Chinese Journal of Mechanical Engineering. 3 321. [4] Information on http://www.cs.ioc.ee/∼khoros2/nonlinear/median-filtering/front-page.html. [5] Xinli Song, Xifeng Zheng, Liqing Ling 2009 Chinese Journal of Liquid Crystals and Display. 24 140. [6] Jeng-Horng Chang, Kuo-Chin Fan and Yang-Lang Chang 2002 Image and Vision Computing. 20 203.

shown in Figures 5 and 6. The calculation results are shown in Table 1. It can be found that there is a larger difference in the variance than the other two parameters between the coal and the rock. 4

CONCLUSIONS

Coal is black. The grey value is relatively low in the image of coal. But part of it is bright

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The application of Dijkstra algorithm based on multi-weighted distribution in GIS Shi-Min Wang, Hai-Tao Luo & Bo-Wen Wang Beijing Technology and Business University, Beijing, China

ABSTRACT: Traditional Dijkstra algorithm gets the optimal path from the source node to the destination node through iteration of path length. In fact, the shortest path cannot always meet the demand, and the path distance sometimes cannot be used as the only weight standard. This paper discusses a kind of improved algorithm of weight allocation, which redistributes the weights according to the actual macrofactors and joins in the coordinate analysis of GIS system and information such as actual traffic jams coefficient, in order to make the optimal path selection more reasonable. Keywords: 1

weight distribution; shortest path; Dijkstra algorithm

INTRODUCTION

1.1

Weight distribution

We can perform the acquisition and inputting of information in the optimization process through real-time information sharing in the GIS[4]. We classify the optimal path weights into three parts: the transportation distance, road traffic and transportation cost.

Geographic Information System (GIS) is a kind of comprehensive application system based on geography and computer science, as well as communication and space science, which is widely used in logistics, city traffic, electric power facilities repair, fire rescue etc. Its outstanding advantage is that it can plan the best route to the destination quickly according to the current geographic information, to provide reference for the rescue and driving. The path planning system is a GIS, which usually uses algorithm such as Dijkstra algorithm, Floyd algorithm and matrix algorithm[1]. The Dijkstra algorithm is commonly used in network topology, such as choosing a path in wireless mesh network[2]. Because Dijkstra algorithm adapts optimal path selection between two nodes in the network path well, it is most suitable for the application of decision in GIS system. This paper uses Dijkstra algorithm based on multi-weighted distribution in the path optimization in GIS. Most path-planning algorithms stake the shortest distance as the only standard, which are not based on the requirements of the users. With the development of economy and society, the shortest distance has been difficult to meet the requirements of the users, and people pay more attention to the optimal path problem with multifactors and multiweights[3]. Strong analysis abilities of GIS provide data to support multi-weighted path planning. After improvement, the algorithm can propose path planning scheme according to different customers’ requirements, which is more realistic and has more availability.

1. Transportation distance Transport distance directly relates to fuel consumption, vehicle wear and fatigue level of the driver, which directly determines the economic benefit. As the calculation of transport distance is simple, it is the most used index to determine the distribution routes. The time from the beginning to the end of delivery is accumulated by adding the transportation time in highway, waterway, railway, aviation, station and wharf cargo. 2. Transportation time Transportation time accounts for the majority of the whole logistic time, especially long-distance transportation. Therefore, shortening the delivery time is of decisive importance in the entire circulation time. In addition, the short ending of transportation time relies on accelerating the transportation turnover to play its full effectiveness and improve the transportation capacity. 3. Transportation cost All costs incurred in the process of carriage generally refer to the total fees that the owners cost including fees on roads, railway, aviation, transfer and other related services.

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1.2

shortest path length from the source point to each end point, the path[n] is a collection of the corresponding path, S is the set of shortest path which has been found, U = V−S is a set of all vertices not containing the source point initially.

The path search principle of GIS system

Road traffic network GIS system in route choice, subdivided into three elements which is point, line and plane, through the combination of these three factors describes the specific physical location, size, shape and other characteristics. The point represents the converging points of two or more than two roads (cross and through). All roads abstracted into line, which is the connection between point and point. Roads and road in the line of the closed area become plane. The form of node data’s storage is point number, the abscissa, ordinate values and correlation line. The form of line data’s storage is line number, starting point, end point and the line weight. The form of surface data’s storage is plane data, the peripheral circuit data and data domain node data. Because of the above-mentioned characteristics, envisaged by the improved and combined with the spatial distribution of GIS Dijkstra algorithm, the complex traffic network path selection has a great help. 2

1. There is only a source point ‘a’ in the set S of the shortest path solved in the initialization, S = {a}. 2. Select a vertex K which has a minimum distance to a source point v from U. Add k in the set S, the selected distance is the shortest path length from the v to k, and the edge does not constitute a loop. 3. Take k as the new middle point, and modify the distance of each vertex in U. If the distance from source point v to vertex u is shorter than the original distance, modify the distance value of vertex u, so the modified distance is the addition of distance of k and the weight value from vertex k to edge u. 4. Repeat the steps of (2) and (3) until all vertices are contained in S. As the above-mentioned steps of the algorithm shows, the core step in the process of Dijkstra algorithm is selecting a minimum weight link node which is never labeled. Distribution of weights has become an important factor affecting the final result of the algorithm. In addition, Dijkstra algorithm is based on the single index (path length) to choose the path. Multi-objective path weight selection cannot be directly solved by it. It needs a corresponding weight conversion. The following Dijkstra algorithm was used to improve the single weight.

THE BASIC IDEA OF DIJKSTRA ALGORITHM

2.1

The description of Dijkstra algorithm

Dijkstra algorithm is a typical algorithm based on shortest path, which is used to calculate the shortest path from a particular vertex to the other vertices in the graph or network. Its main characteristic is that it starts from the center point and expands outward layer by layer until all vertices are covered. The thought of Dijkstra algorithm is as follows: Assume G  =  (V, E) for a weighted directed graph, and divide the vertices graph in set V into two groups. The first group is the set of vertices for which shortest path has been found (it is expressed as S). There is only one source point initially, and vertex with the shortest path will be added in the set S, until all vertices are added to the S. The second group is the set of vertices for which shortest path has not been found (it is expressed as U, U = V−S, the vertices in set U are added to set S constantly until U is empty, S = V). In the process of U entering S, it must be guaranteed that the shortest path from source point to each vertex in S is less than or equal to the shortest path from source point to each vertex in U. 2.2

3 3.1

IMPROVEMENT OF THE DIJKSTRA ALGORITHM BASED ON THE DGIS The disadvantages of Dijkstra algorithm

The traditional Dijkstra algorithm simply takes the distance as the only standard of path selection. However, in real life, we have to consider the cost to achieve optimized configuration of cost and time, namely the lowest cost of logistics under the dual constraints of cost and time. 3.2

The algorithm model

The problem of multi-objective optimal path between two points of the model is shown in Figure 1. Set a directed graph, directed line segment number is s, Directed line segment representation of road links between each node, m nodes, nodes are connected by a directed edge, for each edge of directed graph, gives the multiple target weight,

The execution steps of Dijkstra algorithm

Assume n as the number of vertices in graph G  =  (V, E). The distance[n] is a collection of the

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Figure 1.

(transportation cost, transport distance,) give a comprehensive weight for each section, and then use the comprehensive weight as the target weight, according to Dijkstra algorithm based on the shortest path, and take the shortest path as the optimal path. The second method: take a target weight of the path as the key target weight, the shortest path of N single weights can be found by the single weigh. Then calculate each target weight of the N paths. Give the weights of three sections, which are transportation distance, time and cost. Taking transportation distance as the main weight, N optimal paths will be found through the analysis of the shortest path. Then the other weights of these N optimal paths will be listed, customers can choose the path according their own will. The third method: take each target weight of the section as the main target weight in turn, and find the shortest path of the former N paths. In this paper, there are three target values in each section, so 3 N better paths can be got. Then the weights of 3  N paths are calculated, so users can choose the path which meets their own needs in the 3 N optimal paths. The first method is not only a simple algorithm, but also its shortest path is the optimal path. However, it is the comprehensive value that is assigned, because the unit of each target is different, the functional relation between the comprehensive weights and each target weight is difficult to determine. And the customer’s demands are various—different customers will have different needs in different time, so fixed coefficient is obviously not suitable. This algorithm relies more on the customer experience, and sometimes users need to adjust the relation function between the integrated weight and the target weight constantly according to the feedback information. The second method and the third method give N and 3 N optimal simple paths according to the target weight of the sections, and give the weights of each path. The algorithm is reasonable and convincing. Which is the optimal path in user’s mind is not given by the algorithm, but is selected by the user according to the calculated results. However, the second method and the third method are still different. The second method uses a target weight as the target weight to find the alternative path, and the third method treats the objective weights equally to make full use of the existing data. In the actual demand, 8–9 alternative paths have been able to meet the needs of the users. In the third method, we give three target weights, so nine paths can be given.

Multi-objective optimal path model.

they are the transportation distance, transportation time and transportation cost. The path of the selected target weight is the sum of all sections of the target weight. Out the optimal path between any two nodes in the graph in accordance with customer demand. 3.3

Improvement of the algorithm

According to the analysis of the multi-weights, we can use three methods to improve the algorithm. For the three factors of path (i, j), we record transport distance as L, the transportation time as T and integrated transportation costs as S. For the path, assume that the evaluated value of path (i, j) is P, and we can get: Pij = m1L + m2T + m3S as the scale of addition, in which m1, m2, m3 are constants and can be adjusted according to different constraints. There is a path between any two nodes i and j including n sections, so the weights of Pij can be expressed as Pij = m1 ∑ n =1 L + m k

∑ n =1 T k

m3 ∑ n =1S k

In GIS, collection of edge information can be represented by the edges of road length, average speed and vehicle fuel consumption, and the edge of the weight is made up of these information. A path distance, driving along this path of oil consumption and so on are all terms by super imposing the corresponding weights are calculated. For along this path the time required, can first use the average speed of the edges of each edge length divided by, then find the required on each side. When asked, finally put these time superposition. That is to say in the GIS, most constraint information have the additivity. The first method: simplify some weights of the section. Considering various factors of the section

3.4 The advantage of improved algorithm There is not only one factor of length in the problem of path planning, but there are still many

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Table 1.

Nine alternate sections weight of the path.

Main target value

Source point

Middle vertex

End point

Mean of transportation distance (meters)

Transportation distance

1 1 1 1 1 1 1 1 1

2-7-18-24-33-36 2-7-17-24-29-36 2-7-18-24-30-35 2-7-18-24-33-36 2-7-18-24-30-35 2-5-14-21-25-31 2-7-17-24-29-36 2-7-18-24-33-36 2-7-18-24-30-35

37 37 37 37 37 37 37 37 37

1324 1359 1367 1359 1367 1372 1359 1324 1367

Transportation time Transportation cost

got 9 alternative paths initially, there are few alternative paths eventually. In order to increase the number of final alternative paths and make user have more non-quantitative factors in the final decision, when the final alternative paths are few in number, we can increase the number of k under the condition of constant m, in order to make the number of final output conform to the needs of the users. This paper analyzes the current situation of optimal path research, based on the previous research, and it obtained the following results: 1) This paper analyzes the models of optimal path and related algorithm combined with the actual situation of our distribution center, and also puts forward the multi-weight optimal algorithm. 2) In the multi-weight optimal algorithm, there are not only fixed target weight but also random variable target weight in each section (fixed mean and variance, but not necessarily limited to normal distribution), in order to expand the optimization goal further and more accord with the reality. 3) It puts forward the method to solve the problems existing in multi-weight optimal algorithm. Especially for the algorithm in which each target weight plays the role of weight in turns, this algorithm can make full use of the data to provide multiple paths, and the algorithm is less complex compared with the algorithm based on single target when providing the path of the same number. The paper lays a good foundation for further study on integrate algorithm of the GIS.

factors that need to be considered. Some users pay attention to transportation time, while some users focus on transportation costs, and usually other factors such as roads also need to be considered. After the algorithm improvement based on multiweights, the path selection is more reasonable. To classify the weights according to the actual macroeconomic factors can meet the needs of different users. 4

REALIZATION AND RESULTS OF THE MULTI-WEIGHTED OPTIMIZATION ALGORITHM

We searched road images of a city in China via Google earth, and made a statistics on the road information according to GIS. We get 37 nodes, 117 sections, in which the starting point is 1 and the end point is 37. We processed each target weights. After processing, we can get transportation length, transport costs, and transport time mean and variance. We took the path length and the cost of the vehicle through as a fixed value, and the time of the vehicle through a section as a random normal variable. We took path length, transportation cost and transportation time as target values in turns to obtain three simple shortest paths. This paper assumes that transportation time is not relevant, so it is convenient to estimate the variance of transportation time after getting the nine paths. We take the third method as the basic algorithm, run Dijkstra algorithm mainly based on transportation length, and get nine alternate sections correspondingly. The point and the target weight of the path are shown in Table 1. The results of the third method not only gives nine paths, but also lists the transportation distance, transportation cost, and the mean and variance of transport time for each path. Due to there are repeated path in seeking 3 simple shortest path switch different target weight, though we

REFERENCES [1] Gu ling-lan, The Optimization of Dijkstra in GIS Route Analysis. Department of computer Engineering, 2006, 34(12):54–56. [2] Tang wen-wu, Shi xiao-dong. The Calculation of the Shortest Path Using Modified Dijkstra Algorithm in GIS. Journal of Image and Graphics. 2000, 5(12):1020–1023.

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[3] Shang jing. On the Shortest Path in Direction Diagram byDud Weight. Journal of Institute of Financial and Commercial Management. 2008, 10(2): 21–22. [4] Zhang mei-yu, Jiancheng-feng. Research on Dijikstra algorithm in optimal path with multiple constraints of agricultural product distribution. Journal of Zhejiang university of technology. 2012, 40(3): 322–330. [5] Fan yue-zhen, Jiang fa-chao, Design of Vehicle Optimization route Algorithm. Department of Computer Engineering. 2007, 28(23): 5758–5761. [6] Zhang xin-yi. Wu jin-pei. An Implementation of Path Planning Algorithm Applied to Vehicle Location and Navigation System. Computer Automated Measurement & Control. 2001, 9(4): 16–17.

[7] Li yuan-chen. Liu wei-qun. Analysis of the Shortest Route in Network on Dijkstra Algorithm. Microcomputer Applications. 2004, 25(3): 296–298. [8] Hao wei. Liu wan-qing. Shortest path algorithm for rescue Vehicles based on GIS. Computer Applications. 2008, 28: 104–108.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Design of battery charging and discharging circuit H.Q. Zhan & Z.P. Li Jiangxi Science and Technology, Normal University, Nanchang, Jiangxi, China

ABSTRACT: This study reports on the design of charging and discharging circuit based on operational amplifier. Battery management is divided into charging (constant current and constant voltage), discharging and protection (overvoltage, under-voltage, over-current and over-temperature). At initial charging, the battery uses constant current charging mode. When it reaches the rated voltage, it will switch to the constant voltage charging status automatically. The battery supplies electrical power. When the discharge voltage is less than the minimum voltage, it will convert to under-voltage protection state. If discharging is stopped, then it converts to the charging state. It can convert to the state of charging by artificial conversion too. The design achieves the requirements of the subject well. The circuit design is brief and clear and has a high performance price ratio. Keywords: battery; control circuit of charge and discharge; operational amplifier circuit; circuit protection 1 1.1

current closed-loop control is shown in Figure  1. According to the concept of virtual short of integrated op-amps, we can get it available:

SCHEME DEMONSTRATION AND ANALYSIS The choice of battery charging scheme

IL = Vi/R1.

Scheme one: Using a 555 timer to get a monostable triggered PWM circuit to control the duty ratio of PWM wave. Thereby it can achieve the control of main circuit of charging on–off time and change the average charging current. This scheme uses the battery voltage as the power supply voltage of 555 directly. The voltage fluctuation is large, which is easily go against 555 timers work. And the design of equalizing charge and floating charge is complex. Scheme two: This battery charging circuit design is based on the op-amps and triode. It uses the LM317 1.25 V output, and the end pressure drop to achieve the purpose of filling and floating. Compared with reference voltage, battery voltage output of the high and low level controls the triode conduction, which switches the filling and the floating. This scheme of voltage has a certain error with the theory voltage. Scheme one can better meet the system requirements, but the control circuit is complex. Although the second scheme design parameters have a certain error, the impact is very little on the system. So we chose scheme two. 1.2

where IL is the load current, R1 is the sampling resistor and Vi is the input signal of op-amps. If R1 is fixed, IL is completely determined by Vi. At this time no matter how the Vcc or RL changes, we can make IL to remain stable by using automatic adjustment function of feedback loop.

The choice of battery discharge scheme

Scheme one: We use constant current closedloop to control the circuit. The typical circuit of

Figure 1.

Constant current control.

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Scheme two: Adopting the method of constant voltage discharge, compared with the reference voltage, makes the output voltage constant. But for battery, adopting the method of constant current discharge can improve the efficiency of the battery. The greater the current of battery is, the lower the efficiency of the battery is. In view of the above-mentioned analysis, this system adopts scheme one. 2

SYSTEM THEORY ANALYSIS AND CALCULATION

Figure 2.

System block diagram.

Figure 3. circuit.

Constant current, constant voltage charging

Figure 4.

Charging mode switching circuit.

2.1 Constant current, constant voltage charging circuit parameter design As it is shown in Figure 3, during the early charging, the battery is charged with 0.25  A constant current. According to the three terminal voltage regulator tubes which have 1.25 V of voltage drop between output terminal and earthling terminal, we can work out the following equation: R2 = 1.25/0.25 = 5 Ω. When the voltage of battery reached 7 V by constant current charging, it switches to constant voltage charging. Op-amp reference voltage is 3V. When the partissal pressure value of the R8 and R11 is greater than 3V, the charging of battery is converted into a constant voltage. If we set R8  =  10k, then R11 is a value for 10k potentiometer. 2.2

The parameter design of commercial power, solar power charging switching circuit

As it is shown in Figure  4, R2  =  20k, R4  =  20k, partial pressure value of R4 is 0.5VC2 (VC2 is battery voltage), as a reverse input of the comparator. According to the characteristics of photosensitive resistance, the partial pressure of resistance R5 is VREF. As a positive input of the comparator, it switches to the solar charge during the daytime and adopts commercial power charge in dark environment. 3

THE SYSTEM CIRCUIT ANALYSIS

According to the requirements and scheme demonstration, the block diagram of this system is shown in Figure 2. System working principle: At the early charging, the battery voltage is less than 7.0 V, the signal through the logical processors output low level. That means the base of Q1 and Q2 for the low level and makes the Q1 is on conduction,

Q2 is cut off. At this point, the charging process is at the constant current charging stage. When charging voltage is higher than 7.0 V, then Q2 is on conduction, and charging process converts to constant voltage charging. Adjusting R11 can change the battery voltage during constant current charging. R3 is the resistance of negative

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temperature coefficient, which functions on temperature compensation so as to improve the accuracy of constant-voltage charging voltage. When constant voltage of charging reach to the rated voltage, the power supply to the load condition, which can also be manually switched. This system can achieve the purpose of solar charge during daytime and commercial power charge in dark environment. 3.1

Constant current, constant voltage charging circuit

The LM317 is three-terminal integrated adjustable output voltage stabilization block, with over-current protection and thermal overload protection. When battery voltage is less than 7.0 V, op-amp U1 and U2 output low level, Q1 is on conduction, Q2 is on deadline. At this time, it is at constant current charging stage. When battery voltage rises to 7.0 V, then the charging process is converted into a constant voltage charging. And adjustable R11 can change the charging voltage. R3 has the function of temperature compensation, which can improve the accuracy of constant voltage charging. And the circuit is shown in Figure 3. 3.2

Under-voltage protection circuit.

Figure 6.

Battery discharge circuit.

Charging mode switching circuit

This system design for battery charging methods are solar charging and commercial charging. They can be both automatic and manual switched. This switch circuit is shown in Figure 4. When there is light irradiation, VREF reference voltage is less than 0.5VC2 voltage, signal through logical processors output high level, then relay normally open switch is closed and the normally closed switch is open. The battery switches into solar battery charging. If in the dark environment, relays also switch to the original state which is commercial power supply. This circuit can also be manually switched. When close button S1, relay normally open switch is closed, it’s on solar power charging, When close button S2, it switches into a commercial power charging status. 3.3

Figure 5.

3.4

Battery discharge circuit

This system design of discharge circuit adopts the way of constant current circuit. This circuit is shown in Figure  6. The load current through the resistor R9 obtained by U2 amplification 4 times sampling voltage feedback to the amplifier U1, compared with 3v reference voltage. The load constant current value is 1.5 A. By changing the reference voltage value to change the constant current value.

Battery under-voltage protection circuit

Battery adopts the constant current method supply current to the load. As the battery discharges, the battery voltage is slowly reduced. When the battery voltage is less than 5 V, signal through logic processing and output high level, then relay normally open switch is closed. And the battery is no longer discharge but switches into charging status to make the battery storage voltage. Under-voltage protection circuit is shown in Figure 5.

4

CONCLUSION

We adopt the charging way by using the change of the battery voltage to control the charging

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REFERENCES

circuit directly. It can realize two charging ways: solar power charging and commercial power charging. In the initial stage of storage, battery adopts constant current charging. When it reaches rated voltage, it adopts constant voltage charging. And when full of voltage, the battery can provide the load voltage with the method of constant current. This system completed the topic request, not only can charge and discharge, but also can undertake solar charging. The design of this circuit is brief and has a high cost performance.

[1] Bai, S.T. & Ying, C.H. 2001. Analog electronic technology foundation. Beijing: Higher Education Press. [2] Qun, F.Z. & Song, Q..L & Jin. S. 2004. A battery voltage equilibrium system. Electrical automation 04(4): 94–99. [3] Feng, G.J. & Ming, H.W. 2008. Long life electric bicycle with VRLA battery research. Battery 08(7): 14–19. [4] Bin, L.L. & Peng, C. & Lin, H. 2008. Protection and restoration of lead-acid battery technology research. Battery 08(1): 25–28.

ACKNOWLEDGEMENT Thanks to Zeping Li for the corresponding author of this paper.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Automatic flight control of a certain UAV using LQG design SH. Y. Zhang Department of Aerial Instrument and Electrical Engineering, The First Aeronautical Institute of Air Force Xin Yang, China

ABSTRACT: This paper presents the design and simulation of automatic flight controllers for a UAV by using a Linear-Quadratic-Gaussian (LQG) control approach. The automatic flight controllers are constructed based on two independent LQG regulators which govern the longitudinal and lateral dynamics of the UAV respectively. The LQG design is able to retain the guaranteed closed-loop stability while having incomplete state measurement. The Kalman filter is used to provide the estimated states. The flight-test results show that it is highly feasible and effective with a relatively simple onboard system. Keywords: (LQG) Linear-Quadratic-Gaussian; UAV (Unmanned Air Vehicle); automatic flight controller 1

2

INTRODUCTION

THE UAV MODELING

The UAV is considered as a single rigid-body, for a certain UAV system, the following are defined for the longitudinal model:

In recent years, UAV technology has experienced a rapid growth in popularity. As more countries around the world begin to acknowledge and actively pursue UAV systems to augment their own Intelligence, Surveillance, Target Acquisition and Reconnaissance (ISTAR) capabilities, UAV has been one of the latest military and force-multiplying technologies pursued by many countries around the world. Fixed-wing UAVs, especially the larger ones with gross weight in excess of 100  kg, own distinct advantages over other configurations in terms of payload capacity, operational speed, altitude, range and endurance. This paper focuses on the automatic control strategies developed exclusive of conventional fixed-wing UAV systems. The linear-quadratic regulator, omitted as LQR (1) has following advantages (1) control theory is well-established mathematically and the resulting control law is simple and elegant; (2) the optimal control gains are automatically generated in the solution of the control equations; (3) highly suitable for the automatic flight control of UAVs like the stability and control of a high-altitude, longendurance UAV. However, the biggest pitfall of LQR is the requirement of the real-time full-state measurement which is often unavailable in practice (2). The Linear-Quadratic-Gaussian (LQG) controller is an extension of the LQR where the unmeasured states are estimated using an optimal observer, i.e. the Kalman filter. This gives the LQG the advantage of dealing with the uncertain linear systems disturbed by the additive white Gaussian noise while having incomplete system state information available for the control-loop feedback.

x [ ΔV , Δα , Δqq , h ]T T y [ ΔV , Δq h] u = [ Δδ e ]

(1)

where V, α, q, θ, δe are the airspeed, angle-ofattack, pitch rate, pitch angle, altitude and elevator deflection respectively. Similarly, for the lateral model, x [ Δβ Δppp, Δr, r, Δφ ]T y [ Δp, Δr, Δφ ]T u = [ Δδ a , Δδ r ]

(2)

where β, p, r, φ, δa, δr are the sideslip angle, roll rate, yaw rate, bank angle, aileron deflection and ruder deflection respectively.

3

LQG REGULATOR DESIGN(3)

LQG Regulator is a combination of the LinearQuadratic Regulator (LQR) and the Kalman filter. u(K) = −Kx(K)

(3)

where the optimal constant feedback gain, K is obtained such that the quadratic cost function J is minimized.

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Figure 1.

∞

J

LQG regulator.

(k ) ∑ x(k

T

Qx( k k)) + u( k )T Ru( k )

(4)

k =1

where K is readily attainable by solving the socalled discrete algebraic Ricatti equation (4) for P: P Q + AT PA ( AT PB )(R + BT PB ) 1( BT PA) (5) which yields (R + BT PB ) 1( BT PA)

K

The block diagram of LQR regulator is shown in Figure 1. 4

FLIGHT TEST SIMULATION

Figure  2. Output response of straight-and-level automatic flight.

4.1 Straight-and-level (equilibrium) flight Keep throttle level unchanged throughout all automatic flight modes, Straight-and-level automatic flight are carried out in a gradual, successive schedule. It is evident that the closed-loop control systems for both longitudinal and lateral dynamics are stable during the entire automatic flight lasted for 27.5 seconds. The variation of the altitude is bounded between 276  m and 282  m which means the system error is generally less than 3 m. Moreover, despite having only one control input (elevator deflection) in the longitudinal controller, the airspeed is able to be maintained within an error of 4 km/h from the trim airspeed, 104  km/h. No distinct oscillation was observed on both airspeed and altitude responses as well. On the other hand, after a short transient process, the bank angle response enters steady state with bounded error generally confined within +5 deg. This indicates that the controller is able to maintain the wing at level position under external disturbance (wind gust and aerodynamic load). In addition, the equilibrium condition of the aircraft in flight is being sustained using little

Figure 3.

Simulation of bank angle tracking test.

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separately designed. Flight test simulation have shown that the controllers perform very well even though simplified linear models are used in the synthesis of the controllers. In addition, the controllers exhibit exceptional stability and tracking performance with good disturbance rejection capability. No evidence of significant cross-coupling dynamics between longitudinal and lateral motions was found. Also, the resulting control laws are simple and efficient enough to be easily realized using limited onboard computing power.

control effort. The figure shows that the elevator and aileron typically operate in the order of 4 deg and 2 deg deflections respectively while the rudder is practically unused. This is one of the strength of linear-quadratic control approach where the compromise between control effectiveness and control effort is accomplished with the proper selection of Q and R weighting matrices. 4.2

Bank angle tracking

The desired square wave has an amplitude and period of 20 deg and 10 sec respectively. As shown in Figure 3, the bank angle response exhibits excellent transient and steady-state properties. Typically, the overshoot percentage, rise time and settling time lie in the order of 20%, 1.5 s and 3 s respectively. The damping of system is more than satisfactory where no noticeable oscillation occurs before the tracking error converges to zero. 5

REFERENCES [1] Hsiao, F.B. Engine speed and velocity controller development for small unmanned aerial vehicle, J Aircr, 2008, 45(2), pp 55–65. [2] Lee, C.S., Hsiao, F.B., Jan, S.S. Design and implementation of linear-quadratic-Gaussian stability augmentation autopilot for unmanned air vehicle, Aeronaut J, May 2009, 113(1143), pp 275–290. [3] Li Li, Zhang Hua-min. Neural Network Based Feedback Linearization Control of an UAV, AEIT2011 Conference, pp 45–48. [4] Ogata, K. Discrete-time Control Systems, 1987, Pretice-Hall. [5] Erdos, D., Watkins, S.E. UAV autopilot integration and testing, 2008, IEEE Region 5 Conference, pp 1–6.

CONCLUSIONS

This paper presented a design and simulation of automatic flight controllers based on the LQG theory on a fixed-wing UAV. The automatic flight is achieved by utilizing independent linear longitudinal and lateral controllers which are

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on online and remote calibration technology of electronic belt scale based on weights superposition method X.P. Shang, H.Y. Chen & Z.W. Huang Zhejiang Province Institute of Metrology, Hangzhou, China

ABSTRACT: On the basis of research in existing measuring instruments and measurement method of coal, we explored online and remote calibration technology of the main measuring instruments— electronic belt scale. This system is based on incremental superposition method. It uses weights which simulate actual material to superimpose on the auxiliary belt scale’s weighing sensors, as theoretical superposition. By comparing the cumulative difference between the main and auxiliary belt to the theoretical superposition, we can obtain calibration results. The entire calibration process can be completed online and all real-time data can be transmitted to the high compatibility and high reliability intelligent measurement network platform remotely. The system provides the technical means of online and distance calibration of belt scale for measuring departments and enterprises and it saves a lot of manpower and resources and improves the efficiency of calibration and ensures that the belt scale works accurately. Keywords: 1

electronic belt scale; remote calibration; weights superposition

INTRODUCTION

regulation’s requirements, in order to determine the specifications of belt scale in conformity with the metrological requirements or not, calibration of belt scale is needed. Calibration methods of belt scales are divided into physical calibration methods and simulation calibration methods. Physical calibration methods include hopper scale calibration method and material superimposed calibration method. Simulation calibration methods include cyclic chain method, roller chain calibration method, weights superposition calibration method and so on. This article presents a remote calibration system of electronic belt scale based on weights superposition calibration method. This system can reduce costs of calibration of belt scales in use for enterprises and reduce economic losses from trade settlement for companies which have coal as main energy. For energy management department and statutory metrology department, this remote calibration system can realize the real-time monitoring and forecasting of energy data.

In our country, coal is the main energy. According to statistics, China’s primary energy consumption has been more than 4 billion tons of coal equivalents, of which 3.5 billion tons are coal that accounts for 74.7% of the energy consumption. Modes of coal transportation include railways, shipping and automobile transportation, and shipping becomes the main way for its lowest costs relatively[1]. Coal which is transported by ship is measured mainly by water gauges and belt scales. Water gauge measurement is affected more by waves, temperature and the observer’s capability, and its repeatability and reliability is bad. Furthermore, water gauge measurement is unable to trace. Belt scales are stable, reliable and traceable in measurement and its maximum permissible error can be controlled in the 0.5% range. Belt scales are also widely used in enterprises which use coal as the main energy source. In addition, the electronic belt scale measurement can realize on-line measurement of coal and improve work efficiency and it has achieved more and more attention[2]. As a kind of measuring equipment which weighs continuously and automatically when belt conveyer is conveying solid bulk materials, electronic belt scale’s accuracy is not only associated with the product itself, installation position and installation quality, but also closely linked to periodical calibration[3]. According to the national verification

2

OVERVIEW OF THE DEVICE

The calibration method based on weights superposition of belt scales belongs to a kind of simulation calibration methods. The device includes belt conveyors, the major belt scale, the auxiliary belt scale, weights superposition structure, IPC and so on. Figure 1 is the structure of the belt scale.

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information technology. Electronic belt scale based on incremental superposition method has the advantages of high efficiency for calibration and the calibration process being automatic and online which are very important for realizing belt scale’s remote control for calibration. The calibration system based on weights superposition of belt scales makes use of the Internet and other technical means to develop a Coal Measurement Network Platform which can realize online monitoring and remote calibration. Remote calibration methods of the system are realized by the way that the web server communicates with a local webcam and the calibration device’s IPC in the same time. The local IPC has installed the calibration software which is designed and developed by us. This software can send commands directly to the PLC to make parameter setting, to drive motor, to collect and transmit data. Meanwhile, from the database founded by the Coal Measurement Network Platform, we can inquiry the history of calibration results of the local computer. Figure 2 is the data flow chart of the remote calibration system. The Coal Measurement Network Platform is a part of “Energy Smart Metering Data Service Platform” and its main functions are measurement apparatus management, remote calibration control, monitoring online and statistical diagnosis and so on. First, we make surveillance cameras and IPCs which control PLCs to access the Coal Measurement Network Platform. The Coal Measurement

Figure 1. The chart of the belt scale based on weights superposition.

The basic principle of the calibration method based on weights superposition of belt scales is as follows: we use weights which simulate actual material to superimpose on the auxiliary belt scale’s weighing sensors as theoretical superposition when coal or other solid bulk materials are being conveyed. By comparing the cumulative difference between the main and the auxiliary belt scales to the theoretical superposition, we can get the calibration results. Before you start weights superposition calibration, you must first ensure that the main belt scale’s and the auxiliary belt scale’s error conditions and accuracy are consistent, namely scale– scale comparison experiments are needed. The principle of the scale–scale comparison experiment is as follows: in the main and the auxiliary belt scales weighing the same amount of material conditions, we amend measurement coefficient of the auxiliary belt scale while the main scale is the standard to insure that the main and the auxiliary belt scales get the same material cumulative values. When a calibration is started, we need start weights superposition structure to put standard weights on the auxiliary belt scale’s weighing sensors. After a period of time, we calculate cumulant difference between the main and the auxiliary belt scale within the set time and then compare the difference to the theoretical superposition to obtain calibration results. At the end of the calibration, we start the weights superposition structure again to lift the standard weights. 3

THE REMOTE CALIBRATION SYSTEM

Remote calibration technology is the inevitable result for combining of the information technology and traditional measurement and testing technology. Remote calibration is for distance calibration in the use of information networks and communication networks which is a multi-disciplinary comprehensive cross involved in instrumentation, metrology, computer hardware and software technology, and

Figure  2. The data flow chart of the remote calibration system of belt scales based on weights superposition methods.

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have developed Windows service and web service separately. Windows service gets commands stored in the database periodically, and then sends commands through the web service to related metering monitoring equipment. During the calibration process, we need to control the hopper’s opening or closing, and the belt conveyor’s starting or stopping, and weights superposition structure’s lifting and falling, as shown in Figure 3. The Coal Measurement Network Platform sends control command to the web server by the Windows service. After the web service in the web server receives the commands, it forwards the commands to the IPCs, the cameras and other equipments depending on the control objects. PLCs receive commands stored in the IPCs’ web service, and then control all components’ motors to move. By adjusting the monitors’ angle, operators can monitor metering equipments’ operation status remotely and in real time. Meanwhile, cumulative flow of belt scales and speed of conveyors and other online metering data will send to the Coal Measurement Network Platform remotely. 4

CONCLUSION

This paper has successfully developed remote calibration system of electronic belt scales based on the weights superposition method. It provides technology methods of belt scales’ remote and online calibration for measuring departments and enterprises, saves a lot of manpower and material resources, improves the calibration efficiency and ensures the accuracy and reliability of belt scales’ online measurement. ACKNOWLEDGMENTS This work was financially supported by the major science and technology project of Zhejiang province (2012C01026-1).

Figure 3. The control process of belt scales’ calibration based on weights superposition methods.

Network Platform realize belt scales’ on-line monitoring and remote calibration by controlling cameras, video recorder hard drives, and IPCs. Remote surveillance uses dot-IR cameras which has scanning frequency 50  Hz. In the Coal Measurement Network Platform, the web system can send control commands to PLCs and store these commands in web database. Basic calibration information of belt scales, including models, manufacturers, precision and flow parameters, is stored in database. Web servers and PC machines (or IPCs)

REFERENCES [1] Simpson J. 2000. Canadian Weights and Measures and 0.1% Certified Belt Scales. Canadian Weights and Measures (Technical Paper). [2] Wang Z.J. 2009. The calculation method of hanging code calibration of electronic belt scale. Journal of weighing apparatus 38(7): 36–37. [3] Chen Y.F. 2010. The development of dynamic chain code loop checking device of electronic belt scale. In Shanghai, east China university of science and technology.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The application of wavelet filter in static synchronous compensator Li Zhang School of Electronics and Information Engineering, Anhui University, Hefei, China

Ning Liu Heifei Normal University, Hefei, China

ABSTRACT: Reactive power balance of power system is important. The static synchronous compensator (STATCOM) based on inverter technology is a new dynamic reactive power compensation, and has many advantages compared with Static VAR Compensator (SVC), so STATCOM has vast market prospect. In this article, wavelet filter is applied in STATCOM to filter the harmonics of load current. The active portion and reactive portion of the fundamental current are calculated by dq transformer, and the reference signal of output current is calculated by dq reverse transformer, using the reactive portion and the output of PI controller which controls the DC voltage, and PWM tracking control technology is employed. The simulation results indicate that the application effect is good. Keywords: 1

wavelet filter; STATCOM; reactive power compensation; simulation

INTRODUCTION

in 1998  made by Siemens, Germany. In China, the STATCOM device of ±20 Mvar was run in Chaoyan transformer substation of Henan power company in 1999, and in 2005 the STATCOM device of ±50 Mvar was installed in Huangdu Xijiao transformer substation, which was used to improve the voltage stability of 220 kV bus and inhibit the bus voltage surge.[2–3] In this article, the wavelet filtering is applied in STATCOM, which filters the harmonics in voltage and current and get the fundamental voltage and current. Simultaneously, the active portion and reactive portion of the fundamental current are calculated by dq transformer, and the tracking control technology is employed to control the output current of STATCOM, using reactive portion of the fundamental current as reference signal. The output current tracks the reference signal and the source current does not contain fundamental reactive current.

Reactive power in power grid is the basic reason of the deviation of power system voltage from the nominal value. When the voltage deviation gets big, the electrical equipment performance would be deteriorated, which may lead to not only the low operating efficiency of equipment, but also the damage caused by the overvoltage or overcurrent. Big voltage deviation is the thread to the power system’s stability and influences the system’s economical operation. The necessary and sufficient way to ensure every node voltage of power system in normal levels is that the power system has plenty of reactive power sources and essential voltage regulation method is used. The SVC and STATCOM have dynamic reactive compensation properties in many kinds of reactive power sources. Compared with SVC, the STATCOM has many superior performances, such as fast governing speed, wide running range, and can greatly reduce the harmonics in the compensation current when the PWM control technique and so on are used. Besides, both the bulk and the weight of the device are decreased because the reactor and capacitor used in STATCOM are far smaller than these in SVC. Japan and the USA, respectively, developed a set of 80 Mvar and 100 Mvar STATCOM device adopted GTO thyristor in 1991 and 1994. Both the devices were successfully put into commercial operation.[1] The STATCOM device, with a unit capacity of 8 Mvar, was also put into operation

2

THE STATCOM STRUCTURE AND WORK PRINCIPLE

The schematic diagram of STATCOM adopted voltage source inverting circuit is shown in Figure 1. The voltage source inverting circuit gets into the power grid through the transformer, which can match the source voltage with the grid voltage. If there is no need to match the two voltages, the inverting circuit can also get into the grid through the inductance. In order to

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Figure 1. The STATCOM with voltage converter circuit.

achieve dynamic reactive power compensation, the output voltage phase and amplitude of the AC-side of convert circuit can be controlled (or directly control the current of AC-side) to make the fundamental reactive current ((iiac), which is in the converter circuit, equal with the load fundamental reactive current (i (ial), and there is no fundamental reactive current in the source current (i (ias ). More thyristors used in series can improve the STATCOM capacity and the voltage grade, which can also be achieved through the multiple technology and multi-level technology. 3

Figure 2. Table 1.

THE THEORY OF WAVELET FILTERING

The formula of f(t)∈L2(R) is launched following the space combination shown in equation (1),[4–5]

The wavelet decomposition. The Frequency band and harmonics.

Wavelet

Frequency band (Hz)

Harmonics

cd1 cd2 cd3 cd4 cd5

1600–3200 800–1600 400–800 200–400 100–200

32–64 16–32 8–16 4–8 2–4

J

∑ Wj

L2 (R ) =

VJ ,

(1)

tains base wave after the five layers of wavelet decomposition.

j =−∞

where J is an arbitrary scale. So, the f(t) can be written as J

f t) =

∞

∑ ∑

j =−∞ k =−∞

dj k

j k (tt))

+

∞

∑

c j kφ j k (t ).

4

CONTROL OF STATCOM

Block diagram of fundamental reactive load current’s detection and power grid reference current’s generation is shown in Figure 3, where sine and cosine signal (sinωt, cosωt) are obtained, which have the same frequency and power with the power grid by pll. ial , ibl and icl are the load current, and the fundamental current (iiaf , ibf , icf ) can be get after wavelet filtering described in step 2. The three-phase fundamental current is assumed as follows:

(2)

k = −∞

The signal f(t) is decomposed by five-layer orthogonal wavelet (Fig. 2). If the signal fundamental frequency is 50  Hz, according to Shannon’s sampling theorem, the fundamental signal, sampled 128 points per cycle, can be analyzed to 64 harmonics (3200 Hz). If the maximum frequency of f(t) is 3200  Hz, according to Figure  2, the band division on the wavelet decomposition is shown in Table  1,[1] where each decomposition of the signal or low frequency (ca) of the band is down into some low-frequency band (ca) and high frequency band portion (cd), and the decomposition of the band is in accordance with the highest frequency that can be analyzed. The 128-point sampling is according to the band decomposition at the maximum frequency of 3200 Hz. In Table 1, the voltage signal and current signal are for five-layer wavelet decomposition. If the voltage and current without DC and second harmonic, their low frequency (ca5) only con-

iaf

I m sin(ωt − ϕ )),

(3)

ibf

I m sin(ωt − 2π / − ϕ ),

(4)

icf

I m sin(ωt + 2π / − ϕ ).

(5)

The dq transformer of three-phase current is shown as follows,[1,6] so that the load fundamental current active component and reactive component can be obtained. In order to maintain the DC bus voltage (UC) stable, closed-loop control of capacitor voltage is used, whose controller is the PI regulator. I′P is assumed as the output of PI regulator,

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5

The power supply system with STATCOM is shown in Figure  5, where the three-phase power is symmetrical. The STATCOM and its control circuit are at the right part of the figure. At the left part, there are access point detection of voltage and current, the three-phase power and its load which is the three-phase full-controlled bridge rectifier circuit. The three-phase power supply is joined by the starconnecting and the equivalent inductance of the power is LS, while the equivalent resistance is not drawn. The current whose characteristic harmonic is 6k ± 1 (k = 1, 2, 3, …), causing the supply point voltage distortion, is formed by the three-phase fullcontrolled bridge rectifier circuit. The STATCOM is the three-phase bridge voltage inverter circuit. It is connected to the access point through the inductor LC, whose effect is filtering. The three-phase voltage inverter circuit is controlled using the hysteresis current tracking control technology. The simulation system is to maintain DC voltage (voltage across the capacitor) of STATCOM stable and compensate the load fundamental reactive current. The grid voltage and load current contain harmonics, which can be filtered by wavelet transform. The signal is decomposed by five-layer orthogonal wavelet, and the low frequency part (ca5) is reconstructed. According to the frequency division of wavelet decomposition in Table 1, the lowest harmonic frequency in the signal is 5, and the reconstruction signal of the low frequency part is just the fundamental signal. So, the lowest harmonic of the voltage and current in the simulation system is 5 and both the voltage and current are decomposed by five-layer wavelet, and the fundamental voltage and current can be obtained by the low frequency part’s reconstruction. The simulation results are shown in Figures 6–9. In the four figures, the control angle of the full-controlled rectifier bridge has been changed from 5° to 60° at 0.4 s. Figure 6 shows the three-phase voltage (uap, ubp, ucp) of the public power supply point (PCC) and its wavelet filtered voltage (ual, ubl, ucl). Figure 7 shows

Figure  3. Block diagram of the power grid reference current’s generation.

Figure 4.

Tracking control.

⎡ 2 ⎞ 2 ⎞⎤ ⎛ ⎛ sin ωt sin ωt i ωt + π ⎥ π ⎡ IP ⎤ 2 ⎢ ⎝ ⎠ ⎝ 3 3 ⎠⎥ ⎢ ⎥= ⎢ 2 ⎞⎥ ⎛ ⎢⎣ IQ ⎥⎦ 3 ⎢cos ωt cos ⎛ ωt 2 π ⎞ ωt + π ⎥ ⎢ ⎝ ⎝ 3 ⎠ 3 ⎠⎦ ⎣ ⎡ I m sin(ωt ϕ ) ⎤ ⎢ ⎥ ⎢ I m sin ⎛ ωt − 2 π − ϕ ⎞ ⎥ ⎡ I m cos ϕ ⎤ ⎝ ⎠⎥ = ⎢ ×⎢ 3 s n ϕ ⎥⎦ ⎢ ⎥ ⎣ I m si ⎢ I sin ⎛ ωt + 2 π − ϕ ⎞ ⎥ ⎠ ⎥⎦ ⎢⎣ m ⎝ 3 (6) The reference signals (iiaref , ibref and icref ) of current tracking controlling can be obtained through the dq inverse transformation. The value of d in dq inverse transformation is the PI regulator output I′P, while the value of q is the reactive component (IQ) of load current. Both of them are as follows. sin ωt ⎡ ⎡iaref ⎤ ⎢ ⎛ ⎢ ⎥ ⎢sin ωt 2 π ⎞ ⎢ibref ⎥ = ⎢ ⎝ 3 ⎠ ⎢ ⎥ ⎢ ⎢⎣ icref ⎥⎦ ⎢sin ⎛ ωt + 2 π ⎞ ⎢⎣ ⎝ 3 ⎠

cos ωt ⎤ ⎥ 2 ⎞ ⎥ ⎡I ′ ⎤ ⎛ ωt − π p ⎝ 3 ⎠⎥⎢ ⎥ ⎥ ⎢⎣ IQ ⎥⎦ 2 ⎞ ⎛ cos ωt + π ⎥ ⎝ 3 ⎠ ⎥⎦

THE SIMULATION

(7)

The output current can be controlled in inverter circuit with PWM Tracking Control Technology. Its working principle is shown in Figure  4, where the reference signal i ref e (iiaref , ibref and icref ) can make the output compensation current of inverter circuit have the same value, phase and frequency with the load fundamental reactive current, and there is no fundamental reactive current in power current.

Figure 5.

The power supply system with STATCOM.

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the three-phase load current (ial, ibl, icl) and its wavelet filtered current (iaf, ibf, icf). These two figures indicate that the filter works well. Figure 8 shows the threephase command current of STATCOM (iaref, ibref, icref) and its actual output three-phase current (iac, ibc, icc). Figure  9  shows the waveforms of three-phase supply voltage (uas, ubs, ucs) and current (ias, ibs, ics). Before 0.4 s, the fundamental reactive current is very small, so is the current generated by STATCOM. The supply current almost has no change compared with the load current. After 0.4 s, both the fundamental reactive current and the current generated by STATCOM are big, and the supply current has great changes compared with the load current. 6

Figure 8. current.

The command current and its actual output

Figure 9.

The voltage and current of the source.

CONCLUSIONS

With the development of microelectronics technology, The DFACTS device, based on full-controlled devices and inverter technology, has been continuously improving its reliability and reducing its ongoing cost. So devices, such as STATCOM, APF, have broad application prospects. The wavelet filtering was applied in STATCOM while its

simulation was done. The simulation results show that the application works well. ACKNOWLEDGEMENT This work is supported by the training project of young key teachers of Anhui University (02303301). REFERENCES

Figure 6. The voltage of the public power supply point (PCC) before and after the filtering.

Figure 7. filtering.

[1] Wang Zhao-an, Yang Jun, etc. Harmonic suppression and Reactive power compensation [M]. Beijing: China Machine PRESS, 1985. [2] Liu Wen-hua, Song Qiang, Zhang Dong-jiang, etc. Equivalent Tests of Links of 50 MVA STATCOM [J]. Proceedings of the CSEE, 2006, 26(12): 73–77. [3] Zheng Dong-run, Qiao Wei-dong, Liu W en-hua, etc. Field tests of 500 Mvar STATCOM [J]. East ChinaElectric Power, 2007, 35(1): 47–50. [4] Weon-Ki Yoon, etc. Reactive Power Measurement Using the Wavelet Transform [J]. IEEE Transactions on Instrumentation and Measurement (S0018–9456), 2000, 49(2): 246–252. [5] Zheng Chang_bao, etc. Reactive Power Measurement by Wavelet Transform and Hilbert Transform [J]., Journal of System Simulation 2005, 17(4): 822–824. [6] Jiang Qi-rong, Xie Xiao-rong, Chen Jiang-ye, etc. Power system parallel compensation [M]. Beijing: China Machine Press, 2004.

The current of the load before and after the

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Fractional-order differential application and research in pavement cracks image enhancement W. Jiang & Y.W. Liu School of Science, Chongqing Jiao Tong University, Chongqing, China

H. Zhang Institute of Civil Engineering and Construction, Chongqing Jiao Tong University, Chongqing, China

ABSTRACT: In order to sharpen the image edge features while enhancing texture detail, a new image enhancement method of pavement cracks is proposed by combining fractional-order differential theory with Prewitt operator effectively. The new method, compared with the existing image enhancement methods, retains the advantages of fractional-order differential effectively, which improve signal of high frequency components, and enhance the signal of the intermediate frequency components, and nonlinear keep signal very low frequency characteristics. Simulation results demonstrate that the new method can be obtained the effect of continuous variation, which not only enhanced image texture well, but also improved the edge enhancement effectively. Objectively, using image edge evaluation parameters of texture features such as information entropy and average gradient for quantitative analysis and experimental verification shows that the new method achieves the purpose of image enhancement. Keywords: 1

image enhancement; fractional-order partial differential; Prewitt operator; pavement cracks a hot topic, and some research has shown the fractional-order partial differential’s advantages in image enhancement. Pu Y.F.[5] introduced fractional-order differential into digital image processing and brought forward fractional-order differential masking and its algorithm. Simulation results showed that for image signal with rich texture information, fractional-order differential is superior to integer-order differential in enhancing texture details information. By analyzing the signal amplitude and frequency characteristics, Yang Z.Z.[6] found the fractional-order differential operation can improve image edge and texture details, and extract the edge information can avoid to produce larger noise at the same time, so as to improve the SNR of image processing. Fractional-order variation image denoising model is put forward[7]. The experimental results show that the fractional-order variation models are very effective in improving the peak signal-to-noise ratio and maintaining image texture details. However, to the best of our knowledge, there are few pavement crack image enhancement researches based on fractional-order differential theory, and almost fewer the study of pavement crack image enhancement, which combines the fractional-order differential with the Prewitt operator. Motivated by the above discussions, we

INTRODUCTION

The digital image processing technology, in the application of pavement crack detection, improves the automatization level of pavement crack recognition system based on the camera. In order to analyze the details of pavement crack effectively, there need to identify the enhanced image preprocessing. The existing crack detection algorithm is mainly based on three-dimensional terrain model of pavement crack automatic detection algorithm[1], artificial population algorithm[2], neural network algorithm[3], etc. All above mentioned are improved in a certain extent of regular crack detection algorithm, but for some slight and reticular cracks detection, these methods still cannot achieve the desired effect, and the computation is too large[4]. In digital image processing technology, it is not ideal when dealing with the edge texture details by using one-dimensional wavelet transform tensor product extension of two dimensional. Contourlet transform is due to the presence of the sampling process, which leads to the lack of translation invariance, the result of pavement image enhancement generates pseudo Gibbs distortion and the blurred crack edge. In recent years, the theory of fractional-order partial differential image processing has become

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have combined fractional-order differential theory with Prewitt operator and applied them to pavement crack image enhancement; a new method of pavement crack image enhancement based on fractional-order partial differential is proposed. On the one hand, the fractional-order differential is good at retaining image texture details. Prewitt operator, on the other hand, is better to keep the edge character. The method inherits the advantages of both fractional-order differential and Prewitt operator, and it can enhance image details at the same time, also has a certain resistance to noise. Simulation results show that the new method can make obvious edge features, and smooth area information to retention. It can retain more texture details, thus improve the image visual clarity. So, it is more effective and practical on pavement crack image enhancement.

2

3

Assuming the gray function of image is F ( x, y ), the 3 3 pixel-domain is extracted, as follows. ( x , y + 1) ⎤ ⎡ F ( x , y 1) F ( x 1, y ) F (x ⎢ F ( x, y 1) F ( x , y) y ) F ( x, y + 1) ⎥⎥ ⎢ ⎢⎣ F ( x , y − 1) ( x + 11,, y ) F ( x 1, y 1) ⎥⎦ In order to simplify calculation, the differential is commonly used, instead of difference approximation, in the image processing. The operator template of horizontal gradient and vertical gradient of the Prewitt operator, as for the 3 × 3 area of discrete gray functions.

LH

DIFFERENTIAL EXPRESSION OF THE FRACTIONAL-ORDER PARTIAL DIFFERENTIAL

d f (t ) ( v )( v ≈ f tt)) + ( v f (t (t ) + 2 dtv Γ( v ) + + f t − n) n ! ( −v + n + 1)

)

⎡1 1 1⎤ ⎡1 0 −1⎤ ⎢ 0 0 0 ⎥ , L = ⎢1 0 −1⎥ ⎢ ⎥ V ⎢ ⎥ ⎢⎣ −1 −1 −1⎥⎦ ⎢⎣1 0 −1⎥⎦

∇fx = f ( x − , y − 1) + f x − 1,, y + f ( x − , y + 1) − f x + ,y −

The difference approximate expression of f tt) is exported based on the definition of fractionalorder differential[8]: v

NEW IMAGE ENHANCEMENT MODEL OF PAVEMENT CRACKS

f t

− f x− , y +

− f ( x, y + ) − f x + 1, y + 1),

(5)

where ∇fx and ∇fy are horizontal gradient and vertical gradient, respectively. The step is Δx = 1, as the managed picture is digital image and the grayscale change of image is also limited, and the shortest distance of change is between two adjacent pixels.

) (1)

∂ff x, y ) ⎧ ⎪⎪ f x, y f ( x 1, y ) = ∂x ⎨ ⎪ f x , y f ( x , y ) = ∂f x + , y ) ⎪⎩ ∂x

(6)

So the differentials form of ∇fx is as follows: + , y − 1) ∂f ∂f , y − ) ∂f + , y ) − − ∂x ∂x ∂x ∂f x, y ) ∂f ∂f + 1, y 1) ∂f ( , y + 1) − − − ∂x ∂x ∂x (7)

∇f x = −

(2)

∂f

In the same way the differential form of ∇fy is as follows:

∂v f x , y ) ≈ f x, y ) + ( −v ) f x, y − ) ∂yv ( −v )( −v + ) + f x, y − 2 ) 2 Γ( −v + 1) + + f ( x, y − n ) n!! ( −v + n + 1)

(4)

∇f y = f ( x − , y − 1) + f x, y − + f ( x − , y − 1)

The duration of the two-dimensional digital image on the axis is measured by pixel. In view of the digital image, the difference expression of the fractional partial differentials’ definition of the two-dimensional digital image f x, y ) is as follows: ∂v f x , y ) ≈ f x, y ) + ( −v ) f x − 1, y ) ∂x v ( −v )( −v + ) + f x − , y) 2 Γ ( −v + 1) + + f ( x − n, y ) n!! ( −v + n + 1)

− f ( x + 1, y ) − f x + 1, y + 1)

− , y + 1) ∂f ∂f − 1,, yy)) ∂f + , y + 1) − − ∂y ∂y ∂y ∂f + , y ) ∂f x, y + 1) ∂ff x, y ) − − − ∂y ∂y ∂y (8)

∇f y = −

(3)

∂f

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( + ) ( − )(− )( + ) ( − )(− )( + ) ⎤ ⎡ ( − ))(− − − ⎢− ⎥ 2 2 2 ⎢ ⎥ )( + ) ( − ))(( −v + 1) ( −v)( )( −v + 1) ⎥ ⎢ − ( − )(− +v − +v − +v ⎢ ⎥ 2 2 2 ⎢ ⎥ v −11 v −1 v −1 ⎢ ⎥ ⎢⎣ ⎥⎦ −1 −1 −1

The new model is as follows, though replacing the integer partial differential of the equations (7) and (8) with fractional partial differential. + , y − 1) ∂f ∂f v , y − ) ∂f v + , y ) − − v ∂x ∂x v ∂x v ∂f v x, y ) ∂f v + , y + 1) ∂f ∂f v , y + ) − − − v v ∂x ∂x ∂x v

∇fxv = −

∂f v

⎡ ( − )(− )( + ) ⎢− 2 ⎢ )( + ) ⎢ ( − )(− ⎢− 2 ⎢ )( + 1) ⎢ − ( − )(− ⎢ 2 ⎣

(9) ∂f

∇fyv =− −

v

∂f v

− , y + 1) ∂f ∂f − 1,, yy)) ∂f − − v ∂y ∂yv v

+ , y + 1) ∂yv

v

+ 1, y ) ∂f v ( , y + 1) ∂f v ( x, y ) − − ∂yv ∂yv ∂yv

where ∇f ∇fxv and ∇f ∇f yv are the improved horizontal gradient and vertical gradient by fractional-order differential. To achieve a better image enhancement processing, choosing the first three of fractional partial differential difference expression (2) and (3) to form a 3 × 3 differential mask. In addition, the model of fractional-order horizontal gradient and vertical gradient the of Prewitt operator, which combining type (9) and (10), is as follows:

⎡ ⎢ 0 ⎢ ⎢v v 2 ⎢ ⎢ 2 ⎢v v 2 ⎢ ⎢ 2 ⎢v − v 2 ⎢ ⎢ 2 ⎢ ⎢ 0 ⎢⎣

∇fxv = v − )[ )[ f x, y + f ((x x, y − 1) + f x, y + )] − f x + , y − − f ( x + 1, y ) − f ( x , y + 1) )

4

∇f yv = v − )[ )[ f x, y + f (x− ( x , y ) + f x + 1, y )] ⎡ ( )( + ⎢− 2 ⎣ + f ( x, y )

− f ( x − 1, y + ) − f ( x ⎤ + v ⎥ [ f ( x − 1,, y ⎦ f (x ( x + 1, y − 1)] )

v −1 v −1

v v

2

v v

2

2

v v

2 7

2

0 ⎥

2 6

⎤

2

⎥

2 v v ⎥

⎥ ⎥ 2 2 2 7v v − 6 7 6 v −v ⎥ 4v 4 ⎥ 2 2 2 ⎥ 2 2 7 6 v −v ⎥ 2 2 ⎥ 3 2 3 2 2 2 ⎥ 2 2 2 ⎥ v −v v v v v 0 ⎥ 2 2 2 ⎥⎦ 3

2

2

2

3

2

2

2

EXPERIMENT AND RESULT ANALYSIS

This new model uses the Matlab7.1 for computer simulation experiment, and comparing with the existing part of the image enhancement method. The first set of experiments is to enhance image crack image 1, the experiment result is shown in Figure  1, where, (a) is the original crack image, (b)–(f) are enhance image results, which the new model take difference value of v . By the experiment result, if 0 0.7 , the experimental effect gets better with the increase of v ’s order gradually; if v > 0.7 , the experimental effect get worse with the increase of v ’s order gradually. The whole experiment gray became smaller with the increase of v ’s value; the enhanced image grey value is the most closely to the original image, so the effect is relatively good. when v = 0.65 . To illustrate, the new model of this paper has the advantages of the method of image enhancement. The second set of experiment uses an image crack

(11)

− f x, y +

⎤ − 1⎥ ⎥ ⎥ − 1⎥ ⎥ − 1⎥⎥ ⎦

v −1

Take example by the literature[5], rotating the mask on the up and down or so four directions centered on the point (x,y), then overlay, getting the new model gradient mask is as follows:

(10)

) ⎤ ⎡ ( )( + ⎢− + v⎥ [ f ( x − 1,, y 2 ⎣ ⎦ + f ( x , y) y ) f (x ( x − 1, y + 1)] ( −vv )( v + 1) − [ f x − 2, y − 1) 2 + f x − 2, y f ( x − 2, y) + 1]

( − ))(− ( + ) +v 2 ( − )(− )( + ) − +v 2 ( − )(− )( + 1) − +v 2 −

, y + 1) )

( −vv )( v + 1) [ f x − 1, y − 2 ) 2 + f x, y f ( x + 1, y − 2) ] −

(12) The horizontal direction and vertical direction of gradient backward difference mask of the new improved model in is as follows, respectively:

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Table 1.

Performance analysis.

Relative parameters

Information entropy

Average gradient

Original image Prewitt operator Fractional differential New method

7.0096 5.6614 7.8094 8.0853

7.1606 8.4880 12.3046 16.4117

image by the method in this paper achieved better effect in noise resistance and resolution than other methods. Thus, relative to the other methods, the new method in this article is an effective method of image enhancement.

Figure 1. (a) Crack image 1, (b) ν = 0.6, (c) ν = 0.65, (d) ν = 0.7, (e) ν = 0.75, (f) ν = 0.9.

5

CONCLUSIONS

Currently, seldom people combine fractionalorder differential theory with Prewitt operator to do image enhancement. A new pavement cracks image enhancement method based on fractional-order partial differential is proposed. The new method effectively inherits advantages, which keeps the characteristics of image edge and retains texture details of smoothing region. Simulation results show that the new method, compared with current image denoising methods, can not only suppress noise better, but also keep the characteristics of image edge better. Especially, it is better than current integer-order partial differential methods. It is an effective image denoising method. The result is approximatively expressed by fractional-order differential, so the future research is to determine a better differential order to obtain better results.

Figure 2. (a) Crack image 2, (b) new method, (c) fractional-order differential, (d) Laplacian operator, (e) Sobel operator, (f) Prewitt operator.

image 2 with rich texture details, the result is shown in Figure 2. It is not hard to see, the fractional-order differential treatment on texture details enhancement has certain effect. The exposure of image is too high by enhancement of the fractional-order differential and the visual effect is not very ideal. The visual effect of the enhanced image by Laplacian operator is not ideal as the measure in this article. The visual effect of the enhanced image by the Sobel operator and Prewitt operator appeared the phenomenon of distortion. After processing by the new model in this paper, the image not only has been enhanced in texture details, and the effect of the edge area enhancement is better compared to fractional differential. The enhanced image is improved in terms of signal-to-noise ratio and the average gradient compared with other strengthening methods through objective evaluation. Obviously, the enhanced

ACKNOWLEDGEMENTS The work is supported by the Research Programs for National Natural Science Foundation of China (11071266), the Natural Science Foundation of Chongqing (CSTC, 2012JJA1164), and the Technology Project of Chongqing Municipal Education Commission (KJ120401). REFERENCES [1] Tang, L. et  al. 2008. Automated Pavement Crack Detection Based On Image 3D Terrain Model. Computer Engineering 34(5): 21–38. [2] Zhang, H.G. et al. 2005. Pavement Distress Detection Based on Artificial Population. Journal of Nanjing University of Science and Technology 29(4): 389–393.

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[3] Bray, J. et al. 2006. A neural network based technique for automatic classification of road cracks. Proceedings of International Joint Conference on Neural Networks 907–912. [4] Ma, C.X. et  al. 2009. Pavement Cracks Detection Based on NSCT and Morphology. Journal of Computer-Aided Design and Computer Graphics 21(12): 1761–1767. [5] Pu, Y.F. 2007. Application of fractional differential approach to digital image processing. Journal of Sichuan University (Engineering Science Edition) 39(3): 124–132. [6] Yang, Z.Z. 2006. Fractional order differential in the study of the application of the modern signal analysis and processing. Chengdou: Sichuan university. [7] Thangavel, K. & Karnan1, M. 2007. Automatic detection of asymmetries in using genetic algorithm. International Journal on Computer Methods and Programs in Biomedicine 87: 12–20.

[8] Pincherle, I. 1990. Fractional differential equations. 20–23. SanDiego: Academic Press. [9] Jiang, W. 2011. New image denoising model based on fractional-order partial differential equation. Journal of Computer Applications 31(3): 753–756. [10] Guo, L.Z. & Zhao, J.H. 2007. Edge detection based on wavelet transforms. Journal of Qingdao Technological University 28(2): 78–80. [11] Zhang, J. et  al. 2009. Automatic identification of pavement crack image enhancement technique. Journal of China and Foreign Highway 29(4): 301–305. [12] Yang, Z.Z. et  al. 2008. Edge detection based on fractional differential. Journal of Sichuan University (Engineering Science Edition) 40(1): 152–157.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Molecular algorithm in solving the shortest path problem of the application and research L. Zhang College of Sciences, Chongqing Jiaotong University, Chongqing, China

ABSTRACT: Interest in DNA computing has increased overwhelmingly since Adleman successfully demonstrated its capability to solve Hamiltonian Path Problem (HPP). One line of DNA computing research focuses on parallel search algorithms, which can be used to solve many optimization problems. In this article, we have implemented a new DNA computation-based optimal path planning algorithm on 3D space. The molecular programming algorithm imitates the biological evolution mechanism through artificial programming to enhance the opportunities for searching the shortest moving path while avoiding obstacles. Simulation results are presented to show the effectiveness and applicability of the proposed approach. Keywords: 1

DNA computation; optimal path planning; crossover; mutation

INTRODUCTION

evolutionary process. This work presents clear evidence of the ability of molecular computing to solve the NP-complete problem with mathematical operations. The article is organized as follows: Section  2 introduces the DNA computing in detail. Section 3 introduces the DNA program to solve the optimal path planning problem in three-dimensional (3D) space. In Section 4, experimental results by simulated DNA computing are given. Conclusions and discussion are presented in Section 5.

The shortest path between two given points over a curved surface is a difficult problem in theoretical and practical field. It is widely used in the mapping, the span of electric power and LOC, the layout of the railway, the road, the path of robot etc. It will take huge economical benefit if it has been resolved well. Up to now, genetic algorithm and evolutionary algorithm have been widely applied for system optimization and industrial applications. Because traditional GA or EA can neither represent the diverse genetic information nor can better imitate the regulation of genes to the genetic processes, some biological operations at the gene level cannot be effectively presented in the existing GA or EA. Especially on three-dimensional (3D) space it is a thorny issue. In addition to GA and EA, DNA computing [1] has recently captured more and more attention [2] since DNA sequences encode plentiful genetic information. This article presents an optimal path planning method for the moving object on threedimensional (3D) space using DNA molecular structures and molecular programming. The algorithm is designed to analogously accomplish the evolutionary process. In the proposed method, the 3D workspace is inserted into several points, the DNA sequences are used to represent the points, connecting all optimal points while avoiding the obstacle areas builds the optimal moving path, and the shortest path for obstacle avoidance in the workspace is finely characterized with DNA

2

DNA COMPUTING

In the late 1950s, Feynman first introduced the idea of computation at a molecular level, but his idea was not implemented by experiment for a few decades. In 1994, Adleman wrote the first article that demonstrated that DNA strands could be applied for dealing with solutions of the NP-complete Hamiltonian Path Problem (HPP). Since then, the possibility of DNA computation has attracted many researchers’ attention. In 1995, Lipton [3] wrote the second article that showed that the new techniques could also be used to solve the NPcomplete satisfiability (SAT) problem. Adleman and his co-authors [4] proposed sticker for enhancing the Adleman–Lipton model in 1999. DNA computation uses chromosomes of DNA to describe a specific problem and to manipulate the chromosomes using techniques commonly available in molecular bank for simulating

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the goal along a shortest path while avoiding static obstacles on the workspace. In practical projects, this is very common. In this article, the algorithms will bypass the designated area, and will access a feasible and shortest path successfully. 3.1 The initial path and coding schemes The initial DNA sequences are generated randomly, suppose the workspace is designated. The origin is A( x0 , y0 ), the goal is B ( xn , yn ), wij is the edge weight between two random nodes i and j which have a connection with each other. Divide the area of [ 0 , xn ] into n slices, meanwhile the partition of [ y0 , yn ] is ascertained. Each point we use the DNA strands to encode, the length is 20, and mark it as Oi (i , 2, , n ) . Edges that connect nodes encoded and formed by the following sequence: the first part is a 10 codes comes from the 3′ of the Oi; the middle part is the codes of the edge weight; and the last 10 codes appears at the 5′ of the Oj. In Figure 2, let O2 O3 → O4 as an example to explain the coding of the paths.

Figure 1. The flow of the optimal algorithm.

operations, which can be used to seek for the solution. The central idea of DNA computation is the Watson–Crick model of DNA structure, which specifies complementary binding properties of DNA molecules. The basic elements of biological DNA are nucleotides. Due to their different chemical structure, nucleotides can be classified as four bases: A (Adenine), G (Guanine), C (Cytosine) and T (Thymine). A single strand of DNA can be likened to a string consisting of a combination of the above-mentioned four different symbols; this means that we have an alphabet ∑ {A, G, C, T} to encode information. Any single-stranded DNA will adhere tightly to its complementary strand, in which G always pairs with C and A always pairs with T, and vice versa. The process of DNA computation can be refined into two steps. The first step is to generate all possible solutions to the problem by mixing DNA solutions. DNA complementary binding reactions occur in parallel and extremely fast upon mixing. The second step is to isolate correct solutions through repeated separations of the DNA strands from incorrect solutions and potentially good solutions. A schematic representation of DNA computation is given in Figure 1. DNA computation is attractive mainly for three reasons. First, the computation realizes fast parallel information processing. Second, the process is remarkably energy efficient. Finally, DNA molecules have very high storing capacity. DNA computers have been shown to be at least equivalent to a classical Turing machine [5]. 3

3.2

Choose the fitness function

When listing facts use either the style tag List signs or the style tag List numbers. 3.2.1 The design of avoiding obstacles The slope and length are two important factors in path planning. In the numerical simulation example, the value of z on the roadblock will be added a large constants cˆ ( ˆ max( ij )) , zij is the elevation value of the corresponding point which is decided by the landform. Because the fitness of the slope and length are all poor, the individual which gets across the obstacle areas will be eliminated during the operation. 3.2.2 The calculation of fitness value After times of genetic operation, the new path set is appearing, we define it for Γ k , and suppose rki is the node sequence of the ith path in Γ k .

PATH PLANNING AND OBSTACLES AVOIDANCE

Our goal is to characterize a feasible moving path which is required to start from the origin and reach

Figure 2.

The coding of paths.

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rki

i i i {( xki1, yki 1 zki1 ),(x ) (xki 2 yki 2 , zki 2 ), ,( xkn , ykn , zkn )}

In the path searching, in order to obtain some better individual, part of the initial DNA sequences should be interchanged, the via-points in an adjacent area can be displaced by each other through the crossover process to change their forward directions and have more chances to avoid the obstacles. Crossover operation can avoid the algorithm getting into the local optimal solutions, the design of the operator is especially important. The descendant must keep the excellent ingredient of their fathers, and must have their validity.

(1) In which, i 1, 2, …, m , m is the quantity of the g, and g is the evolutional genroutes; k erations. Suppose Lik is the length of the ith path. Lik

rki

n−1

= ∑ ( xki

j+ j )

i 2 xkj ) + ( yki

i 2 j ) − ykj ) j+

+ ( zki

i 2 j+ j ) − zkj )

j =1

(2)

3.3.2 Mutation Any change in a DNA sequence is called a mutation, it includes point mutations and structure mutations. In mutation operation, the randomly selected bit in the sequence is replaced by the rule that A changes to T and vice versa. To avoid more duplicate points generated by crossover, the mutation has function to enhance the chances of exchange between the points in a small scope. For the optimal path searching, let the vij and v( i )()( j ) replace the vi( i ) and v j ( j ) when wij + w(ii j + ) < wi ( i + ) + w j(j ( j + ) . This algorithm can eliminate overlapping phenomenon of the sides. When we improve the route by mutation, to every point, their correlative sides are all taking place around it. So we only have to carry through the optimized computation between the point and the points of their adjacent array to improve the loop. This method can enhance the efficiency, and decrease the searching space. Before the mutation:

The Dki

is the sum of variance, the distant height of two connecting nodes will be measured by it. E = (z j

n−1

+ z j + z jj++1 ) 3

(

Dki = ∑ ( zki j =2

j+ j )

i E )2 + ((zzkj

E )2 + ( zki

j− )

)

E )2 3 (3)

So the fitness function is Fitnessk (i ) = 1000 − 0 * Lik

− 0.. * Dki

i 1, 2, …, m; k 3.3

g

(4)

The manipulation of DNA computing

After the initial DNA sequences are generated, we should reproduce the route by the adaptive value which was determined preliminary by the length of the path. The purpose of reproduction is to make the individual which has bigger adaptive value has more chance to breed the descendant. This article adopts the ways of fitness rule and distillate conservation to reproduce.

GACT ATATCGCGGGTTCAACGTGC A GACG 

After the mutation: GACT ATATCGCGGGCAGTTCGTGC A TAGA 

3.3.1 Crossover Crossover is a process of exchanging genetic information between two DNA sequences. For simplicity, the one-point crossover is adopted here where the crossover point is determined uniformly at random. However, a multiple point crossover is also permissible. After crossover, the codes placed in front of the crossover point are kept invariant, while the subsequent DNA codes are interchanged. Before the crossover:

3.3.3 The result After the crossover and mutation, there will be appearing a series of new generation paths, if the fitness is not satisfactory, we should repeat the above operations, all of the operations will make the individual’s fitness value and the average value becomes higher and higher. Until the best individual’s fitness is achieving a restrict value or the individual’s and the average fitness couldn’t change anymore, then the iterative process can be converged to the optimal solution, and the algorithm finished. The final process is to separate the DNA strands. Gel electrophoresis is a technique for separating DNA strands according to its length through a gel in an electrical field based on the fact that DNA is negatively charged [6]. As the separation process

 GACT A ATATCGCGGG TATCGCGGG TTCAACGTGC GACG   ATAG CAGCTCATCG C CAGTTGACAT TCTG G

After the crossover:  GACT A ATATCGCGGG TATCGCGGG CAGTTGACAT TCTG   ATAG CAGCTCATCG C TTCAACGTGC GACG G

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Table 1.

Figure 3. Gel electrophoresis process.

continues the separation between the larger and smaller chains increases as depicted in Figure  3 [7]. At last, we should incise the joint between two nodes by cutting enzyme, a series of polynucleotide fragments are received. Then the path nodes are decided by contradistinguishing the initial coding schemes. 4

n

1

2

3

4

5

6

7

8

xc yc p q u v h

25 87 1.5 1.5 30 20 57

64 28 2 3.5 65 12 19

34 53 3 2 21 13 −32

77 92 2 2 6 25 38

94 55 3 3 11 14 24

65 24 1.5 1.5 13 9 −22

15 11 3 2 11 13 −14

93 14 3 3 13 11 −17

Table 2.

DEMONSTRATIONS AND VERIFICATIONS

This article cites the simulation landform of the literature [8] to search the square space on 100*100. we use the analytical function to simulate the original terrain, and the curved surface has been constructed by the following function:

Parameters of simulated altitude.

Coordination of result nodes.

n

1

2

3

4

5

x y z

5 92 29.2

15 63 2.2

25 54 −23.4

35 43 −14.3

45 19 8.4

n

6

7

8

9

10

x y z

55 12 −1.2

65 13 −3.6

75 10 −2.45

85 9 −12.5

95 9 −15.4

pi q ⎧ ⎛ ⎛ y yc ⎞ i ⎫⎪ ⎪ x xci ⎞ i ∑ hi exp ⎨⎪− ⎜⎝ u ⎟⎠ − ⎜⎝ v ⎟⎠ ⎬⎪ i i i =1 ⎩ ⎭ n

z ( x, y

(5) The data in Table  1 are the parameters of the above-mentioned function. In the simulation example, suppose the initial positions of the object and goal are specified, respectively, at (5,92) and (95,9). The area of x is established for [5,95] and y is been decided in [9, 92]. Then take 10 as the length of stride to divide the area of x into nine segments. We can obtain the value of z through the curved surface function and the value of the x and y, the raised square areas are the roadblock. In the example, choose eight points to divide the workspace into nine slices, every point denoted as the feasible via point, the optimal moving path is obtained by connecting the best via point which is determined by the applying the proposed computing algorithm with the fitness function. After specifying the roadblock, the DNA computing-based search algorithm is activated in the path planner to generate via points for the short and safety path to the goal. The scale of the initial population is 20, let 0.1 as the reproductive probability, 0.7 is the crossover probability, and the mutation probability is 0.05, the most iteration number is three hundreds, and cˆ = 500 . After the simulation example, the length of the best path is L = 190.87. But the

Figure 4.

The three-dimensional sketch drawing.

length when it is only been connected by beeline between two points is = 201.2 , the result is better than the method mentioned in [8], the optimization efficiency λ is:

λ=

S−L × 100% = 5.13% S

(6)

The best node sequences of the function are presented in Table 2. Simulation results confirm the feasibility of our proposed approach (see Figs. 4 and 5). It is obvious that with this method the path planning is reasonable, it bypasses the roadblock and some steep places well.

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nature. Hence, the applicability of DNA computing could be extended into greater fields of other engineering-related problems. REFERENCES

Figure 5.

5

1. Adeleman L.M., 1994, Molecular computation of solutions to combinatorial problems. Science. (266), 1021–1024. 2. Garzon M.H. and Deaton R.J., 1999, Bimolecular computing and programming, IEEE Trans. On Evolutionary Computation, vol. 3, no. 3, 236–250. 3. Lipton R.J., 1995, DNA solution of hard computational problems. Science. (268), 542–545. 4. Roweis S., Winfree E., Burgoyne R., Adeleman L.M., et  al., 1999, A sticker based model for DNA computation. Second Annual Workshop on DNA Computing, Princeton University. American Mathematical Society, 1–29. 5. Maley C.C., 1998, DNA computation: theory, practice, and prospects. Evol. Comput, 6(3), 201–229. 6. Paun G., Rozenberg G., Salomaa A., 1998, DNA Computing: New Computing Paradigms. Lecture Notes in Computer Science, Springer-Verlag, Vol. 1644 106–118. 7. Amos M., 1997, DNA Computation. PhD Thesis, The University of Warwick, UK. 8. Huang Zhang-can, Chen Si-duo, Kang Li-shan. 2000, Solving the Shortest Path on Curved Surface Based on Simulated Annealing Algorithm. The transaction of Wuhan University, 6(46)3: 273–276.

The contour schematic drawing.

CONCLUSION

It is expected from experimental results that the shortest DNA sequence length will represent the required optimal path planning on 3D space. Based on the DNA computing algorithm, a novel process for charactering the optimal path while avoiding obstacles is developed. With the successful confirmation of the expected result, the applicability of DNA computing could be extended into many more complex problems of this type of

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A method about video quality assessment in video phone service over 3G network C. Feng & L.F. Huang School of Information Science and Technology, Xiamen University, Xiamen, China

W.J. Xu Jimei University, Fujian, China

ABSTRACT: The method about video phone service and quality evaluation on TD-SCDMA has been studied in this article. A wireless module LC6311+ is used as the front-end video data transceiver equipment and the ITU-T J.247 PEVQ algorithm is used as the back-end video quality evaluation model. The LC6311+ wireless module is developed via AT commands over videophone CS64k link. After sending a prepared video compression stream over the CS64k link, the receiving end will get the video stream and preserve it. The original video sequences and the degraded video sequences can be obtained after decoding them. The degraded video sequences are imported to PEVQ model, comparing them with original video and analyzing them. The score of the video quality can be obtained. The results show that this method may be effective for an objective assessment of the video quality of the 3G network evaluation. It is useful for mobile network operators to optimize scalable video quality. Keywords: 1

in video quality assessment; TD-SCDMA; video phone; PEVQ; LC6311+

INTRODUCTION

the front-end for capturing the video data and the back-end for video quality assessment [2]. The LC6311+ wireless module is described in this paper. A standard H.263 video stream is sent by videophone services through the CS64k channel to the receiver. It is studied by the degraded video stream. After obtaining the decoded source video and the degraded video, the video quality evaluation score can be calculated from the PEVQ model. The main indicator of PEVQ is introduced in the final part [3].

In 3G or LTE mobile communication system, the most attractive service is about video transmission. But the key indicator that affects the user experience is the wireless video quality. The methods of currently wireless video quality assessment are divided into two basic categories: Subjective evaluation and objective evaluation. Many observers are required to rate the quality of the video in subjective evaluation. The disadvantage is time-consuming and expensive. So it is not widely used. It is widely used for objective evaluation of video quality because it has less cost, time-saving, objective, reliable and repeatable etc. The common algorithm about image quality assessment is the Peak Signal-To-Noise Ratio (PSNR), the edge of the Peak Signal-To-Noise Ratio (EPSNR) and structural similarity (SSIM) [1]. There is a number of recommendations for video quality assessment including ITU-T J.247 recommended evaluation model—Perceptual Evaluation of Video Quality (PEVQ). It has become one of the major video quality assessment methods. The PEVQ is the full reference video quality evaluation model. A complete reference source video is needed. For evaluating the video quality of videophone service, there are two main steps:

2

THE BASIC PRINCIPLE OF VIDEOPHONE SERVICE QUALITY EVALUATION

As the current TD-SCDMA test terminal does not provide relevant programming interface, the TD-SCDMA test terminal cannot be directly used to get the video data. The video sample data files saved on the computer’s hard disk are encoded in accordance with videophone 3G-324M protocol. Then the encoded video stream is transmitted over videophone link (CS64k) to simulate videophone data transmission process. The 3G-324M protocol is integrated on the LC6311+ wireless module. Meanwhile, the video quality assessment method

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and memory system includes the core processor which is a dual-core processor: BlackFin DSP core, running physical layer protocol and AMR codec; ARM926 MCU core, running senior protocols, and communication with external interfaces. External interface provides external connectivity including power input, communication interface, control signals and debugging interface. The LC6311+ module uses the USB interface to communicate with the PC. It provides two Endpoint links to achieve CS64k signaling and data interaction which is operated by AT commands. Endpoint6 is used as the link1 which mainly makes video calls and hangs up the phone. Endpoint4 is used as link2 for the interaction of video data. All the USB endpoints are developed as virtual serial port. In a video call, the sending data rate is required to meet channel rate CS64k, i.e. 64k = 48k (for video data) +12.2 k (for voice and data)  + control information. Because the voice data are acquired with the LC6311+ module, the control information is also packaged in it. The terminal application need to control the transmission rate of the video data. The LC6311 + modules are operated via AT command set which mainly reference the 3GPP 27.007, 3GPP 27.005 and ITU-T V.25. The AT commands is generally used in connection with the communication between the terminal device and the PC [5]. The flowchart of operation with LC6311+ including the module initialization, startup, calling a video phone and shutdown is shown in Figure 2.

Figure  1. Block diagram of video quality assessment for video phone.

uses the ITU-T J.247 PEVQ (Perceptual Evaluation of Video Quality). The algorithm compares source video (SRC) and the video after transmission (PVS). Then the MOS score which is an objective assessment to simulate the user’s subjective evaluation can be calculated [4]. The basic principle of the design is shown in Figure 1. 3

THE VIDEO PHONE SERVICE SIMULATION WITH LC6311+

We use the LC6311+ TD-SCDMA wireless module to call a CS64k videophone service. The video data is transmitted through the channel so as to obtain the test video data. The terminal uses MTK Co.  Ltd ASIC chipset, including RF subsystem and analog baseband subsystem. The RF subsystem includes radio frequency transceivers, amplifiers and transceivers, such as switches and filters. The analog baseband subsystem integrated DAC, PMU. PMU (Power Management Unit) module contains a number of different voltage and current path in the power supply unit to meet the needs of the power supply. Digital baseband processor

Figure 2.

Call a video phone with LC6311+.

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luminance part and the chrominance part of the edge images. E x [ i , j ,t ] =

Pedge,x [i, j ,t ] − Sedge,x [i, j ,t ] Sedge,x [i, j ,t ] ( x isY i oor Cb or Cr)

W −1 H −1

Figure 3.

The saved SRC and PVS.

Ex [t ] = 2

i =0 j =0 W −1 H −1

∑ ∑ w [i j ]

( x isY or Cb or Cr) w[ i , j ]

⎛ i ⎞ ⎛ j⎞ sin π ⋅ sin π ⎝ H⎠ ⎝ W⎠

(2) (3)

The H and W represent the row and column. The i and j represent the coordinate of the pixel. 4.1.1 Luminance indicator When the x is Y, it means equations (1) and equation (2) compute the luminance indicator. The indicator not only indicates loss of sharpness. Also, the introduction of sharpness is registered as a distortion. This indicator is perceived as a loss of sharpness. Some source sequences have an overall higher edginess than others. The introduction of edginess in areas with a lot of edginess is less disturbing than the introduction of sharpness where little edginess is originally present. The luminance indicator is then calculated by averaging the frame wise edginess distortions over time

THE VIDEO QUALITY ASSESSMENT WITH PEVQ

In this article, PEVQ algorithm is used to evaluate the video quality of 3G videophone. PEVQ is developed by OPTICOM and recommended by the ITU-T J.247. The evaluation results have been recognized by many manufacturers. It gives different weighting coefficients for different indicators in order to approximate subjective evaluation value [6]. The PEVQ is a full reference model which requires the same resolution source video sequence (SRC) and test video sequence (PVS). The PEVQ uses several fitting parameters which are extracted from the time domain, space domain, luminance and chrominance domain of the SRC and PVS. After extracting the parameters, they are imported to the model. So the MOS score can be obtained. All the indicators are described as follows: 4.1

2

E x [ i , j , t ] w[ i , j ]

i =0 j =0

In Figure  2, the OpenComm() is used to open the serial port (USB Endpoint6) and configure it. Init_LC() function is used to initialize the module. After initialization the Start() function is used to power the module. VoiceDial() function is used to make voice calls and VideoPhone() function is used to make video calls; The Hand_up() function is used to hang up a voice call and Hand_up_VP() function is used to hang up the video phone; and Off() function is used to execute the shutdown command. When the wireless module is powered on, the PC executes module initialization, startup, make video calls and other AT command operation. The compressed bit stream which is sent by wireless module can then be decoded by the PC. When we get the transmitted video data, they are imported to the PEVQ module. After calculating, the video quality MOS score can be obtained. The sample frame of SRC and PVS is shown in Figure 3. 4

∑∑

(1)

Lum IIndicator =

1 N −1 ∑ EY [t ] N t =0

(4)

4.1.2 Chrominance indicator The chrominance indicator uses a similar approach as the luminance indicator. When the variable x in equation (1) and equation (2) represents the Cb or Cr. The Ecb [i, j ,t ], Ecr [i, j ,t ], Ecb [t ] and Ecr [t ] are calculated separately. As for the deviation signal, the maximum of the color saturation of the reference signal and the degraded signal is taken as above. The change in edginess of both color components is evaluated. The extraction methods of chromaticity component and luminance component are similar. Chrominance value contains components of Cb and Cr which are calculated separately as above. The chrominance indicator is then calculated by averaging the frame-wise edginess distortions over time:

Spatial variability indicators

The spatial variability indicators involve two indicators: the luminance indicator and the chrominance indicator. They are calculated based on the

Chrom Indicato I r=

1 N −1 ∑ (Ecb [t ] + Ecr [t ]) 2N t = 0

(5)

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4.2

Temporal variability indicators

Table 1. Weight values and model parameter (QCIF).

The edginess indicator is a pure spatial indicator. However, the spatial content of a sequence is judged more critically in case of still images than for images with fast motion and rapid changes. To reflect this, we introduced two indicators: The Omitted Component Indicator (OCI) and the Introduced Component Indicator (ICI). The temporal variability of the processed video signal is also influenced by transmission errors and the presence or absence of frame repeats. As a result, the temporal variability is best measured on the luminance of the source sequence. d [i, j t ] = SY [i j t ] − SY [i, j t −

dO/I [t ] = 5

W H

H −1W −1

∑ ∑d i =0 j =0

5

/ + [i,

1 N −1 ∑ d [t ] N t =0 O

]

1 N −1 2 ∑ dI [t ] N t =0

β[i]

0 1 2 3

LumIndicator ChromIndicator OCI ICI

6.184 2.501 −5699.585 −0.866

0.168 1.351 0.0036 0.311

−1.449 −17.748 8.729 −6.767

The VQA result of PEVQ method.

j ,t ]

Hall

Carphone Suzie

2.9136 2.9133

Akiyo

2.7821 2.7665

(6) 4.3 The aggregation of indicators The perceived video quality is estimated by mapping the Indicators to a single number using a sigmoid approach. Let I[i] represent the indicators. Then the mapping function may be defined by a set of input scaling factors I[i], a set of scaling factors w [i ], α [i ] and β [i ]: 3

Score Offs O et + ∑ i =0

w[ i ] 1 + eα [ i ] I [i ] β [i ]

(10)

Mapping coefficients used for QCIF resolution. The offset is equal to −0.93 [6]. The PEVQ Score of five test video sequences: Foreman, Hall, Carphone, Suzie and Akiyo are given in Table 2. 5

CONCLUSION

With the rapid development of 3G video phones and other wireless video services, in order to enable users to obtain a better subjective experience, the importance of video quality assessment in the development of wireless video services, network optimization and other areas are also increasingly prominent. In this article, LC6311+ wireless module and FFmpeg video codec library use the LC6311 modules via serial programming completed encoding YUV material sent and program received design; realize the video of the original material and synchronization obtained through material wireless transmission after the; adoption PEVQ objective evaluation of the degradation of the video quality of the video. After the experiment, the method can be effectively applied to TD-SCDMA system CS64k videophone business objective evaluation of video quality, the video can optimize the quality of service operators, and network planning and optimization work to provide a convenient way.

(7)

(8)

The Introduced Component Indicator (ICI) is then calculated using an L2 Norm over the framewise introduced distortions over time ICI C =

α[i]

PEVQ 2.8881

The Omitted Component Indicator (OCI) is then calculated by averaging the frame-wise omitted distortions over time. OCI C =

w[i]

Foreman

Frame loss caused by wireless transmission not only causes big difference between the two continuous frames in SRC and PVS, but also significantly influences the subjective feeling ability of human eyes. The temporal variability indicators in PEVQ model is defined as equation (6), where d [i, j ,t ] is computed per pixel per frame, SY [i, j ,t ] and PY [i, j ,t ] are representing the luminance of pixel at the SRC and PVS of frame t. Therefore, this algorithm can only be used in full reference evaluation model of video quality. The HVS reacts differently if a new component is introduced to the signal then if it is removed from the signal. Therefore, two different indicators are evaluated. To measure the omitted component part, the negative part (d [i, j ,t ] ) and the positive part − (d [i, j ,t ]) of d [i, j ,t ] are separately calculated as + equation (7). 1

I[i]

Table 2.

]

− PY [i j t ] − PY [i, j t −

i

(9)

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ACKNOWLEDGEMENTS

[2] Margaret H Pinson and Stephen Wolf, A New Standardized Method for Objectively. IEEE Transactions on Broadcasting, 2004, 50(3):312–322. [3] Chao Feng, Image quality assessment-oriented frame capture of video phone over 3G system; Anti-counterfeiting, Security and Identification 2010, Page(s): 359–362. [4] Xin-Bo Gao, Quality Assessment Methods for Visual Information, Xi’an, XiDian University Publishing house, 2010. [5] Leadcore Technology Co. Ltd. The Video Phone Development Manual_1.10 based on LC6311+ [DB]. [6] ITU-T J.247 Objective perceptual multimedia video quality measurement in the presence of a full reference[S], 2008.

The work presented in this paper was partially supported by 2011  National Natural Science Foundation of China (Grant number 61172097), 2014  National Natural Science Foundation of China (Grant number 61371081), 2012  Natural Science Foundation of Fujian (Grant number 2012  J01424) and Foundation by Comba Co., Ltd. REFERENCES [1] Kjell Brunnstrom. David Hands. VQEG Validation and ITU Standardization of Objective Perceptual Video Quality Metrices[J]. IEEE Signal Processing Magazine [96] MAY 2009.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis on the degree of the international fragment of China’s manufacturing industry based on processing trade Y.H. Yang School of Economics and Management, Yunnan Normal University, Kunming, China

ABSTRACT: This article constructs measuring indexes of the international fragmentation production of manufacturing industry based on the data of processing trade. Using China’s manufacturing trade data encoded into 4-digit HS code, it measures the international fragmentation production of China’s 26  manufacturing industries. The result shows that there are industrial differences in the participation of international fragmentation of China’s manufacturing production, that is, the degree of international fragmentation production of some industries is high, while others’ is low. Some industries participate in the international fragment of production mainly through the processing trade exports. While others participate in the international fragment of production mainly through the processing trade imports. Other participations of imports and exports are more prominent. Keywords: international fragmentation of production; processing trade; China’s manufacturing industry 1

INTRODUCTION

extent of our participation in the international fragmentation of production has been increasing. Manufactured goods are the subject of China’s participation in the international fragmentation of production, which is an important form of China’s participation in international economic activity. It is a valuable research to accurately measure the degree of international fragmentation of Chinese manufacturing industry and to comprehensively analyze China’s manufacturing industries’ participation in the international fragmentation of production. Input–output method has been commonly used in the literature to measure international degree of fragmentation in the production of our manufacturing industry (Zhao lei and Yang Yonghua, 2011). Due to the unavailability of the key variable—the intermediate input amount of data—the intermediate goods imports were assumed, that is, the proportion of industry’s total import of intermediate products is equal to the import of k industries. There are some deviations from the intermediate input the actual situation of various industries. Therefore, the input–output method to measure the true degree of international fragmentation of China’s manufacturing production has its limitations. Therefore, this article uses the data of processing trade which is relatively easily available to measure China’s manufacturing industries’ international fragmentation of production.

Since the reform and opening up, China’s export expensed rapidly. The rank of the total amount of China’s export rose from the 28th in 1980 to the 2nd in 2007. From 2009 to 2011, China remains the world’s largest exporter. China’s exports are mainly manufactured goods-oriented. In recent years, the share of manufactured goods in total exports has remained at 95%, and exports of manufactured goods grew faster than the growth rate of exports of all goods. The basic phenomena of current products is that different processes, sectors, and components in the production process scattered to different countries and regions, thus the same final product was manufactured by different countries or regions’ participations in the international fragmentation of production. Two or more countries or regions completed a final production through the Foreign Direct Investment (FDI) or outsourcing activities to produce or assemble parts. A distinguishing feature is that the intermediate input circulated in nations, resulting in a large number of intermediate goods trade. Processing trade is one of the basic forms of the international fragmentation of production, as well as China’s main way to participate in the international fragmentation of production (Lufeng, 2004). China’s processing trade export share of total export increased from 5% in 1981 to 56% in 1996, then at around 55% by 2009. Obviously, the

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2

in 1997 and in 2002, other manufacturing industry in 1997, other industry in 2002, arts and crafts industry and the processing of waste recycling in 2007. We categorized some industries and unified their names, such as classifying agro-food processing and food manufacturing as food processing and manufacturing, classifying wood processing and wood, bamboo, cane, palm, grass products industry and furniture industry in the year of 2002 and 2007 as wood processing and furniture manufacturing, classifying sawn timber processing and furniture, wood and man-made board manufacturing and grass bamboo rattan palm products manufacturing in 1997 as wood processing and furniture manufacturing, so as to get 26 manufacturing industries. For the convenience of following analysis, we encode the 26 manufacturing industries (see the note of Table 1).

MEASUREMENTS OF INTERNATIONAL FRAGMENTATION IN PRODUCTION BASED ON PROCESSING TRADE DATA

Processing trade is to use foreign parts, components and other resources and to process, manufacture, assemble and produce in domestic country, which is then sold in foreign trade. Processing trade is one of the basic forms of the international fragment of production, as well as China’s main way to participate in the international fragment of production. Measurements of international fragmentation in production based on processing trade data are mainly used in many literatures (Yeats, 1998; Görg, 2000; Baldone et al., 2001, 2007; Helg and Tajoli, 2005). Referenced Balassa index of revealed comparative advantage, and Baldone et  al. (2007), we can construct a metric index of China’s manufacturing industries international fragment of production as following: RFP PijT = RFP PijM =

PT T jt / T jt PT Tt / Tt

, RFP PijX =

PTM M jt / TM M jt PTM Mt / TMt

PTX X jt / TX X jt PTX X t / TX t

Table 1. Tendency of international fragment of China’s manufacturing production based on the measurement of processing trade export from 2001 to 2008.

, Industry year C1 Average

(1)

3

Average

C4

C5

C6

C7

C9

C10 C11 C12 C13 C14

1.02 1.35 1.19 0.48 0.42 0.38 0.48

Industry year C15 C16 C17 C18 C19 C20 C21 Average

1.50 1.03 0.41 0.28 0.73 0.58 0.77

Industry year C22 C23 C24 C25 C26 Average

0.75 1.39 1.20 1.62 1.46

Note: Corresponding of codes and categories: C1 (food processing and manufacturing industry), C2 (beverage manufacturing industry), C3 (tobacco industry), C4 (textile industry), C5 (textile and garment, shoes, hats manufacturing industry), C6 (leather fur and feather industry), C7 (timber processing and furniture manufacturing industry), C8 (papermaking and paper products industry), C9 (printing and record medium reproduction), C10 (cultural educational and sports goods), C11 (petroleum refining and coking), C12 (chemical raw materials and chemical products manufacturing industry), C13 (pharmaceutical manufacturing industry), C14 (chemical fiber industry), C15 (rubber product industry), C16 (plastic product industry), C17 (non-metallic mineral products), C18 (black metal smelting and rolling processing industry), C19 (non-ferrous metal smelting and rolling processing industry), C20 (fabricated metal products), C21 (ordinary machinery manufacturing industry), C22 (special equipment manufacturing), C23 (transportation equipment manufacturing industry), C24 (electrical machinery and equipment manufacturing industry), C25 (communication equipment computers and other electronic equipment manufacturing industry), C26 (instrumentation and cultural office machinery). Source: Calculated based on IIR network database data.

DESCRIPTION OF THE DATA SOURCE AND INDUSTRY SELECTION

3.1

C3

0.55 0.14 0.05 0.48 0.66 0.75 0.67

Industry year C8

PijT , RFP PijX and FP PijM , respectively, Here, RFP represent the indicators of international fragment of manufacturing production measured from the perspective of trade import and export volume, export and import; i represents trading nation, and j represents some industry. PT T jt and PT Tt , respectively, represent j industry’s import and export volume of processing trade, and Chinese manufacturing industry’s import and export volume of processing trade in the t year. T jt and Tt , respectively, represent j industry’s import and export volume, and Chinese manufacturing industry’s import and export volume in the t year. PTM M jt , PTX X jt , PTM Mt and PTX X t , respectively, represent j industry’s import and export volume of processing trade, and Chinese manufacturing industry’s import and export volume of processing trade in the t year. MT T jt , XT T jt , MT Tt and XT Tt , respectively, represent j industry’s import and export volume, and Chinese manufacturing industry’s import and export volume in the t year.

C2

Industry selection

According to the National industrial classification, we select the manufacturing industries except arts and crafts industry and the processing of waste recycling

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3.2

Data source

Table  3. Tendency of international fragment of China’s manufacturing production based on the measurement of processing trade import and export from 2001 to 2008.

We can obtain the data in equation (1) from the IIR network database such as import and export of China’s processing trade imports and exports and manufacturing data. But IIR network database data on trade are classified by HS codes (Harmonization Code System Code), and are not according to national industrial classification. Referring to Cheng Bin (2002), we took the related merchandise trade data in correspondence with the 26 selected manufacturing industries. Then we obtained the processing trade data of the industries. Processing trade includes processing and assembling trade, processing trade, processing trade and imports of foreign-invested enterprises as an investment in equipment and supplies. The time span of China HS code of 4-digit data available in IIR network is from 2001 to 2008. 4

Industry year C1 Average

Average

Average

Average

C5

C6

C9

C7

C10 C11 C12 C13 C14

0.69 0.61 1.44 0.05 0.72 0.04 1.49

Industry year C15 C16 C17 C18 C19 C20 C21 Average

0.65 1.12 1.33 0.69 0.91 1.03 0.80

Industry year C22 C23 C24 C25 C26 Average

C7

C9

C10 C11 C12 C13 C14

0.75 1.15 1.25 0.24 0.60 0.18 0.89

1.28 1.07 0.61 0.45 0.83 0.68 0.78

0.81 0.85 1.24 1.47 1.37

tries’ trends above 1  such as C8, C9, C10, C15, C16, C23, C24 and C26. Other industries’ trends are relatively lower, such as C2, C3, C17, C18, C12 and C13. Table 2 shows the tendency of international fragment of China’s manufacturing production from 2001 to 2008 measured by the import of processing trade. Among them, C6 has the highest trend. There are some industries’ trends above 1 such as C4, C5, C10, C14, C16, C17, C24, C25 and C26. Other industries’ trends are relatively lower, such as C2, C3, C11, C13 and C23. Table  3  shows the tendency of international fragment of China’s manufacturing production from 2001 to 2008 measured by the total amount of import and export of processing trade. Among them, C25 has the highest trend. In addition, there are some industries’ trends above 1  such as C9, C10, C15, C16, C24 and C26. Other industries’ trends are relatively lower, such as C2, C3, C11 and C13. Obviously international fragment of production measured by the total amount of import and export of processing trade reflects the general participation of China’s manufacturing industries. But there are different tendencies of international fragment of China’s manufacturing production measured by the import or export of processing trade. It also shows that some industries participate in the international fragment of production mainly through the processing trade exports, such as C8, C9, C15, and C23. While others participate in the international fragment of production mainly through the processing trade imports, such as C6, C4, C5, C14 and C17. Other participations of imports and exports are more prominent, such as C10, C16, C24, C25 and C26.

0.43 0.03 0.28 1.94 1.32 1.71 0.91

Industry year C8 Average

C4

C6

Source: Calculated based on IIR network database data.

Table 2. Tendency of international fragment of China’s manufacturing production based on the measurement of processing trade import from 2001 to 2008. C3

C5

Industry year C22 C23 C24 C25 C26

In equation (1), we use the 26  manufacturing industries’ data such as export, import, import and export trade, processing trade import and export, and total amount of import and export to calculate and measure the degree of international fragment of China’s manufacturing production from the point of import and export (see Tables 1–3). Table 1 shows the tendency of international fragment of China’s manufacturing production from 2001 to 2008 measured by the export of processing trade. From the perspective of average, C25 has the highest trend. In addition, there are some indus-

C2

C4

Industry year C15 C16 C17 C18 C19 C20 C21

DEGREE OF INTERNATIONAL FRAGMENT OF CHINA’S MANUFACTURING PRODUCTION BASED ON PROCESSING TRADE

Industry year C1

C3

0.50 0.12 0.09 0.70 0.72 0.88 0.73

Industry year C8 Average

C2

0.85 0.09 1.30 1.27 1.36

Source: Calculated based on IIR network database data.

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5

degree of participation in the international fragment of production, and international fragment of production greatly accelerated the growth of manufacturing output and exports in China. Meanwhile, it also showed that China’s manufacturing output and exports contained large amount of the foreign investment. After the deduction of foreign intermediate inputs, the real added value and export of Chinese manufacturing industry might not be so much, and so does the trade interests. So, what kind of stage should Chinese manufacturing production sections are in the international division of production system, and how to strive for a higher degree of fragmentation in the production with high added value and more interest? This research direction is worthy of further investigation.

CONCLUSIONS

The advantages of using metrics based on processing trade data to measure the degree of China’s participation in the international fragmentation of production is availability of data and that processing trade imports and exports can comprehensively reflect the situation and the characteristics of China’s manufacturing industries’ participation in the international fragment of production. Measurements based on processing trade data shows that from the perspective of overall imports and exports, seven manufacturing industries have higher degree of international fragmentation of production, respectively: communication equipment, computers and other electronic equipment manufacturing industry, printing and record medium reproduction, cultural and educational sporting goods manufacturing industry, the rubber products industry, the plastic products industry, electrical machinery and equipment manufacturing industry, instrumentation and cultural office machinery. Other four industries have lower degree of international fragmentation of production, respectively: beverage manufacturing industry, tobacco industry, oil processing and coking industry and pharmaceutical industry. Papermaking and paper products industry, printing and record medium reproduction, rubber Product industry and transportation equipment manufacturing industry participated in the international fragmentation of production through the export of processing trade. Industries of leather fur and feather, textile industry, textile and garment, shoes, hats manufacturing industry, chemical fiber industry and non-metallic mineral products participated in the international fragmentation of production through the import. Cultural educational and sports goods, plastic product industry, electrical machinery and equipment manufacturing industry, communication equipment, computers and other electronic equipment manufacturing industry and instrumentation and cultural office machinery participated in the international fragmentation of production through both the import and export. Obviously, most of China’s manufacturing industry had relatively high

ACKNOWLEDGMENT This research is supported by Natural Science Foundation of China (No. 71163047).

REFERENCES Buckley, P.J. The Impact of the Global Factory on Economic Development [J]. Journal of World Business, 2009, (44):131–143. Egger, H. & Egger, P. (2003), “Outsourcing and skillspecific employment in a small economy: Austria after the fall of the Iron Curtain”, Oxford Economic Papers 55(4), 625–643. Feenstra, R.C. & Hanson, G.H. (1996), “Globalization, Outsourcing, and Wage Inequality,” American Economic Review, 86(2), 240–245. Hummels, D., Rapoport, D. & Yi, K.-M. (1998), “Vertical specialization and the changing nature of world trade”, Federal Reserve Bank of New York Economic Policy Review 4(2), 79–99. Hummels, D., Ishii, J. & Yi, K.-M. (2001), “The nature and growth of vertical specialization in world trade”, Journal of International Economics 54(1), 75–96. Maskell, P., Pedersen, T., Petersen, B. & Dick-Nielsen, J. Learning Paths to Offshore Outsourcing: from Cost Reduction to Knowledge Seeking [J]. Industry and Innovation, 2007,14, 14(3):239–257.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The voltage-controlled low-pass filter based on FPGA frequency measurement Sheng-Qian Ma, Li-Rong Zheng, Juan-Fang Liu & Yan-Ping Ji Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu, China

ABSTRACT: This paper describes the circuit and implementation method of the voltage-controlled self-tracking low-pass filter, which uses FPGA to measure the frequency of the input signal, and uses a special method to make frequency convert to corresponding voltage by D/A converter. Then the voltage signal is input into voltage-controlled low-pass bi-quad loop filter circuit which is mainly composed of an analog multiplier AD835 and the current feedback operational amplifier OPA658. Cut-off frequency of the filter can automatically track the input signal frequency changes by adjusting the external control voltage through FPGA programming. This paper describes the system work principles, infers the transfer function of the filter and designs the circuit of voltage-controlled second-order low-pass filter. The filter can realize the scope of the cut-off frequency up to 8 MHz. The simulation and experiment results show the valid of the filter system designed. Keywords: FPGA frequency measurement; D/A; frequency automatically tracking; voltage-controlled low-pass filter 1

INTRODUCTION

difficult to achieve the tracking of the frequency of the input signal. This paper proposes a voltage-controlled selftracking filter system that can control the cut-off frequency of the filter by changing the external voltage. In the system, FPGA is used to measure frequency of the input signal, and D/A convertor is used to convert the measured frequency to voltage signal which will be input into the bi-quad loop filter as the external control terminal to control the cut-off frequency to track the input signal frequency changes automatically. Compared the voltage-controlled filter designed in this paper with other voltage-controlled filters, its cut-off frequency can dynamically adjust, and filtering speed is high, waveform is smooth and other features.

The filter has a wide range of applications in the fields of the signal processing, the data collection and the communication systems. Especially, the self-tracking filter plays an important role in the field of the adaptive signal processing. There are many kinds of methods to realize the automatic self-tracking filter. The first is the state variable method which can be carried out simultaneously low-pass, high-pass and band-pass filter, but the design and debug are troublesome and difficult to control, and besides the components are discrete and the non-linear effects are large. The second method is the voltage-controlled method. Using this method when the input signal frequency is within the bandwidth of the filter, there is no phase difference between the filtered signal and the input signal. But the offset voltage and drift will directly affect the stability of the low frequency. The third method is using existing active filter chip. This method does not require to know the signal frequency in advance, and filtering frequency is controllable, but there are some circuit noise and signal aliasing problems in the course of the use, even through clock frequency or pin programming is difficult to achieve the filter cut-off frequency continuous change. If the signal frequency changes in a wide range, the above three methods are

2

FREQUENCY VOLTAGE CONVERSION

The frequency voltage conversion circuit is composed of FPGA and D/A. FPGA measures the frequency of input signal, and then D/A converts the measured frequency into voltage. It is necessary to establish a linear relationship between the measured frequency and the converted voltage. Then we can obtain the voltage corresponding to different frequency by finding the scale factor.

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2.1

Frequency measurement circuit based on FPGA

The frequency measurement circuit uses the EP2C20F484C8  N of Altera Corporation as the core chip. The chip have 50 MHz active crystal and 50 MHz active clock inside. The measured signal fin is amplified, shaped and limited by signal preprocessing module, and the shaped rectangular pulse fx is input into FPGA as the input signal. The diagram of FPGA frequency measurement is shown in Figure 1. The crystal oscillator provides 50  MHz clock signal fs. The signal fs is divided into two paths, the first inputs into counter 2 as clock pulse, the other one makes frequency division through signal source module and generates 1  second gate time Td, which is regarded as the clock input of the control module. The control module generates the count enable signal EN and the reset signal CLR. When measuring, the first thing is resetting the two counters. When the signal EN is for high level and the rising edge of fx signal to be measured is arrived, counters start to count the measured signal and the clock signal separately. When EN is for low level and the rising edge of fx signal to be measured is arrived, counters stop counting. The count value is latched and sent to display unit. Assuming within a gate time Td, the count value of the measured signal counted by the counter is Nx, and the count value of the clock signal is Ns, then the frequency of the measured signal is fx N x /N s fs . 2.2

Figure  2. convert.

The relation of linear frequency-to-voltage

Table 1. The measured and theoretical voltage values of the frequency voltage conversion circuit. fx/Hz

2M

4M

6M

8M

Vx/V Theoretical Measured

0.250 0.248

0.500 0.479

0.750 0.748

1.000 0.996

Relative error δ (%) Absolute error/V

0.800 0.002

0.600 0.003

0.267 0.002

0.400 0.004

in order to achieve a good linear relationship, we design that when the measured frequency is 8 MHz, the converted voltage is 1.00 V, and when the measured frequency is 0  Hz, the converted voltage is 0.00V, thus we can get that the conversion relationship between the measured frequency and the converted voltage is

D/A conversion circuit

D/A conversion circuit is mainly responsible for converting the digital frequency measured by FPGA to the corresponding analog voltage, and loading the voltage signal into the control terminal of the voltage-controlled filter, it can change the cut-off frequency of the filter, so as to achieve the goal of the cut-off frequency automatically tracking the input signal frequency. This design uses a programmable dual 12 bit converter, that is TLC5618 AC D/A converter. Supposing the measured frequency fx is from 0 Hz to 8 MHz, the voltage is from 0.00 V to 1.00 V,

fx = 8 × 106 Vx

(1)

From formula (1), we can calculate the corresponding conversion voltage. The relation of linear frequency-to-voltage convert is shown in Figure 2. The measured and the theoretical voltage values of the frequency voltage conversion circuit is shown in Table 1. From the table, we can know the error of the frequency voltage conversion circuit is small. 3

DESIGN OF VOLTAGE-CONTROLLED TRACKING FILTER

The system diagram of the voltage-controlled tracking filter is shown in Figure 3. The input signal fin is divided into two paths, the first is transformed into rectangular wave signal fx through amplifying, amplitude-limiting and shaping, and put fx into FPGA to measure the frequency, then use the special linear frequency-to-voltage conversion method to control D/A convert the measured

Figure 1. The diagram of FPGA frequency measurement.

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U4, R5 and R7 can reduce the closed-loop gain, improve the stability of gain and decrease the nonlinear distortion. U1 can achieve the square of the signal, which makes a linear relationship between the cut-off frequency and the input frequency. U1 and U3 are both four quadrant analog multiplier AD835, U2, U4 and U5 are low power current feedback operational amplifier OPA658, D0, D1, D2 and D3 are resistance network, Vx is voltage control terminal, fin is input signal terminal, fout is filtered signal terminal. According to Kirchhoff’s law, we can obtain: Figure 3. The system diagram of the voltage-controlled tracking filter.

VW = Vx2

(2)

Vout/R5 = VG/R7

(3)

VWVF = VE = VoutC1sD2

(4)

Vin/D0 = VF/D3 + VFC2s + VG/D1

(5)

According to the formula (2–5), the transfer function of the filter is:

H (s) =

Vout Vin

Vx2 C1C2 D0 D2 = Vx2R7 s 2 s + + C2 D3 C1C2 D1D2R5

(6)

According to the formula (6), it can deduce the cut-off angular frequency is ωLP = (VxR71/2)/(C1C2D1D2R5)1/2 Figure  4. The diagram of the voltage-controlled second-order low-pass filter circuit.

the gain factor is K = (R7D0)/(R5D1)

frequency into the corresponding analog voltage Vx, which is the voltage control terminal of the filter. The other one is as the input signal of voltage-controlled filter, so that the combination of the two signals can realize the function of the frequency tracking automatically.

(8)

the quality factor is QLP = C2D3ωLP

(9)

so the cut-off frequency is fLP = (VxR71/2)/[2π (C1C2D1D2R5)1/2]

3.1

(7)

Design of voltage-controlled second-order low-pass filter

(10)

When taking C1  =  C2  =  C, D0  =  D1  =  D2  =  D, R5 = R7, it can deduce

The second-order voltage-controlled filter circuit is achieved by using the analog multiplier and introducing into bi-quad loop filter circuit model, its circuit is shown in Figure  4. The addition of damping integrator composed by D0, D1, D3, C2 and U2 plays a role of automatic compensation. The inverting integrator composed by D2, C1 and U5 is used as the linear modulator to complete the pulse width demodulation. The inverting input closed-loop operational amplifier composed by

fLP = Vx/2πDC

(11)

The formula (1) is substituted into formula (11), it can obtain fLP = fx/(8 × 106 × 2πDC)

(12)

It can be seen from (12) the cut-off frequency of the filter designed will change linearly with input

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Figure 5. The experimental and theoretical values comparison of the amplitude-frequency responses of the voltagecontrolled second-order low-pass filter.

frequency as long as adjusting the value of capacitance and resistance reasonably. 3.2

Table 2. The comparison of the measured and theoretical values of the cut-off frequency of the second-order low-pass filter. f0/KHz

The experimental result

In the actual measurement, when taking C1 = C2 = C = 100pf, D0 = D1 = D2 = D = 200Ω, D3 = 115Ω, R5  =  R7  =  500Ω, then according to formula (12), it can deduce fLP  =  fx, fx is the frequency signal after fin amplifying, amplitude-limiting and shaping, so fLP  =  fx  =  fin, that is, it realizes the cut-off frequency to track the input frequency. From formula (11), it can deduce the cut-off frequency of filter is fLP = 7.9577 × 106Vx. The amplitude of the input signal is taken 1.0 V, the control voltage Vx are taken 0.1 V, 0.4V, 0.7 V, 1.0 V, for physical testing, the actual measured corresponding cut-off frequency are 796.2 KHz, 3184.7 KHz, 5573.3 KHz, 7961.8  KHz, the experimental and theoretical values comparison of the amplitude-frequency responses is shown in Figure 5. From Figure  5, it can be seen that when the second-order voltage-controlled low-pass filter taking different control voltage Vx, the cut-off frequency will realize to change and track the input signal. The advantages of this method are wide range of adjusting and tracking and high precision. Detailed error analysis is shown in Table 2.

Vx/V

Measured

Calculated

Relative error δ (%)

0.1 0.4 0.7 1.0

796.20 3184.70 5573.30 7961.80

795.77 3183.08 5570.39 7957.70

0.054 0.051 0.052 0.051

4

CONCLUSION

This paper describes a method that firstly uses FPGA to measure the frequency of input signal, and uses the special method to control D/A to convert the measured frequency into the corresponding voltage, then inputs this voltage signal into analog multiplier which is a filter unit to control the cut-off frequency of the filter indirectly. Based on the proposed design method, we design self-tracking second-order low-pass filter circuit, the input signal frequency ranges up to 8 MHz to ensure the cut-off frequency of the filter can track automatically the input signal. The experimental result matches well with the theoretical value,

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which can prove the correctness and effective of circuit design. The structure of circuit is simple and conveniently controlled. And the filter system has an ability of fast tracking input signal change, and good real-time dynamic characteristics. It can be widely used in all aspects of the signal processing.

Jiang. Z.P. 2010. Design and implementation of FM exciter. Communication technology 9: 50–53. Li. X.P, Xu. J, Li. J. 2006. The principle and implementation method of the voltage-controlled filter. Modern Manufacturing Engineering 10: 114–115. Ma. S.Q, Ran. X.P, Fan. M.H. 2013. The design of the self-adaptive low-pass filter. Piezoelectrics & Acoustooptics 35(2): 245–249. Rezaei. F, Azhari. S.J. 2011. Ultra low voltage, high performance operational transconductance amplifier and its application in a tunable Gm-C filter. Microelectronics Journal 42: 827–836. Shi. X.C, Liu. S.L, Yu. M. 2010. The design of the antialiasing filter using the monolithic integrated active filter chip. Automation and instrumentation 3: 10–11. Tao. L.M, Li. Y, Wen. X.S. 2007. Signal tracking filter method based on technology of switched capacitor and its application in Rotor Balancing. China Mechanical Engineering 18(4): 427–430. Yang. Z.M. 1999. A basic method of the circuit based on the operational amplifier converted into the circuit based on CFA. Journal of Northwest normal university (natural science edition) 35(2): 31–38. Yu. W.W, Yan. D.T, Yang. Y. 2008. The engineering design and application of the automatic tracking filter based on MAX260. Modern electronic technology 31(5): 76–78.

ACKNOWLEDGEMENT In this paper, the research work was sponsored by the Natural Science Foundation of China (61162017), Department of Education-fund projects in Gansu (1101–03) and Northwest Normal University NWNU-LKQN-13-16. REFERENCES Guo. C.P, Ni. W.Q. 2013. The stability analysis of voltage-controlled voltage source filter. Journal of Luoyang Normal University 32(5): 32–34. Horng. J.W, Hou. C.L. 2005. Voltage-mode universal biquadratic filters with one input and five outputs using MOCCIIs. Computers and Electrical Engineering 31: 190–202.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Departure capacity assessment of close staggered parallel runways J.G. Kong, X. Li & W.B. Ding School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan, China

ABSTRACT: First, this article analyzes the staggered layout of closely spaced parallel staggered runways, its advantages compared to the traditional closely spaced parallel runway system and operating mode of the departure of aircraft. According to its operational characteristics, this paper learns from the departure capacity calculation model under mixed operation mode of single runway to establish the departure capacity calculation model of closely spaced parallel staggered runways. Then, it takes a group of closely spaced parallel staggered runways of Shanghai Pudong airport which consists of runway 1 and runway 3 as example, collects the relevant operational data, and uses the established model to do the data simulation to obtain the departure capacity of this parallel runway system. Keywords: 1

closely spaced parallel runway; staggered; departure capacity one runway for departure, the other runway for arrival, to establish a correlation between departure and arrival safety interval. The implementation of this way of running parallel runways called closely spaced parallel runway, referred to closely runway. In a certain mode of operation, flight procedures, air traffic control and communications and navigation equipment conditions, closely spaced parallel runway capacity is much larger than a single runway, but the land used for significantly less than the distance parallel runways. Accordance with the provisions of , closely runway’s operation mode only with one arrival one departure, and Article 43 of also made clear that, The two aircraft use the parallel runways which centerline intervals less than 760 meters should be equipped with wake turbulence separation. However, due to geographical constraints, or the flight procedure design considerations, many closely spaced parallel runways is designed to be the threshold of the two runways staggered, We call this system of closely spaced parallel runways is close staggered spaced parallel runway[1]. Runways threshold staggered can be achieved in three ways: the runway threshold physical staggered; threshold displace; and combination of both. Research indicates that runway threshold have to stagger at least 300 m to significantly enhance closely runway capacity[2]. However, completely physical staggered will increase the amount of land use, and when the runway system uses the most common along with one arrival and one departure, with the running direction of the change, takeoff and landing

INTRODUCTION

At the beginning of the 1970s, the USA and Europe began to raise closely spaced parallel runways technology. At present, the rapid development of the civil aviation market which makes the construction of airports and other infrastructure needs a lot of land resources, but because of limited land resources, national efforts to control the basic construction land get more and more, making building large spaced parallel runway system economy and social costs unbearable. Therefore, there is a need in the finite field area within, to achieve the maximization of the runway capacity, improve the operational capability of the airport, only through the design different layouts runway system to achieve. In the country, in March 2008, Pudong Airport closed runway operation mode enabled, becoming China’s first implementation of the operating mode of the close-up runway airport. In March 2010, the Shanghai Hongqiao International Airport was officially opened a second runway, formed a group spacing of 365  meters with the original runway closely spaced parallel runways. 2

CLOSE STAGGERED PARALLEL RUNWAYS OPERATING CHARACTERISTIC

Closely spaced parallel runway is a combination of running parallel runways. Reference to FAA regulations and some European countries, the definition of closely runway is less than 760 meters to the distance between the two parallel runway centerline,

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can learn from the capacity calculation method of single runway under the mixed operation. At first, we discuss the interval requirements of two arrival aircraft when insert the departure aircraft. Suppose that: 1. Lδ is the spacing of close staggered parallel runways, Lγ is the departure runway’s threshold displace; p 2. Vi A is arrival aircraft Fi final approach speed, Ti A is the time reach the approach runway’s threshold; 3. V j A is arrival aircraft Fj final approach speed, T j A is the time reach the approach runway’s threshold; 4. RT T j A is arrival aircraft Fi occupied runway time; 5. L is the limit distance which Fj away from approach runway when departure aircraft take off; 6. Ti D is departure aircraft occupied runway time; 7. Ti j A is the time interval which can inserted departure aircraft between two consecutive arrival aircraft.

Figure  1. The operation mode of one arrival and one departure.

runway must be interchanged. Thus, in the nonprimary landing direction will inevitably lead to outside takeoff and inside landing, it’s unreasonable for the safe operation, runway capacity and infrastructure investment. Therefore, in the design of close staggered parallel runway system, generally use the runway threshold displace or combination of physical staggered and threshold displace, able to deliver the promised runway capacity, saving the runway construction land and to maintain safe and efficient operation. In accordance with ICAO’s current air traffic control rules, the closely spaced parallel runway, regardless of how much runway spacing and how far away the runway threshold staggered are not allowed to implement parallel instrument approach. Appropriate threshold staggered to help the closely spaced parallel runway parallel approach, and thus significantly increase the runway capacity. This article focuses on the close staggered parallel runways departure capacity under the operation mode of one arrival one departure. Between two arrival aircraft, departure aircraft to leave the field under the appropriate security conditions, shown in Figure 1. 3

p When departure aircraft need to take off, time interval Ti j A is calculated as follows: Ti j A

T j A − Ti A = RT Tj A

L /V j A

( N − 1) × RT Ti D

where N is the number of departure aircraft could take off, it is obviously that when Ti j A > RT T j A ( N − ) × RT Ti D , departure aircraft can take off. When departure aircraft take off, arrival aircraft need maintain a certain time interval with departure aircraft, which to make ensure that the two runways at the same time only one aircraft. According to the actual situation of the relevant rules of the air traffic control and airports, we can set a safe p interval value Ts , and T j AD is the actual time interval between arrival aircraft and departure aircraft, only T j AD Ts departure aircraft could take off, or stand by. This could be calculated as follows:

DEPARTURE CAPACITY ASSESSMENT MODEL OF CLOSE STAGGERED PARALLEL RUNWAYS

First analyze the characteristics of the runway system before in the assessment of close staggered parallel runways departure capacity[3]:

T j AD =

1. The priority of the arrival aircraft; 2. At the same time, only one aircraft on the two runway; 3. Arrival aircraft from the runway threshold time is less than Ts, the departure aircraft have to hold; 4. After arrival aircraft landing, departure aircraft on another runway can take-off.

(

L

L +L

Vj A

)

According to the above discussion, departure interval of departure aircraft decided by miniTi j D , departure mum safety clearance interval minT aircraft occupied runway time RT Ti D and arrival A . Departure aircraft occupied time ( j AD i ) interval:

In the current operating conditions[4], the flow of arrival aircraft and departure aircraft is related in close staggered parallel runway, so close staggered parallel runway’s departure capacity calculation

Ti j D

ρ)MAX M (min Ti j D , RT Ti D ) + ρ ⎡⎣MAX (min Ti j D , RT Ti D ) + (T j AD (

RT Ti A ) ⎤⎦

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where Ti j D the departure interval of two consecutive arrival aircraft is, ρ is the proportion of departure aircraft cannot take off due arrival aircraft, which can obtained by historical statistical data. Fij D is the combination model of two consecutive departure aircraft, so the average time interval of consecutive departure aircraft on the close staggered parallel runways could calculate as follows: n

E [Ti j D ] = ( +

aircraft in actual operation, the capacity of the runway system reaches the maximum, and N = 1, ρ = 0.5 [6]. Based on the collected data, we can calculate the departure capacity of close staggered parallel runways is 31.8 flights per hour, average departure interval is 113.2 s. 5

ρ)∑ ∑ Fij D M MAX (min Ti j D , RT Ti D )

i =1 j =1 n n ρ Fij D i =1 j =1

∑∑

At first, this article analyzes the structure and operational characteristics of close staggered parallel the runways, then uses the calculation model of mixed operation mode of the single runway, established the departure capacity calculation model of close staggered parallel runways and collect actual data validated the model. Through this practice, I hope to provide some useful reference for close staggered parallel runways fully utilized and reduce flight delays.

D D ⎡M ⎣MAX (min Ti j , RTi )

+ (T j AD

RTi A )⎤⎦

And departure capacity of close staggered parallel runways CCL is: CCL =

4

EPILOGUE

n

1 E [Ti j D ]

REFERENCES [1] Guo Hai-qi, Zhu Jin-fu. Calculation models of capacity and delay for closely spaced parallel runway [J]. Journal of Traffic and Transportation Engineering, 2008, 8 (4): 68–72. [2] Wang Wei, Wang Mei-ling, Qian Xue-bing. Study on Method of Determination of Centrally Distance and Threshold Staggering Manner for Closely Spaced Parallel Runways of Airport [J]. Journal of Civil Aviation University of China, 2011, 29 (2): 23–26. [3] Richard de Neufville, Amedeo Odoni. Airport Systems: Planning Design, and Management [R]. Beijing; China civil Aviation Press, 2006. [4] Milan Janic. A Model of the Ultimate Capacity of Dual Dependent Parallel Runways [R]. Delft: OTB Research Institute Technical University of Delft: 2006. [5] Hu Ming-hua, Liu Song, Su Lan-gen. Research of Airport Capacity Estimation Model Based on Statistic Analysis [J]. Journal of Data Acquisition & Processing, 2000,15 (1): 74–77. [6] Wang Wei, Li Wei. Airport Capacity Calculation of Closely Spaced Parallel Runway under Operation Mode of One Arrival and One Departure [J]. Journal of Civil Aviation University of China, 2009, 27 (3): 20–22.

DEPARTURE CAPACITY ASSESSMENT OF SHANGHAI PUDONG INTERNATIONAL AIRPORT

The spacing between the 1st and the 3rd runway in Shanghai Pudong International Airport is 450 m, the Stagger distance is 600  m. According to the <2008 Pudong Airport demand forecasting and resource allocation Technical Report>, the proportion of heavy aircraft (FH) is 46.5%, moderate aircraft (FM) is 53.5%. Operation of aircraft model is shown in Table 1. We suppose the final approach speed of heavy aircraft is 150 knot, occupied runway time is 60 s; the final approach speed of moderate aircraft is 130 knot, occupied runway time is 50 s. The limit distance which arrival aircraft from approach runway when departure aircraft take off is 2 nm[5], in closely spaced parallel runways, the minimum wake turbulence separation between two departure aircraft is 2 minute. Generally cleared one departure aircraft takeoff between two consecutive arrival Table 1. Operation of aircraft model in Pudong Airport. Aircraft model

Proportion

Proceed: FH; after: FH Proceed: FH; after: FM Proceed: FM after: FH Proceed: FM after: FM

21.62% 24.88% 24.88% 28.62%

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The control design of blast furnace clay gun play mud quantity B.H. Jiang, J. Mei, X. Zhao, D.C. Gao & H. Liu Institute of Electrical and New Energy, China Three Gorges University, Yichang, Hubei, China

ABSTRACT: The hydraulic clay gum is a key equipment of blast furnace smelting. This article mainly describes the control system design of blast furnace clay gun play mud quantity. It analyses the error of clay gun and gives a method to reduce the error which can reduce the loss of engineering effectively. In addition, it also briefly describes the hydraulic system and the detection device of play mud quantity. Then the design and implementation of the relevant hardware and software are discussed in detail and the interrupt subroutine is given. The result indicates that it improves the operational reliability and fulfills the production requirement continuing to strengthen in industry. Keywords: 1

clay gun; blast furnace; hydraulic system; play mud quantity

INTRODUCTION

With the continuous development of the iron and steel enterprise, the higher requirements are put forward in view of the blast furnace. After the iron is come out when the blast furnace is opening every time, the amount of clay gun that is input along the channel of molten when the clay gun is blocking up the iron mouth will affect the iron mouth depth of blast furnace. When the blast furnace smelting system becomes more and more sophisticated, the corresponding precision of blast furnace clay gun play mud quantity is higher and higher. This makes the use of flow meter in the system of play mud an inevitable trend[1]. In order to accurately control the play mud quantity, the system of clay gun should install flow meter. It provides the operator with intuitive data and convenient operation, at the same time it can avoid unnecessary loss of clay gun[2]. Figure 1 displays the principle diagram of the hydraulic system. As is shown in Figure 1, when the iron mouth of blast furnace need to be plugged up, hydraulic proportional valve XZ3 power on, oil inputs through the A2 cavity and returns through the B2 cavity. The piston moves to the right driven by the pressure and the bod connected to the piston rod also moves to the right. Therefore clay gun can input the iron mouth of blast furnace through the mouth of clay gun. Flow sensor is installed in the return pipe channel because it can only be used in the single direction. When reverse flowing, it will result in the differential pressure of flow of the import and export increases. For a long time, it can cause damage of the flow sensor.

Figure 1.

2

The principle diagram of hydraulic system.

THE DETECTION DEVICE OF PLAY MUD QUANTITY

In the process of playing mud, as the route of the bod cannot be precisely measured in real-time, play mud quantity cannot be accurately detected. This detection system of mud amount is through the flow test device to test the hydraulic flow. Based on the principle of clay gun, as is shown in the literature[3], the actual route of the bod is: L=

V oil V = mud 2 π R 2oil π R mud

(1)

From equation (1): 2

Vm mud = Vooil ×

R mud 2

R oil

= Vooil ×

2 φ mud 2

φ oil

(2)

Due to the diameter of the mud cylinder and oil cylinder (φ mud φ oil ) of the clay gun is determined.

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When the hydraulic flow (Voil) in the process of playing mud is known, we can calculate the play mud quantity (Vmud) through equation (2). In the process of playing mud, there is some error between the play mud quantity of the calculation and the actual requirement. The ratio of K between the calculation and the actual requirement is set as 0.95 according to the experience. The play mud quantity of the calculation multiplied by the proportion K is equal to the actual play mud quantity. At the same time, the proportion coefficient is saved in the PC so that you can change the actual play mud quantity by changing the proportion coefficient. 3

THE CHOICE OF SYSTEM CONTROLLER AND MODULE

In accordance with the control requirements of the hydraulic clay gun, we increase by 15%–20% spare capacity base on the I/O points of actual statistics to adjust and expand afterwards. At the same time, the input and output expansion units should be made full use of to improve the utilization rate of the host. Thus, the S7-200 CPU226 series PLC is adopted. The amount of mud of the system is a simulation, thus analog input and output module is used. This system uses the S7-200  series EM235 module[4]. Its resolution is 12 bit A/D converter and the current input range is 0–20 mA which can meet the requirement of system control. 4

Figure 2. amount.

Calculation flow chart of subroutine for mud

THE DESIGN OF THE SYSTEM PROGRAM

In the process of playing mud of blast furnace hydraulic system, the play mud quantity is the key part. In view of the play mud quantity, we design the subroutine of the play mud quantity as shown in Figure 2. The system begin to start and initialization, reset and assign a default value in the register VD200. At the same time, the total amount of mud of mud cylinder is put in the register VD100. When the rotated gun turns the position and the play mud handles start then the system start playing mud. As is shown in Figure 2, every 0.2 seconds regularly get flow and put it in the VD118 register. The each flow is added up through the accumulator then the accumulative value multiplied by proportion coefficient K and stored in the VD110 register. Comparing the value in VD110 register with the present value, if the accumulated value is less than the present value, continued to play mud until the accumulated value is more than the present value. The injection of clay gun is stopped and gets into

Figure  3. Ladder diagram of reading flow for timer interrupt.

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be reduced and the mud is more close which greatly reduces the error of play mud quantity.

the holding state. In the measurement, what we get is just the oil quantity (Voil) in the process of playing gun, and then through equation (2) we can get the play mud quantity (Vmud). After playing mud each time, each of the accumulative amount of mud is accumulated by accumulator and the total of accumulated mud volume is stored in the VD210 register. The amount of mud of mud cylinder is known, when the residual amount of mud (that is the total amount of mud minus the mud volume which has played in the VD210 register) only 10% of the total amount of mud, then an alarm signal is created. In this article, the taking of flow regularly uses interrupt timing instead of a timer. If the timer is used, the execution of the program is limited by the bondage of the scan. But the interrupt timing is different, as long as the interrupt time comes then execute the interrupt subroutine which can enable the timing more accurate. Timer interrupt subroutine is shown in Figure 3.

6

CONCLUSIONS

The control system adopts Siemens S7-200  series programmable controller. The control design of play mud quantity detection in the hydraulic system of clay gun blast furnace is completed. It greatly improves the accuracy of the clay gun play mud quantity and the ability of quantitative control. It keeps the depth of the iron notch at the best depth. In addition, it provides corresponding guarantee to realize the stable production and high yield of blast furnace. At the same time, it can improve the using efficiency of clay gun and generate huge economic benefits. The system also has the warning function of flow which is conductive to the stability of the system. REFERENCES

5

THE ERROR ANALYSIS AND MEASURES OF PLAY MUD QUANTITY

[1] Liu Libing, Zhou Xuebing, Gao Jingbo, Wang Baohu. The improvement and perfection of the system of Hangang 3200 m3 blast furnace clay gun [J]. China’s new technology and new products, 2013(2):7–7. [2] Sun Jiarong, Kong Ju. The research and application of digital play mud quantity detection technology in the operation of blocking up iron notch [C]. Shangdong iron making proceedings of academic exchange, 2009(9):182–183. [3] Yu Chenglong. Blast furnace clay gun play mud quantity indicating device improvement and application [J]. Electronic test, 2013(4):121–122. [4] Xiao Junming, Zhang Rui, Jiao Lingyun, Zhu Haiming. The application of S7-200 PLC in the temperature control system [J]. Journal of zhongyuan institute of technology, 2010(6):13–15. [5] Lu Shikui. Reasons for the Difficulties in Clay Gunning in NO. 1 BF Taphole and Its Measures [J]. Bao-steel technology, 1996(2):1–3.

In the process of playing mud of blast furnace hydraulic system, the actual play mud quantity of iron notch will produce deviation because there is air gap and thus affect the effect of blocking up iron mouth[5]. The solution is: increasing the working oil pressure of rotated gun hydraulic cylinder to increase the force that presses the gun. The most effective and direct way to increase the working oil pressure of rotated gun hydraulic cylinder is to add a pressurization system to the hydraulic oil pump. When the hydraulic clay gun is within 0.5  meters from the tap hole position, the manual reversing valve of pressurization system is opened which leads to the increase in pressure of rotated gun hydraulic cylinder. At the moment, the speed of rotated gun is also accelerated, the air in the iron mouth will also

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research and implementation of coal traffic video management system based on the technology of image tracking and recognition S.J. Jin Mechanical Engineering College, Jilin Agricultural Science and Technology College, Jilin, China

ABSTRACT: This article introduces in detail the three components of video monitoring system of coal, by means of the target tracking technology and the effective integration of the license plate recognition technology, combined with the rotation of the PTZ function. At the same time, it realizes the clearer target image from different angles of scanning, improves the accuracy and rapidity of system identification; it is realistic and has a significant role in the reasonable exploitation and utilization of coal resources. Keywords: 1

target tracking; image identification; vehicle monitoring system

SYSTEM HARDWARE INTRODUCTION

communication format, the system make the PC to read digital signals the arrival of vehicles or through the module of the write operation to send commands to lower computer. AC6652 is shown in Figure 2.

The system use the DVR—SV4125  industrial computer as the upper machine of this system, the computer for anti-interference ability is very strong, it can be implemented in a very bad environment normal work. Considering the system actual application environment, the whole video monitoring and control system hardware is mainly composed of four parts: vehicle arrive induction module, image acquisition module, PTZ control module, PC and so on. The system hardware structure is shown in Figure 1. 2

2.1

2.2

Isolating anti-interference module

Because the whole system working environment is bad, it is easy to bring an interference to input signal. In order to protect the normal signal of the scene, this module uses a terminal card AC140 on account of photoelectric isolation function. AC140 is shown in Figure 3. The connection of terminal card AC140: 1. K0–K7: corresponds to the eight relays where 0–7 normally open switch contact, any one K contact are two terminals that the corresponding to relay switch contact all the way.

THE MODULE OF VEHICLE ARRIVE INDUCTION HARDWARE DESIGN AND IMPLEMENTATION PCI switch plate

AC6652 is a low price general photoelectric isolating I/O board, with 16 road input, 16-way output. Through the adoption of PCI trunk so as to realizes supporting plug and play and no address jump line. At the same time, large-scale programmable gate array which is used to design for improving the reliability of system, AC6652  input support 5–24 V input, while output for the open collector output (OC output, output chip: 6N33 or TIL113), output drive current is greater than 30 ma, can easily drive miniature relays and the LED load. AC6652 uses CH series of PCI interface chip arrays as the master control chip. AC6652 PCI switch board has the function of the photoelectric isolation, It can receive signals from the AC140, and then through the module processing into a PCI

Figure 1.

The system hardware structure.

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is less affected by environmental factors, so, more stable; this system uses a magnetic sensor. Geomagnetic sensor is used when the vehicle through earth’s magnetic field to realize the detection of vehicles, because the earth’s magnetic field can basically constant in a few miles, large ferromagnetism objects will produce very large perturbation to the earth’s magnetic field. Geomagnetic sensor can distinguish the earth’s magnetic field 1/6000 with slight changes, when the vehicles pass through the road, the impact of geomagnetic can reach a fraction of geomagnetic intensity. To sum up, using the geomagnetic sensor to detect the vehicle have a sensitive enough. Figure 2. AC6652 switch plate.

3 3.1

Figure 3.

T Video acquisition card (Tian min 4000 Video acquisition card)

The video acquisition card is used to receive the video input analog video signal In PC, so as to realize the collection and quantitative for the signal, and then the compression and coding become digital video. Because acquisition card has the hardware compression, so in the acquisition of video signal, it must be compressed firstly, and then through the PCI interface of the compressed video data transmitted to the host. Tian min 4000 Video acquisition card is shown in Figure 4. Ordinary PC video acquisition card mainly adopts frame compression algorithm to the digital video files into AVI files, high-end video acquisition card still can more directly the collected digital video data real-time compression into mpeg-1 format file. Because the analog video input has the function of providing uninterrupted source, so acquisition card has to gather any frames in simulate video sequences, and before going to collect the next frame image you should put the data file to

Isolating anti-interference module (AC140).

2. DI0–DI7: corresponds to the eight road photoelectric isolation input of 0–7. 3. The GND: photoelectric isolation of input ground, namely DI0–DI7 ground. 4. POWER: corresponds to the +12 V power input, and the power supply cathode and photoelectric isolated input ground GND is isolated from each other. 2.3

IMAGE ACQUISITION MODULE

Geomagnetic sensor

Accurately induction the vehicle arrival is the first condition of the video monitor system, there are many ways such as infrared, ultrasound and magnetic induction, etc. Ultrasonic sensor is easily affected by the environment: when the wind speed is above level 6, it is due to the reflection wave produced drift and therefore unable to realize normal detection; at the same time, people or objects which in the bottom of the probe will also produces reflected wave as form error checking. In addition, the infrared sensor which is due to ice fog, dust, etc in the job site, the normal work of the system will also is affected. Geomagnetic signal

Figure 4.

Tian min 4000 Video acquisition card.

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index are mainly considered in the choice of slewing range, bearing capacity and rotational speed. General PTZ all belong to cable control electric.

PC system. Therefore, key steps which in realizing the collection of real time is the processing time required for each frame. If more than the interval of time between two adjacent frames, so the data file will be lost, or to say frame Loss. So when acquired the video sequence by acquisition card, it is necessary to compress firstly, and then deposited in the hard disk, namely the acquisition and compression is always together, there is no need for video sequence to compress again. Different acquisition card collection is endowed with different compression quality. 3.2

4.2

The selection and characteristics of the camera

This system adopts the Sony company EVI-D30 color camera. Its main functions: automatic Tracking. it through the camera to constantly automatic obtain the user predefined themes. Around the choice of the theme, it will gather the similar pixel color and brightness. Based on optical reverse principle and nonlinear reverse camera processing and predefined themes which including the automatic tracking function, automatic focusing, automatic exposure, motion detection, so as to select a goal. 4 4.1

PTZ equipment installations

PTZ is usually associated with the camera which by providing extra lines to make PTZ equipment and the computer joining together. This circuit is often connect with the computer in the form of a COM port, On the other side, PTZ control of converter, as shown in Figure 5. PTZ control converter is connected to the computer in the form of COM port, Figure  6  shows the connection between PTZ control converter and the PC. 4.3

PTZ control analysis

Through serial communication, it realizes the communication between computer and PTZ.

PTZ CONTROL MODULE PTZ introduction

PTZ is the support equipment which has a fixed camera function, it is divided into two types: fixed and electric. Fixed PTZ: it mainly used in small size range of monitoring, when using, it is bound to fix the camera in PTZ and adjust the angle of the camera level and pitch, make its best work attitude, finally lock adjusting mechanism. If you need to scan and monitoring a wide range when electric PTZ is used. Because it can expand the camera monitoring field of vision, improve the actual use value of the camera. According to the characteristics of the rotary, it can be divided into: horizontal rotating around (rotating) and all-round (rotate and can up and down or so). It turns to level 0–350 degree angle, pitch angle of 0–90  degrees, some of them can also automatically patrol. Electric PTZ posture adjustment mainly through two force motor which by accepting the controller signal to implement the accurate operation positioning to realize the rotation. Through controlling signal, camera can automatically scan on the area of monitoring, and can through to the monitoring center on duty personnel to implement tracking monitoring object manipulation. From using environment, it also can be divided into: indoor and outdoor. The PTZ

Figure 5.

PTZ control converter.

Figure 6. Connection diagram of the converter and the upper machine.

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its working principle, the performance is specifically presented and analyzed.

PC provides only standard RS-232 interface, then PTZ provides industrial standard RS-485  interface, so it requires a RS232-485 converter to RS-232  signals converted into RS-485 between them. And PTZ is able to implement connected with the computer serial port through the decoders and RS232-485 converter, which is controlled by procedure in the form of sending commands to PTZ. These instructions are determined by PTZ control protocol, so the manufacturer is different, PTZ control protocol is also different. This system adopted the protocol PELCOD2400. 5

REFERENCES [1] Jialei Cui. 2008. the research between FY-1 receiving system timing synchronization with PCI interface, xi ’an university of electronic science and technology, xi ’an. [2] Sony EVI-D30/EVI-D31 Operating Instructions, Sony Corporation, 2001. [3] Shi jia jin, 2010. Research and application of image identification and tracking technology in video management system of coal transportation, North-East university master degree theses, Shenyang. [4] Suliang Liu et al. 2006. Based on VFW video application development, northwestern university institute of visualization, 45(5):10–15. [5] Xiaojing Liu & Yu Cheng, 2003. Car license plate automatic identification technology research, journal of nan jing university of aeronautics and astronautics, 30(5):573–576. [6] Xuqiang zhan et  al. 2001. Fast algorithm based on MPEG-2 video target tracking, journal of Shanghai jiao tong university, 35(9):1321–1324.

SUMMARY

This article analyzes and designs the hardware of the whole video management system. Considering the application environment of the whole system, the hardware structure of system can be divided into the following three parts, respectively: vehicle arrives induction part, image acquisition, PTZ control part. And each part of the hardware and

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Residents air conditioning load management model studies and impact analysis B. Li & S.W. Li Beijing Guodiantong Network Technology Co. Ltd., Beijing, China

S.X. Zhang State Grid Information and Telecommunication Branch, Beijing, China

X.S. Jing, F. Wu & X.J. Weng Beijing Guodiantong Network Technology Co. Ltd., Beijing, China

ABSTRACT: With the growth of the national economy and the people has improved continuously, the electricity load to maintain a faster, higher growth, especially air conditioning load growth on the impact of the economic operation of power grids, power supply and demand balance is growing. Based city of intelligent air-conditioning load management model studies and impact analysis is the main research contents of this paper, this paper focuses a comprehensive set to meet the city air conditioning load regulation and energy management mode and control means, and its widespread implementation of urban energy management and energy saving effect. The article describes the status and trends of the domestic airconditioning load growth, domestic and foreign air conditioning load management and energy conservation case studies of air conditioning load control mode and means of summary, and finally elaborated promote the application of far-reaching impact. Keywords: 1

load regulation; energy conservation; air conditioning load

INTRODUCTION

tions, Beijing grid will face the problem of power shortage. Harbin July 27 sustained hot weather caused a sudden increase in local residents air conditioning electricity consumption, so that the local power supply reached 40,734,000 KWH, compared with the same date in 2010 of 23.5% growth in electricity demand, the highest value. Due to the high power air conditioners and other appliances in use, resulting grid load appears short load “peak”, which brings transformers and power lines overload and overload problems in terms of power equipment; need emergency personnel at work mobilize the majority of substation operator to closely monitor the situation daily power load growth, increasing the frequency of inspection equipment, heavy duty power line contacts for strengthening monitoring; serious impact on the city safe and reliable power supply. Meanwhile, in terms of grid construction load for a short period of “peak” need to invest in large-capacity transformers, electric peak load distributor and other antihypertensive equipment, causing power resources severely strained.

With the enlargement of city scale, the improving of the total urban residents, city industrial and commercial the most efficient and comfortable residence requirements has improved energy application requirements for urban energy supply pressure, especially during the peak of the high power load to the urban power grid load carrying ability put forward higher requirements, how to coordinate and control city energy load pressure, ensure stable operation of city power grid, improve urban effect is the problem of the current must pay attention to energy saving and emission reduction. According to statistics, Beijing July 14, 2011 power grid load between 18.5 to 19.1 million kilowatts, an increase compared to the same period in 2010 of 11.04%∼14.64%, of which about 40% of air-conditioning cooling load. Beijing area total power generation of 400  million kilowatts, about 78% of the electricity needed to send power plants rely on electricity supply outside Beijing, during the summer period, in the lack of an external power supply capacity or extreme weather condi-

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2

to the survey results show that if the current set of air conditioning room temperature 2 °C more than the summer, the peak power can reduce air conditioning load of 10% to 15%, namely cut about 50–75  million KW power peak capacity requirements, at ease tensions in the electric power at the same time can save a large amount of electric power construction investment; About 2400–3500 tons of emissions of sulfur dioxide, carbon dioxide is about 40–60 million tons. By the calculation, if the summer air conditioning temperature raise 1 °C, air conditioning energy consumption can be reduced by 8%, only 4  million households in Beijing can save about 108 million KWH. Prove that, our country the present situation of the air conditioning load is air conditioning load absolute value growing every year, gradually become the city constitute the key influencing factors of high load pressure, if not for effective control and management in the future, will cause the urban energy supply pressure is too large, reliability and reduce the energy use of the city.

DOMESTIC PRESENT SITUATION AND TREND OF AIR CONDITIONING LOAD

Based on the present situation of the air conditioning load, our first-tier cities (such as Beijing, Shanghai and Guangzhou) air conditioning load rapid growth, accounting for the proportion of the total urban load continues to increase. Growing air-conditioning load is mainly conditioned by the urban cool in summer and winter air conditioning cooling load heating load composition. As the city air conditioning gradually into the household, commercial and industrial buildings, causing rapid expansion of the application of air conditioning equipment, its concentrated in summer and winter peak use, and promote the rapid rise in the city’s total load. Beijing, for example, the proportion of Beijing’s air conditioning electricity demand increased every year. In 2000–2004, for example, the maximum power load in summer continues to rise, from 676.8 KW to 9.5 million KW in 2000, the air conditioning load ratio rose from 36.94% to 45%. In recent years, the Beijing area’s air conditioning ownership from 2 sets/hundred households in 2001 rose to 22 sets/hundred households in 2011. By 2012 the Beijing air conditioning has reached 13 million units. Residents take conditioned average power 1.3  KW, while the coefficient 0.5, the load rate of 0.6 (the value of the experience gained by the general air conditioning running), you can get the 2012 Beijing air conditioning load which is 5070 MW. In Shanghai, for example, Shanghai electric power load is the cause of the sustained and rapid economic development and people’s living standards continue to improve and to buy cooling products, caused by air conditioning load is a major cause of electric power peak load to produce; The temperature is damage the final determinant of power load. According to statistics analysis, the Shanghai summer air temperature at 30 °C above, the temperature rise per 1 °C, about 240000  KW load increases. But for days above 35 °C high temperature, the heat accumulation of household, make the lowest temperature at night will rise to more than 27 °C, the growth of the load and speed will be faster. In smart grid as an example, according Table 1.

3

DOMESTIC AIR CONDITIONING LOAD MANAGEMENT APPLICATION CASES

To verify the residents and commercial users of air conditioning load as the feasibility of interruptible load, the National Development and Reform Commission commissioned by the State Grid Corporation of China to carry out research, November 2011 Guo dian Tong organizations State

Figure 1. Networking diagram of the air conditioning load empirical topics.

Centralized control platform monitoring data.

Category/date

2012 06

2012 07

2012 08

2012 09

2012 10

2012 11

2012 12

2013 01

2013 02

Visual group Control group Base group

15.53 14.59 15.11

11.97 12.86 14.12

13.68 8.95 10.84

15.27 15.21 16.57

7.3 7.51 7.83

18.66 18.05 18.23

8.36 8.59 8.22

20.79 21.98 20.93

19.44 21.2 20.6

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docking with electricity information acquisition system, copy the user home electric meter data, achieve air conditioning load management implementation and monitoring of detailed data through centralized control monitoring platform. According to the data obtained from monitoring management platform of empirical analysis platform on June 2012 to March 2013, as listed in Table 1. Use group in the peak (specified group peak hours electricity/designated group, the total electricity consumption (calculated monthly cycle)) when the power load control effect evaluation, the group focused on the regulation of 6,8 months of 2012, according to data analysis for June monitoring platform of the air conditioning load control can effectively reduce the air conditioning load ratio of peak load, through June and August compared to control frequency and control status directly affect the depth of peak load (as shown in Fig. 2). In August 31, 2012 air conditioning control, for example, three strategies for control group were used respectively to control air conditioning in the home. Table 2 to carry out close operation control group, the change of the air conditioning load, it can be seen after regulation was implemented, the air conditioning load of air conditioning control group overall decline, the air conditioning load regulation time have obvious rising trend.

Grid Electric Power Research Institute, joint Beijing Electric Power Company, Shanghai Electric Power Company, Jiangxi Power Company, Chongqing Electric Power Corporation and the Ningxia Electric Power Company, relying on the already built four intelligent village, three intelligent building air conditioning load empirical research work. 3.1

Analysis of load control effect

Project using mature intelligent electrical products (home gateway, intelligent electrical outlet, etc.) and intelligent control user air conditioning technology for electricity, using electric power fiber to the home technology to realize the application platform of network architecture (Fig. 1). Participating in the project to install 100 residents in intelligent interactive terminals, smart home gateway, smart sockets and other equipment acquisition and power consumption of air conditioning, according to the subordinate transformer or become benchmark users can be divided into groups and utilization, visualization and air conditioning control group, by

3.2

Figure 2. Power consumption comparison group were peaks when analyzing data.

Table 2.

Air conditioning load data analysis

According to the data in Table 2, the control group using intelligent power device remote shut down air conditioning, during the period of peak power grid under the premise of good agreement with the user, more than 50% of the users meet the control requirements for 30 minutes, the user cooperation degree is higher.

Air conditioning load change.

Category

Unit

19:55

20:05

20:15

20:25

20:35

20:45

Control group Visual group Control group of air-conditioning closing rate

kW

31.16 31.03 –

17.78 28.50 95.44

20.60 27.71 67.22

22.80 28.65 55.67

27.39 26.34 41.56

31.10 30.19 27.15

Table 3.

%

Empirical project data.

City

Data entry

Unit

Base group

Visual group

Control group

Beijing

Maximum load Minimum load Valley-to-peak Load reduction rate

kW

39.53 4.85 34.68 –

37.46 4.83 32.63 6.82%

32.88 5.04 27.84 38.12%

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to achieve the goal of energy conservation and emissions reduction. 3. Residents to actively participate in energy load management strategies. Formulate relevant policies to encourage and guide the user to actively participate in electricity power clipping. Under the guidance of the government’s macroeconomic policy, government, energy-efficient appliances manufacturers and users consist of the three closed loop network, government policies and economic support, energy-saving appliances manufacturers to provide energy-saving technologies and appliances, residential customers use energy-efficient appliances and normal fees paid in the economic, material level achieved within the loop.

According to the data in Table  3, after the implementation of the regulation, control group of 40 families peak valley is 27.84 kW. Compared with the baseline group 34.68 kW and 32.63 kW of visual group have significant lower, load reduction rate of 38.12%. Project results show: the effect of air-conditioning load by implementing evidence-based regulation, the regulation can effectively reduce air conditioning load during peak hours power peaking pressure, improve power supply reliability and service levels, and help users build load shifting consciousness. Prompting power grid load rate to increase, hair power distribution equipment utilization to be improved, improvement in power grid operation state. In narrow peak valley electricity at the same time, reduce the line loss of power transmission and distribution network, improve the economic benefit of the power grid operation as a whole. 4

4.2

Residents air conditioning load management objectives

Air conditioning load management through the implementation of the air conditioning load management measures, from the viewpoint of power industry in our country to realize the sustainable development, not only can improve the efficiency of energy use, effectively reduce the future demand for energy, reducing energy consumption level, to achieve power and the coordinated development of environmental protection, is to achieve the development goal of building a moderately prosperous society in an all-round way, and is an important part of national energy strategy. For power grid at the same time, the effective demand side management measures can reduce the peak load and increase the low power consumption, achieve peak shift, balanced load and improve the effect of load curve, thereby increasing the stability of power system operation and reliability, guarantee efficient grid economic operation. Air conditioning load management is an important measure to ease the power shortage situation, improve the efficiency of electricity use, is a concrete manifestation of the scientific concept of development, the promotion of energy, economic, and environmental development of great significance.

RESIDENTS OF AIR CONDITIONING LOAD MANAGEMENT MODEL RESEARCH

4.1 Residents general idea of air conditioning load management With the rapid growth of air-conditioning load of the impact of the grid, the general idea of air conditioning load management is divided into three areas: 1. Strengthen macro-oriented government agencies. In the energy air conditioning load under the condition of serious damage to the power grid security, air-conditioning load management requires the active participation of the whole, put forward the corresponding policies and regulations, formulate policy guidance, under favorable circumstances of promoting technology mature energy-efficient appliances and intelligent home appliance. Meanwhile countries need to actively encourage the development of advanced energy-saving technology to achieve strategic policy in line with national development of advanced energy-saving technology. 2. Energy-efficient appliances manufacturer actively cooperate with the government of negative control strategy. The government needs to choose a home appliances with energy-saving technologies of home appliances manufacturers in line with the actual situation of load management strategy, by the implementation of load management strategy can effective promote the use of intelligent home appliances and help the government

4.3

Residents air conditioning load control and management methods

4.3.1 Residents air conditioning load control method The main reason for the air conditioning load caused by sudden changes in power curve is due to cooling and heating load required for residents, cooling and heating load is always in dynamic change, such as the outdoor environment and human status, real-time impact on the residents’

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management. for example, the Shanghai municipal government in the public electricity side control measures adopted for the peak air conditioning load on government buildings, shopping malls and hotels, such as the use of central air-conditioned places, take effective nodes to avoid the peak measures; in the civilian side of the home air conditioning production specifications, and guide the rational use of public air conditioning; use of energy-saving air-conditioning; formulate relevant policies to encourage and guide the user to actively participate in electricity clipping.

demand for air conditioning. General price guide using the air conditioning load management, load control management and other means, to achieve the load curve shape to be adjusted to achieve effectively reduce peak load electricity demand target. Air conditioning load management has a direct load control, load control and contract interrupt request interrupt load control three types. Direct load control, namely in the power system load curve peak load period of time, through electric power dispatching personnel issued instructions, electric power marketing personnel through to the construction of electric power communication network, to direct jurisdiction area air conditioning load and electricity at any time. Reduce peak load users typically involved large consumption of electricity or air conditioning load into urban neighborhoods. Contract interruptible load control, that means through incentives, according to the contract signed before, in the peak load curve segment, the air-conditioning load control systems by directly controlling the load dispatchers or after a direct request user, interrupt the power supply. Users is usually involved in contract the residents of the air conditioning users. Request interrupt load control, that is registered in the area of the load control of air conditioning use, through the information management, user interruption to the area air conditioning load control request operation, new users can implement the air conditioning load control operation to the negative control management, eventually to perform corresponding compensation mechanism for the user.

4.4

Residents air conditioning load management impacts

The use of air conditioning load management can reduce the high cost of peak load, delaying the construction of new peaking units, thereby saving total system operating costs, peak clipping reduced the peak period of purchasing electricity, while total operating expenses will also be reduced. According to the status constitutes a means of load and peak load shifting and other current analysis, the power company is still a lack of air conditioning load, especially household air conditioner load control means, the government has not yet associated with air-conditioning control policies. Therefore, through the establishment of a centralized air-conditioning load control monitoring platform will make up for the air-conditioning load control means peaking blank, promoting effectively reduce the air conditioning load, to enhance the effectiveness and level of demand side management. Thereby reducing the peak power grid companies to cope with the growing burden of infrastructure investment, to further improve the power grid load factor and energy efficiency, but also for the government to promote energy conservation, to deal with high energy consumption caused by environmental pollution and energy waste provides a new one kind control means in order to achieve the overall energy saving environment.

4.3.2 Residents air conditioning load management Under the guidance of a unified government, through the development of “air-conditioning load management approach” and other policy objectives, to achieve joint air conditioning manufacturers and air conditioning units of energysaving technologies, users of air conditioning for the promotion of energy-saving air-conditioning products. In the current market as a whole and residents under the condition of using air conditioning, air conditioning load management generally adopt specific measures as follows: Air conditioning load management policy measures; Encouraged to promote energy-saving air conditioning, intelligent air conditioning and new energy-saving air conditioning technology; encourage and guide the power user involved in the promotion of the use of air conditioning electricity clipping; through the government regulation and achieve tripartite joint research on air conditioning load management; Achieve the ultimate goal of air conditioning load

5

ECONOMIC ANALYSIS

From a macroeconomic perspective, the air conditioning load management in electric power market is one of the effective measures to alleviate the contradiction between supply and demand of electric power, but also an important part of electricity demand management. Load management can not only achieve peak shift and optimization of load curve, improve the utilization rate, but also bring corresponding economic benefits. Load management has great cost effectiveness, through the load management realize load saving

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management of economy, technology and administrative means, realize the effective management of air conditioning load, to achieve effective management of air conditioning load, which can effectively alleviate the shortage of electricity supply contradiction to ensure grid smooth and safe operation, but also to optimize the power consumption, improve energy efficiency, promotion of energy-saving appliances and expanding domestic economy. To build a conservation-minded society, achieve the optimal allocation of power resources and the sustainable development has a far-reaching influence. Supported by the National High Technology Research and Development Program of China (863 Program): 2011AA05A117.

benefit, realize load energy saving benefit at the same time. Potential savings are greater than the savings load efficiency, economic and environmental benefits more significant. Electricity load by load management optimization to improve the rate of electricity load, thereby reducing transmission and distribution losses, improve the operating efficiency of the transformer to improve power generation efficiency of the generator set. To enhance the efficiency of power generation, transmission and distribution means that the whole power system loss reduction. According to statistics, Beijing residents about 6.2  million subscribers for air conditioning, calculated according to full coverage cooperation degree is 25%, calculated is 1.55  million households, household air-conditioning to reduce peak load 1.89  W, according to a degree of 90  days calculated in summer, air conditioning regulation can reduce peak power load 265000 kW, save power optimization and secondary input costs 9.275  million Yuan, can reduce a 110  KV substation (in accordance with the reduction of a 110 KV substation, covering 3,000 square meters, construction area of 1,600  square meters for the reference), to reduce carbon dioxide emissions 264205  kg, it is conceivable by air conditioning load control management, energy can achieve the government’s macro-control, air conditioning manufacturers to achieve product sales, boosting domestic demand throughout the consumer electronics market space, at the same time to the people’s normal life and living environment has brought great benefits.

6

REFERENCES [1] Yin Shu-gang, Zhang Yu, Bai Ke-ming. Based on the real-time intelligent power consumption of electricity price system [J]. Power System Technology, 2009, 33(19): 11–16. [2] Zhang Zhi-qiang, et al. Its control measures based on the grid side of the air conditioning load characteristics [J]. [3] Tong shu-lin. Wen fu-shuan. Measurement and analysis of energy saving environment, Guangdong Province, the largest annual cooling load [J]. North China Electric Power University (Natural Science), 2010.05. [4] Wen quan, Li jing-ru, Zhao jing. Air conditioning load calculation methods and its application [J]; Demand Side Management 2005.04. [5] Cheng Yu, Zhang Li-zi. The co-integration analysis of power tariff and demand [J]. Proceedings of the CSEE, 2006, 26(7): 118–122. [6] Angel A. Aquino L., Ray K. A control framework for the smart grid for voltage support using agent-based technologies [J]. IEEE Trans on Smart Grid, 2011, 2(1): 161–168. [7] Pedrasa A., Spooner D., Macgill I.F. Coordinated scheduling of residential distributed energy resources to optimize smart home energy services [J]. IEEE Trans on Smart Grid, 2010, 2(1): 161–168. [8] Negenborn R., Houwing M., Schutterr D.B., et  al. Adaptive prediction model accuracy in the control of residential energy resources [C]. Proceedings of 2008 IEEE International Conference on Control Application. Pisa, Italy: IEEE, 2008: 311–316. [9] Haasr, Nakicenovic N., Ajanovic A., et  al. Towards sustainability of energy systems: aprimer on how to apply the concept of energy services to identify necessary trends and policies [J]. Energy Policy, 2008, 36(11): 4012–4021.

SUMMARY

Realize the peak load shifting of smart grid is air conditioning load management and user friendly interactive service base, is the government and power grid company in residential electricity side load control of the important means and key link. Through intelligent air conditioning load management realized the decline of the peak load of power grid, effectively reduce the power grid operation economic investment, greatly reducing the grid infrastructure investment and reduce energy secondary pollution from emissions. Therefore, attaching great importance to the intelligent air conditioning load to power grid security, stability, the influence of the economic operation, utilization of electric power demand side

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Multi-objective optimized scheduling for hydro-thermal power system W.J. Liu School of Electrical Engineering, Guangzhou College of South China University of Technology, Guangzhou, China

P.F. Cheng Power Company of Fushun, Fushun Liaoning Province, China

ABSTRACT: This paper puts forward a multi-objective optimization model which can make the coordination among economy, energy saving and environmental protection, in order to improve the combination property of hydro-thermal power system with cascade hydro plants. The multi-objective optimization problem is difficult to solve, on the one hand is because the targets are conflicting, on the other hand is because the weight coefficient of the target is difficult to determine, therefore, using the satisfaction function and Euclidean distance function to normalize the three targets, then using improved Particle Swarm Algorithm (DPSO) to optimize. An example with 8 cascade hydroelectric plants and 10 thermal plants was executed, the simulation results show that in scheduling period, the hydropower station affords larger power output, instead of coal-fired power, reducing the coal consumption and pollutant emissions. Keywords: 1

hydro-thermal power system; optimization scheduling; multi-objective; improved PSO cascade hydroelectric plants and 10 thermal plants was executed to verify the correctness of the models and the effectiveness of the arithmetic.

INTRODUCTION

Energy shortage needs the electric power industry improve efficiency, realizing the maximize utilization of resources. Only pursue power system economic dispatch already can’t satisfy the requirements, multi-objective optimization scheduling[1–3] become the current research hot spot. Conventional algorithms for processing high-dimensional multi-objective optimization of hydro-thermal have certain limitations, people turn to the intelligent optimization algorithms, such as genetic algorithm[4], particle swarm algorithm[5], because they have faster convergence performance and higher convergence precision. Standard PSO can effectively optimize the single objective problem, then people usually adopt weight coefficient, goal programming method or other method convert multi-objective problem into single objective problem to optimize. These processing methods need to have strong prior knowledge, cannot effectively response the actual operation situation. Considering various factors, this paper puts forward multi-objective model coordinate energy-saving, water resources and environmental protection. Using the satisfaction function and Euclidean distance function to normalize three objective function with different dimensions, avoiding artificial factors, and then adopt the improved PSO to optimize the goal after processing. An example with 8

2 2.1

MULTI-OBJECTIVE OPTIMIZATION MODEL Objectives

Here, we establish three objectives function, cost-minimized, emission-minimized and water consumption-minimized. ⎧ ⎪min f1 ⎪ ⎪ ⎪ ⎨min f2 ⎪ ⎪ ⎪min f 3 ⎪⎩

T NT

T NT

t =1 i =1 T NH

t 1 i =1

∑∑

∑ ∑ (α j PT,t,j2 + β j Pt j + γ j )

( Pt ,i ) ∑ ∑ fc (P

[ ai ( ∑ [a

t

2 ,i )

bi PTt ,i + ci ]

∑∑

WGt , j t =1 j =1 T NT E ( PT,t j ) t =1 i =1

T NT

t =1 i =1

(1) where t is time index, T is scheduling period; f1 is total coal consumption(t), f2 is total water consumption(m3), f3 is total pollutant emissions(t), t WG, j is the water consumption of hydropower station j at time t(m3); NH , NT is the number of hydropower station and thermal station.

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2.2

where i is the number of objective functions; ϕi(x) is the satisfaction function of the objective function i; fi ,opt p x ) is the optimum solution of objective i; fi ,inf x ) is the worst solution of objective i; structuring the Euclidean distance function as follows:

Constraints

1. System balance constraint NT

NH

i =1

j =1

∑ PTt ,i ∑ PHH,t j = PDt

PLt

(2)

t PH, j is generation level of hydropower plant j at time t (MW), PT,t i is loading of thermal plant i at time t (MW), PDt is the demand of the power system at time t (MW), PLt is total transmission line losses at time t (MW). 2. Hydro-thermal plant loading limits

⎪⎧PH, j ⎨ ⎩⎪PT,ii

PHt j ≤ PH, j PTt ,i

(j

a

≤ PT,i max a (

2

m

min

V1t −1 V jt −1

+ ( I1t + ( I tj

(j = ,3

Q1t − S1t ) t Qtj −−τ1 + S tj −τ1

)

n; t ∈T )

V jt ≤ V j max ( j

, 2,

, NH ; t T )

⎧vi j (t ) = w ⋅vvi j (t (t ) c1 ⋅ r1 [ pi j (t ) xi j (t )] ⎪ + c2 ⋅ r2 ⋅ [ pg, j (t ) − xi j (t )] ⎨ ⎪x (t + ) = x (t ) + v (t + ) i j i j ⎩ ij

(5)

5. Hydropower plant discharge limits t QG j ≤ QG , j

QG, j

(j

2

(6) 6. Hydropower plant spillage limits

3

(7)

MULTI-OBJECTIVE OPTIMIZATION ALGORITHM

3.1

Multi-objective processing w(t ) = wmax −

First, processing the single objective, and unify each objective to one standard with formula (8), then work out the satisfaction function of each objective.

ϕi (x ) =

fi x ) − fi ,opt x ) fi ,

x ) − fi ,opt x )

i 1, 2, …, m

(10)

where t is iteration, w is inertia factor, decide the search capabilities of particle; c1, c2 is learning factor, usually, c1 = c2  = 2, c1 is the learning ability of particle itself, c2 is the ability of particle exchange information with other particles; r1, r2 is random number between[0, 1]. For standard PSO easy to fall into local optimal solution and other defects, using the way of linear decrease inertia weight, then the algorithm with dynamic self-adaptive can converge to the optimal solution quickly[6]. Inertia weight linear decrease

NH ; t T )

0 ≤ S tj ≤ S tj max

(9)

In the standard PSO, particles’ flight rely on the velocity and displacement to update themselves in order to seek optimal solution. Assuming that there are M particles in the N dimensional space, the position of the i-th particle can be expressed as xi ( xi , , xi , xin ) , its speed can be expressed as vi (vi , , vi , vi ,n ), its speed and displacement update equation is:

(4)

V1t −1, V1t is the first reservoir volume at time t−1, t (m3); I1t is inflow of first reservoir during time t (m3); Q1t is water discharge of first reservoir at time t (m3/s); S1t is spillage of first reservoir at time t; j is the number about the rest reservoir; τ is water time delay. 4. Reservoir level limits Vj

2

3.2 The improved Particle Swarm Optimization algorithm (DPSO)

(t T ) − Qtj − S tj )Δt

ϕ i* ( x ) ⎤⎦

where d ( x ) is the constructed Euclid distance; ϕ i* ( x ) is the optimization (minimum) of each objective satisfaction function ϕ i ( x ) ; ϕ ( ) is the function of the optimum solution x which is under the constraint of all the objective formula. The distance between ϕ ( ) ϕ i* ( ) is smaller, indicating that they are more close to the optimization.

(3) 3. The constraints of cascade hydro plants water balance ⎧V1t ⎪ t ⎨V j ⎪ ⎩

x)) ∑ ⎡⎣ϕ ((x) i =1

NH ; t T )

, 2, ...,, NT ;

(x)

t (wmax wmin ) tmax

(11)

where wmax is maximum of w, wmin is minimum of w, usually, wmax  = 0.9, wmin = 0.4; t is current iteration; w(t ) is the inertia weight when the number of iteration is t, tmax is the maximum number of iteration.

(8)

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and hydropower stations parameters are shown in Table 1, 2, 3. The scheduling time is 24 hours. Under the condition of satisfying all the constraints and system load balancing, using improved PSO algorithm to solve multi-objective optimization problem. The particle number is 100, iteration is 500 times, each time hydropower units and thermal power output situation as shown: In order to verify the superiority of improved particle swarm algorithm, using the PSO and DPSO to solve the multi-objective problem in the same time, the parameters of two algorithms is shown in Table 5, the results are as follows: Optimization results analysis 1. From Figure 2 and Table 3, we can see the second and fourth hydropower station which take a larger output in the scheduling period have larger installed capacity and higher water head. As one of the goals is minimum water consumption, so large capacity unit will be preferential arrangement generation under the high head for reducing water consumption, which is consistent with the actual situation. 2. It can be seen from Table  1 and Figure  3, the average coal consumption of no. 1, 2, 4 power

DPSO algorithm steps: Step 1: Initialize all parameter, such as particle scale, initial position and speed of particle. In the paper, we regard the output power of heat-engine plant and water discharge of hydropower station as particles in every scheduling time; Step 2: Using the fitness function to evaluate the adaptive value of particles, then compare with the adaptive value of optimal location Pi , update Pi ; Step 3: Comparing all particles own fitness and global optimal adaptation value of particle position Pg, using the optimal adaptive value updates Pg; Step 4: Using (10) to update the particle’s speed and displacement; Step 5: Using the (11) to update the inertia factor; Step 6: Determining whether the stop condition is meet, if achieve the maximum number of iteration or accuracy requirement, then stop the operation, output the optimal solution, otherwise return to step 2 to continue iteration until meet the condition. 4

EXAMPLE

An example with 8 cascade hydroelectric plants and 10 thermal plants is analyzed. Thermal power plants

Table 1.

Parameters of coal-fired plants. Coal consumption coefficient

Power output limit

Power plant no.

Installed capacity [MW]

aj [×10−5t/MWh2]

bj [t/MWh]

cj [t]

Pj min [MW]

Pj max [MW]

Average coal Consumption [g/kWh]

1 2 3 4 5 6 7 8 9 10

2 × 600 2 × 600 2 × 300 2 × 600 2 × 125 2 × 360 2 × 330 2 × 200 2 × 330 2 × 135

0.3 1.4 6.1 0.8 3.3 1.6 0.3 2.1 1.5 4.9

0.27 0.26 0.28 0.27 0.31 0.29 0.31 0.30 0.30 0.31

13.70 14.50 6.35 14.10 4.64 6.81 6.74 5.42 6.77 5.04

300 300 150 300 62.5 180 150 100 165 67.5

1200 1200 600 1200 250 720 600 400 660 270

298 291 329 297 356 318 319 335 322 353

Table 2.

Parameters of emissions.

Power plant no.

αi [t/h]

βi [t/MWh]

γi [t/MWh2]

1 2 3 4 5 6 7 8 9 10

0.0531 0.0423 0.0254 0.0236 0.0619 0.0164 0.0263 0.0210 0.0307 0.0163

0.0355 0.0509 0.0605 0.0412 0.0556 0.0222 0.0591 0.0132 0.0288 0.0212

0.0333 0.0459 0.0564 0.0438 0.0515 0.0260 0.0558 0.0148 0.0277 0.0256

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Table 3.

Basic parameters of cascade hydropower stations.

Power plant no.

Maximal power output [MW]

Minimal power output [MW]

Maximal reservoir volume [× 108 m3]

Minimal reservoir volume [× 108 m3]

Initial reservoir volume [× 108 m3]

Water head [m]

1 2 3 4 5 6 7 8

1200.00 1320.00 405.00 4200.00 1210.00 566.00 192.00 600.00

405.20 730.00 126.90 1234.00 242.00 201.00 123.00 213.40

102.57 0.88 2.78 164.00 33.50 9.64 3.40 9.50

25.99 0.08 1.84 50.60 10.40 2.84 0.65 3.56

83.95 1.46 2.21 111.10 22.00 6.54 2.00 6.02

110.70 176.00 34.00 125.00 60.80 22.00 9.70 19.5

Table 4.

Situation of system loads in scheduling periodic.

Time [h]

Load [MW]

Time [h]

Load [MW]

Time [h]

Load [MW]

Time [h]

Load [MW]

Time [h]

Load [MW]

Time [h]

Load [MW]

1 2 3 4

7200 7400 7600 8000

5 6 7 8

8600 9500 9700 11000

9 10 11 12

11500 11900 12000 12300

13 14 15 16

12200 11300 12300 12400

17 18 19 20

12000 11600 11300 10000

21 22 23 24

9800 9600 9200 9000

Table 5.

DPSO/Std.PSO arithmetic parameter.

DPSO

Std.PSO

Parameter

Value

Parameter

Value

Particle number w c1 c2 Iteration

100 w(t) 2 2 500

Particle number w c1 c2 Iteration

100 (0,1) 2 2 500

Figure 2.

Power output of thermal power plants.

station is lower than other power plants, and one of the goal is to pursue the minimum coal consumption of thermal power generation, therefore, the units with lower coal consumption rate and large capacity should assume larger output which can effectively reduce the coal consumption amount, thus reducing emissions of pollutants. 3. It can be seen from Table  6, with the total load settled, the water consumption, coal consumption and pollution emissions solved by improved algorithm are lower than them

Figure 1. Power output of cascade hydropower stations.

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For solving multi-objective optimization model, the objective function without the need for the determination of weighting coefficients, using Euclidean distance and satisfaction function on different dimensions and conflicting objectives normalized to simplify the solution process. For containing multi-dimensional constraint, a large, complex nonlinear problem solving, improved PSO has higher accuracy and faster convergence speed and better accuracy than standard particle swarm algorithm. ACKNOWLEDGEMENTS

Figure  3. Optimal scheduling of hydrothermal combined system. Table 6.

Firstly, I would like to express my heartfelt gratitude to my friend Pengfei Cheng, he give me many ideas. Second, I would like thank for Guangzhou College of South China University of Technology and the project “Student Research Plan:The optimization scheduling and benefit evaluation for wind and Hydro-thermal Power System, JY140635.” Last but not the least, my thanks would go to my students who helped me make the program. It took them a year spare time to make the program and help me write the thesis.

Dispatch results.

f1 [×104t] f2 [×108 m3] f3 [×105t] The total output of hydropower stations [×105MWh] The total output of thermal power plants [×105MWh]

DPSO

Std.PSO

3.310 5.534 1.136 1.3810

3.432 5.613 1.178 1.3676

1.0245

1.0492

REFERENCES which by standard particle swarm algorithm, it shows that the precision of improved algorithm is higher. From Table 6, hydropower output is greater than the output of thermal power units in the scheduler all times, reached the water replacing coal, which can effectively reduce the use of coal and pollutant emissions, proves the model and the algorithm are availability. 5

[1] Z.Z. Guo, J.K. Wu, F.N. Kong. 2013. Multi-objective Optimization Scheduling for Hydrothermal Power Systems Based on Electromagnetism-like Mechanism and Data Envelopment Analysis. Proceedings of the CSEE33(4): 53–61. Nanning: Guangxi. [2] J. Yu, X.M. Ji, A.B. Xia. 2009. Multi-objective Hydrothermal dispatch based on energy conservation and environmental protection. Power System Protection and Control 37(1): 24–27. Nanjing: Jiangsu. [3] J.K. Wu, Y.Li. 2011. The Multi-objective Optimized Scheduling of Hydro-thermal Power System. Modern Electric Power 28(1): 53–57. Nanning: Guangxi. [4] Y.J. Wang, X.Y. Xiong. 2000. A genetic algorithm on short term optimal scheduling for a hydrothermal system. RELAY 10(28): 21–24. Wuhan: Hubei. [5] S.Q. Liu, L.F. Zou, H.L. Zhang, etc. 2010. Hydrothermal Generation Scheduling Based on Enhanced PSO. Water Resources and Power 28(7): 153–156. Kunming: Yunnan. [6] R.F. Wang, Y.J. Zhang, Z.S. Pei. 2011. Novel particle swarm optimization algorithm. Computer Engineering and Applications 47(16): 32–34. Zhengzhou: Henan.

CONCLUSIONS

In this article, we establish multi-objective optimization model which can make the coordination among economy, water resources and environmental protection. The simulation results show that the large capacity, high head hydroelectric power generation assumes greater output in the scheduling period, while the lower coal consumption rate and larger output capacity units assume larger output, achieving water to replace coal, energy saving and emission reduction.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

An optimal space-borne Solid-State Recorder based on domestic chips S. Li & Q. Song National Space Science Center, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China

J.W. Song, W. Wang, Y. Zhu & J.S. An National Space Science Center, Chinese Academy of Sciences, Beijing, China

ABSTRACT: This article describes the design and implementation of a novel space-borne Solid-State Recorder (SSR) by domestic chips which have never been used in space environment. Currently, the foreign high-grade chips embargo seriously restricted the development of China’s space cause. The attempt of domestic chips in this field will change the situation. The domestic chips include a radiation hardened CPU and three types of 3D package radiation-hardened memory chips. The structure of novel SSR is CPU + FPGA + main storage area. The main storage area, which has the capacity of 512 GB, is made of eight pieces of 64 GB NAND FLASH. FPGA and CPU are connected by a PCI bus, which increase the speed of commands and state signal. The SSR is designed for a satellite and the system cascade tests with 19 payloads are finished. Its throughput is 300 Mbps and can be raised to 1 Gbps and the power is no more than 5.4 W. Its capacity can be expanded to TB by PCI. The result shows that the SSR meets the requirements of practical application and the performance of which can compete with imported components. Keywords: 1

NAND FLASH; PCI; SSR domestic chips a high-capacity storage system. Shijian-5  satellite used SDRAM which has 64 Mbits of one chip, to produce a 512 Mbits SSR. After that, the SDRAM is widely used in the space-borne SSR, for example the series of Shenzhou. The largest SSR with SDRAM used in space is 48 Gbit of single module, but it is not enough for the increasing demand of space exploration. Recently, with the rapid development of the NAND FLASH technology, it has the advantages of non-volatile, higher storage density, lower cost and lower power which give it wide options to replace the SDRAM to obtain the powerful states in the field of space memory. Because of the embargo of the space-grade, radiation-tolerant chips such as anti-radiation CPU, of the developed countries, the development of China’s Space Cause has been held back for many years. Research on the domestic high-performance chips could meet the increasing demand of space exploration tasks and enhance the security and reliability of our equipment. Developing and shows our own intellectual property rights especially in the field of space exploration, which could break the blockade on techniques, means a lot to our country. We designed a high-speed SSR with mostly of domestic chips, which could reach 1 Gbps and 512 GB capacity of one module. The capacity and throughput of whole SSR could be extended by PCI.

INTRODUCTION

The storage system is an important component of a modern electronic system, especially in the field of communications, radar and aerospace[1]. The data transmission system is the most important link in satellite communication, whose most important unit is the storage system. With the rapid development of China’s Space Cause, the demand for storage system is rising. The payload for space surveillance, earth observation and space exploration in the future will produce more and more high-speed and high-definition data, so a high-throughput and large-capacity space-borne storage system is needed. Initially, a space-borne storage system was made by tape drives. With the increase in integration density of semiconductor memory chips, a spaceborne storage system entered the age of Solid-State Recorder (SSR). The SSR used electronic components to do all the operation instead of mechanism components[2],[3], so it is stable when hit, struck and shocked which is a notable advantage for application in the field of space exploration. The earliest SSR in China is the SSR of Shijian-4 satellite, which is composed of SRAM to attain a capacity of 2 Mbits. For the limited technology and high cost of SRAM, the storage density is so low to form

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2

used is a high-performance application processor SOC based on LONGSON-1 processor[7]. It contains interrupt controllers, timers, RS232  serial port controller, floating-point processor, PCI and some memory interface supporting SDRAM and Flash ROM. The internal frequency is 50  MHz, while the external frequency is 100  MHz fixed point 300 MIPS and floating-point 50 MIPS. To make it competent for use in the radiation harsh environment, it has been fastened in modules. It can stand the anti-radiation dose not less than 168 krad, and the single particle lockout threshold of it is not less than 74 Mev ⋅ cm2/mg[8]. Therefore, it is qualified for satellites in all orbits.

HARDWARE DESIGN

2.1

The characteristic of domestic memory chips

Currently, the mainstream chips used in SSR are SDRAM and NAND FLASH. NAND FLASH chips have the largest memory density, while the access time of SDRAM is the least among mainstream space-borne memory chips. In our design, we combine the two chips to achieve a high-speed SSR with large capacity. In space, there are a great number of high energy particles due to which Total Ionising Dose (TID), Single Event Upset (SEU), Single Event Latch up (SEL), single event functional interrupt, single event burnout, single event transient and displacement damage[4] will have a great influence on the performance of memory chips. Space-borne chips must do some measures against the effect. We use the chips produced by Zhuhai Orbita Company. Table 1 shows the antiradiation property of the used memory chip which is NAND FLASH (VDNF64G08-F), SDRAM (VDSD3G48) and EEPROM (VDEE8M08). As the cost of sending a satellite into the space is rather huge, the space-borne equipment needs high integration. Zhuhai Orbita used 3D technology[5] to form one high integration chip with many dies. The core memory chips in SSR are NAND FLASH VDNF64G08-F. It is packed with eight pieces of 8G NAND FLASH die to attain the capacity of 64G for one chip. It looks like a golden cube. The eight pieces in one cube share the I/O bus, power but have independent CE, CLE, ALE, RE, WE. 2.2

2.3

Large-capacity storage system can be classified into five modules, which are the storage module, the FPGA module, the computer module, the external interface module and buffer module, according to module’s function. The storage module is made of NAND FLASH. We use four chips to do 32-bit parallel operation. The buffer module is made of SDRAM. When data come in, they will be stored into SDRAM first, then transfered to NAND FLASH at spare time of SDRAM. NAND FLASH runs in 32 M clock while SDRAM runs in 64 M clock, which will enable the system get 1 Gbps throughput. The CPU module is made of LONGSON, SDRAM and EEPROM, which is the core of the system. To make the system core reliable enough, the chips are all of anti-radiation. Moreover, the error detection and correction are employed. The interface module is a key factor which will affect the versatility of the whole system. We have done research on all tasks in the recent 5 years and conclude the commonly used interfaces. The SSR needs interface to transfer data both in low-speed and high-speed, and the bus interface to communicate with the external control unit of a whole system. The command and logic information of storage area are all from the bus, so it must be

The characteristic of domestic CPU

For the embargo of high-performance CPU, the CPU of most of the SSR in use is low-end products. The highest product among most commonly used CPU is ERC32 with SPARC V7 core, which can only operate at 25 MPIS[6]. The CPU we use is called LONGSON, which is designed towards the space application with forward design and has got intellectual property rights. The LONGSON we

Table 1.

Hardware design

The anti-radiation property table of memory chip.

Specification

Capacity

TID

NO SEL

NO SEU

X section

VDNF64G08 VDSD3G48 VDEE8M08

64 G 3G 8M

50 kRads 50 kRads 50 kRads

50 Mev ⋅ cm2/mg 5 Mev ⋅ cm2/mg 7E−11 80 Mev ⋅ cm3/mg 2 Mev ⋅ cm2/mg 3E−11 6 Read 1E-5/ 80 Mev ⋅ cm /mg Read 25 Mev. cm2/mg/ write 5E-4 write 10 Mev ⋅ cm2/mg

Temperature

Largest access time (ns)

−55–125 ºC −55–125 ºC −55–125 ºC

25 7.5 250

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scheduling algorithm, and the same priority tasks will be operated in the round-robin scheduling algorithm. The software is designed for a real task which will be sent to space in 2016. The main function of the management software includes the four points below:

Figure 1.

1. External bus communication management: communicates with the external control unit, receives control commands and resolves it, does long hold cycle test, checks the time with the whole system, sends engineering parameters of SSR. 2. Storage Management: allocates new address for the storage area, gives the playback command, records the information of writing and playback, sends erase command, manages the Bad Block Table (BBT) for SSR. 3. Clock management: system auto-time determination and system automatic correcting time. 4. System maintenance: system initialisation, tissue engineering parameters, feed the dog.

Functional block diagram of SSR.

very reliable. The dual redundant bus 1553B is used. In our design we use LVDS as a high-speed data interface, RS422 as a low-speed data interface. Within the SSR itself, the FPGA module will have a lot of communication with the CPU module. The CPU module will resolve all the command from the external control unit of the whole system, and does logic management including address allocating, playback check, bad block management and so on, for SSR. The FPGA module is responsible for all timing control of all the hardware operations in SSR. To some extent, the CPU module is the software core and the FPGA module is the hardware core of the SSR. There must be many information interaction between the two, so the bus connecting them would decide the efficiency and flexibility of the SSR. The PCI is a local computer bus for attaching hardware devices in a computer. PCI is an initialism of peripheral component interconnect[9] and is part of the PCI local bus standard. The PCI bus supports the functions found on a processor bus, but in a standardised format that is independent of any particular processor’s native bus. Devices connected to the PCI bus appear to a bus master to be connected directly to its own bus and are assigned addresses in the processor’s address space[10]. It is a parallel bus, synchronous to a single bus clock, which could reach the speed of 132 Mbps[11]. Figure 1 shows the functional block diagram of SSR. 3

The relationship between the various functional modules is shown in Figure 2. The software will auto-set some related registers to ensure the system could boot with a stable environment. The related registers will be extracted from the BBT, so it will read the storage area by inquiry. FPGA will gather the information in spare area of NAND FLASH and send it to CPU module for it to organise a BBT. The initialisation end command will be sent after the valid block number is got by the CPU. After initialisation, the software will enter the main circle by itself. It will be run in the circle until a command arrives. When it gets the writing command, it will search the BBT to allocate a valid block for new data to be stored. After one block is finished, a state register of NAND FLASH would be checked to verify whether the data are stored well. If it is stored well, the information of the block will be remembered in CPU to form a Block Access Table (BAT). When the playback command is obtained, it will search the BAT, locate the target data stored block and sends the address to FPGA module. When the erase command is obtained, it will check the address with the BBT to avoid erasing a bad block for it may

SOFTWARE DESIGN

SSR management software is designed based on VxWorks, an embedded real-time operating system. The application implementation process is realised in the form of the operating system tasks. Vxworks is a kind of preemptive real-time kernel operating system, commonly used in satellites[12]. Vxworks operate the tasks by the priority

Figure 2.

The relationship of each module.

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The actual playback speed can reach higher, but the transmit speed of the transmitter unit is 150 Mbps. It is no sense to reach so much high. The capacity and throughput also could be enhanced by adding more modules. 5

CONCLUSION

The design proposed in this article is a high-speed, extended, universal end reliable SSR by using domestic chips. The SSR is already used in a practiced task, a satellite is to be sent in 2016. It could meet all the demands of application and have the potential to be developed and spread. ACKNOWLEDGEMENT This paper is supported by the ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences under Grant No. XDA04060300.

Figure 3. Table 2.

REFERENCES

Functional block diagram of SSR.

[1] L. Lei. Design and implementation of a multiplefunction storage system based on NAND FLASH. Beijing Institute of Technology. Beijing. 2008. [2] Zhu Zhibo. Design of high speed and mass storage system based on NAND FLASH [J]. Modern electronics technique, 2011, 34(8):170–173. [3] Song Jie, He You, Tang Xiaoming. The ultra-high speed radar signal real time acquisition and storage system based on FPGA [J]. Application of electronic technique, 2005(11):18–20. [4] Ningfang Song, Mingda Zhu, Xiong Pan. Experimental Study of Single Event Effects in SRAM—Based FPGA. Journal of Astronautics. 2012, 33(6):836–842. [5] Said F. Al-sarawi, Derek Abbott, Paul D. Franzon. A review of 3-D packaging technology, IEEE Transactions on Components, Packaging and Manufacturing Technology. 21(1998):2–14. [6] Z.Y. Wang. The application of domestic CPU in space computer. Chinese Academy of Sciences National Space center. Beijing. 2006. [7] McCollum, J. ASIC versus antifuse FPGA reliability[C]. Big Sky, MT: Aerospace conference, 2009 IEEE, 2009:1–11. [8] LONGSON processor user manual. LONGSON Branch Technology Co., Ltd. 2011.07. [9] http://www.webopedia.com/TERM/P/PCI.html. [10] Hamacher et  al (2002), Computer Organization, 5th ed., McGraw-Hill. [11] PCI Special Interest Group. PCI local bus specification [J]. Kluwer Academic Publisher, 2001, 2(3). [12] Zhang Zengji. Design of Center Control Software of an Airborne SAR Based on VxWorks [J]. Industrial Control Computer, 2012(25(9)):3–6.

System parameters.

Parameters

Indicators

Size Power Capacity Throughput The actual playback speed

6U <5.5 W 512 GB 1 Gbps 300 Mbps

break the initialisation information for SSR. All the operations done by the FPGA module will produce an interrupt to inform the CPU module. The workflow of the management software is shown in Figure 3. 4

TEST AND ANALYSIS

This SSR has been used as a storage system in a satellite. So far, the unit test and system cascade test have been completed. The test results are good. In the test the completed core features are BAT table read, update BAT after bad block occurs, reading, writing, erasing at the same time. In this satellite the SSR is designed to receive data from 19 payloads in the two tanks. The playback data will transmit to the transmitter unit. The system parameters and test results are shown in Table 2.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study on additional damping controller for VSC-HVDC to prevent low-frequency oscillation Z.H. Wang & Y. Li School of Electrical Engineering, Northeast Dianli University, Jilin Province, China

X.Y. He State Grid Liaoning Electric Power Supply Co. Ltd., Liaoning Province, China

ABSTRACT: In this paper, an additional damping controller for VSC-HVDC with the structure of single input and single output is presented. Choosing active power order of VSC-HVDC as input and active power transferred on parallel AC line as output, the simplified opened-loop transfer function between input and output is derived by Prony identification. Then the damping controller is designed by closedloop pole placement. The model of VSC-HVDC system utilizing additional damping controller is constructed, and the time-domain simulation analysis of an example of accessing the VSC-HVDC model is carried out. The simulation results show that AC system damping for low-frequency oscillation is obviously increased and operation stability is improved. Keywords: 1

VSC-HVDC; additional damping controller; Prony identification; low-frequency oscillation

INTRODUCTION

2

VSC-HVDC can fast and flexibly control active power transferred, meanwhile it can dynamically compensate reactive power to AC bus and stabilize AC bus voltage [1–3]. This can provide necessary reactive power support. Also this realizes optimal regulation of power flow during normal operation, rapid and emergent power support within the AC system and isolate fault for limiting extension of accident. Meanwhile, the VSC-HVDC system could enhance the controllability and anti-disturbance ability to improve operation stability. Prony identification is a method of solving system model by time-domain response data. Application of this method has covered several areas such as measured data analysis of power system, on-line stability control and parameters design of power system stabilizer. Therefore, it promises a good prospect of application [4]. This paper presents an additional damping controller for VSC-HVDC to increase AC system damping for swing and to improve operation stability. The paper will first give a brief description about the study system. Following that the damping controller for VSC-HVDC is designed based on Prony identification and pole placement. Finally, the time-domain simulation is used to verify the effectiveness of the additional damping controller.

BRIEF DESCRIPTION OF THE STUDY SYSTEM

Taking an example from reference [5] based on LCC-HVDC, the single line view of the study system is shown in Figure  1, where a single generator is connected to the infinite bus by VSCHVDC and parallel AC line. In normal condition, station 1 adopts active power control mode and reactive power control, and station 2 adopts DC voltage control mode and reactive power control. Utilizing dynamic adjusting loop and amplitude limiter, which is the so-called damping control, is an effective control strategy to prevent power oscillation [6–7]. Figure  2  shows that, the Pm in active power controller is a disturbance reference value added when considering system oscillations. It is added with stability order Pset for preventing power oscillation, improving the static stability of

Figure 1.

Single line view of the study system.

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Figure 3. Figure 2.

Active power control of VSC-HVDC.

2. When the expected dominant pole is selected as λ0, the time constant of phase compensation is determined by Equation (3). The estimated value of damping controller gain is equal to k/|G′(s)|, 3. Repeat Prony identification process after damping controller configuration, and adjust damping controller gain based on identification results until the Prony identification pole of closed-loop system is approximate to the expected pole λ0.

AC system and providing emergent power support to keep AC system stable after disturbance. 3

DESIGN OF ADDITIONAL DAMPING CONTROLLER FOR VSC-HVDC

As shown in Figure  3, the opened-loop transfer function between the VSCs active power set point Pset and active power Pac transferred on parallel AC line is represented as G(s), and the transfer function of damping controller is represented as H(s). The closed-loop transfer function Gc(s) can be expressed as Gc ( s ) =

G (s) . 1 − G ( s )H ( s )

4

(1)

(2) (3)

Then the damping controller is designed and tuned based on Equations (2) and (3). The steps of utilizing Prony identification and pole placement for additional damping controller design are as follows:

5

1. Applying a short narrow pulse, whose Laplace transform value is approximate to k, to the input Pset, the simplified transfer function G′(s) is derived by Prony identification of Pac output response, which is bi . s − zi i =1

SIMULATION ANALYSIS

In order to verify the effectiveness of additional damping control, time-domain simulation model of an example as shown in Figure 1 is constructed in Matlab/Simulink. Utilizing the damping controller for VSC-HVDC with structure of single input and single output, as shown in Figure 5, the expected dominant pole is selected as −0.20 ± j6.60. Therefore, the corresponding oscillation frequency is equal to 1.05  Hz and damping ratio is about 3.13%. Based on this, the parameters of damping controller are as follows: T1 = 10, T2 = 10, T3 = 0.55, T4 = 0.2, K′ = 0.25.

p

G′′( s ) = ∑

THE RELATIONSHIP BETWEEN DC POWER FLUCTUATION AND DC VOLTAGE FLUCTUATION

When the AC system is disturbed, the DC power regulation gives rise to DC voltage fluctuation, for which there is risk of overvoltage and overcurrent for VSC-HVDC. To avoid this problem, an amplitude limiter is required to limit the output of additional damping controller. The maximum value and minimum value of limiter can be determined by the relationship curve between DC power fluctuation and DC voltage fluctuation. By adding DC power fluctuation of sinusoidal form with different amplitude to Pset, the corresponding DC voltage fluctuations can be measured as shown in Figure 4. Using the relationship curve, the maximum value of DC power fluctuation is about ±17% on the condition that the maximum allowable DC voltage fluctuation ±5%.

Based on the method of pole placement, if the expected pole is selected as λ0, the amplitude and phase of H(s) at λ0 should satisfy the following equations H ( λ0 ) G ( λ0 ) arg [ H ( λ0 )] arg [G ( λ0 )].

Closed-loop system between Pset and Pac.

(4)

Since the input is a non-ideal shock input (the Laplace transform value of ideal shock input is equal to 1), the gain of real system model is k times to that of identified system model.

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Figure 4. Relationship curve of DC power fluctuation and DC voltage fluctuation.

Figure  5. HVDC.

Additional damping controller for VSCFigure 7. Simulation results of generator power angle, AC line active power and DC bus voltage.

black line represents the condition with damping controller activated. Figure  7a,b shows that the generator power angle and AC line active power can rapidly recover to steady state. The DC bus voltage of VSC-HVDC, as shown in Figure 7c, is maintained well during DC power regulation. It is concluded that additional damping controller for VSC-HVDC can effectively increase system damping for active power oscillations in the AC system and improve operation stability. 6

CONCLUSION

A simulation model of the VSC-HVDC integrated into a simple power system with single generator and infinite bus is built in the Matlab/Simulink environment. The additional damping controller based on Prony identification is constructed and applied to the VSC-HVDC system. Two conditions with the damping controller activated and inactivated are simulated. Simulation results show that the VSC-HVDC can provide flexible power support with no static error, so that AC system damping for active power oscillation is increased to improve system operation stability.

Figure  6. The DC power and its reference of VSCHVDC in different conditions.

Under steady state, the VSC-HVDC only transfers nominal active power. At t = 5 s, a three-phase fault is applied to bus i, lasting for 0.1 s, to lead to active power oscillation. The conditions with the damping controller activated or inactivated are simulated. In Figure 6, the red line represents DC power reference while the measured DC power is shown in black. As can be seen from the graphs, the DC power of VSC-HVDC can track its reference fast and accurately. Figure 6b also shows that the amplitude limiter of damping controller is functioned in early stage of DC power regulation after disturbance. In Figure  7, the red line represents the condition with damping controller inactivated while the

REFERENCES [1] Y.H. Li, J.Y. Yang, J.H. Zhang. Application of VSCHVDC in Urban High Voltage Power Network. Power System and clean Energy, Vol. 25, No. 8 (2009): 14–18.

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[2] G.B. Zhang, Z. Xu, G.Z. Wang. Steady-state Model and its Nonlinear Control of VSC-HVDC System. Proceedings of the CSEE, Vol. 22, No. 1 (2002): 17–22. [3] G.F. Tang. High Voltage Direct Current Transmission Based on Voltage Source Converter. Beijing: CEPP, 2010. [4] C. Zheng, X.X. Zhou. Small Signal Dynamic Modeling and Damping Controller Designing for VSC Based HVDC. Proceedings of the CSEE, Vol. 26, No. 2 (2006): 7–12.

[5] Kunder P. Power System Stability and Control. New York: McGraw-Hill Inc, 1994, 777–781. [6] Preece R, Almutairi A.M, Marjanovic O. Damping of Inter-Area Oscillations Using WAMS Based Supplementary Controller Installed at VSC based HVDC Line. IEEE Power Tech. Conference, Trondheim, 2011. [7] W. Yao, J.Y. Wen, S.J. Cheng. Design of Wide-Area Supplementary Damping Controller of SVC Considering Time Delays. Transactions of China Electrotechnical Society, Vol. 27, No. 3 (2012): 239–246.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Lightning surge analysis for 220 kV AC double circuit transmission line using LPTL Y. Ma Laboratory of State Grid, Jiangsu Electric Power Research Institute, Nanjing, Jiangsu Province, China

C.S. Liu Huazhong University of Science and Technology, Wuhan, Hubei Province, China

Y. Liu, G. Chen, T.X. Xie, Z.C. Zhou & F.B. Tao Laboratory of State Grid, Jiangsu Electric Power Research Institute, Nanjing, Jiangsu Province, China

ABSTRACT: In order to analyze the main factors that influence lightning back flashover performance of 220 kV double-circuit AC transmission lines on the same tower and guide lightning protection renovation work, a lightning back flashover simulation model of double-circuit AC transmission lines on the same tower is established by using lightning protection calculation tool ‘LPTL’ in this paper. In the model, equivalent tower of wave impedance model with multi-conductor parameter distribution is utilized, and leader method is using to judge the insulator string flashover as insulator flashover criterion. The lightning back flashover level and back flashover trip-out rate are calculated to analyze the influence of tower height, ground resistance and soil resistivity on the tower back flashover performance, and then, a case analysis of lightning trip-out is conducted by using LPTL. The results show that these factors have a great impact on the performance of the tower lightning protection. Keywords: double-circuit transmission lines on the same tower; lightning protection performance; LPTL; simulation model 1

INTRODUCTION

are more prominent [4] [5]. Therefore, an accurate assessment of the performance of a 220 kV double-circuit transmission line on the same tower is critical to ensure safe and stable operation of the power grid. Based on transmission equipment lightning protection calculation tool of Jiangsu Electric Power Corporation (hereinafter referred to as ‘LPTL’), a double-circuit transmission line tower with counter simulation model is established, and factors sensitivity of back flashover of the 220 kV double-circuit transmission line on the same tower is studied.

Overhead transmission line is an important part of China’s electric power system, and its state determines whether the power system can operate safely, reliably and stably. According to statistics of China’s high voltage transmission lines accident, transmission line trip-out accidents account for about 40%–70% of total lines trip-out accidents [1–3]. Accidents caused by lightning have become one of the major failures of transmission lines. In some areas where frequent lightning activity exists, the probability of line accidents struck by lightning is greater. Therefore, an accurate assessment of the line lightning performance is significant for design and construction of transmission lines, especially for improving the stability of power system operation. Since a 220 kV transmission line is one of the main transmission lines grid, thus ensuring the safe and stable operation of 220 kV line is a major problem that is placed before power workers. Currently 220 kV transmission line is a double-circuit on the same tower which has large triggered lightning area and big opportunity of lightning strike, and the shielding failure and back flashover failure

2 SIMULATION CALCULATION OF LPTL 2.1

Introduction of LPTL

LPTL is a software for calculation and analysis of transmission line lightning protection performance. LPTL allows users to create transmission lines and towers, count temporal and spatial distribution of lightning activity along the line corridors and view and obtain the topography of each base tower. Through the appropriate choice of calculation model and criterion, the lightning analysis tasks

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lightning back flashover performance of transmission lines.

are established; based on a typical transmission line tower, several towers or the whole line, analysis and evaluation results of lightning shielding and back flashover performance can help and guide the user to carry out lightning protection work. LPTL software has the following features:

2.2

In LPTL, entering basic information lines and towers is made to complete the creation of lines and towers. In LPTL, regulation method and electromagnetic transient analysis method are the main methods to calculate the lightning back-flashover performance, while the latter is a simplified integration model of ATP/EMTP, when modeling, lightning current model, the channel impedance, line model, tower model, flashover criterion, the induced voltage and grounding resistance, and other model parameters can be directly selected, which greatly reduce the workload of system modeling.

1. manage a grid of transmission lines and towers; 2. grasp lightning activity of a region and the temporal and spatial distribution of flash frequency law; 3. grasp temporal and spatial distribution law of lightning activity of the transmission line corridor; 4. obtain high-resolution area and flashover density distribution of lines to guide the grid lightning protection work; 5. query and access leach base tower topography, which is convenient for line inspection and troubleshooting; 6. use regulation method and electrical geometric model law and leader method to research and analyze the lightning shielding strike performance of transmission lines; 7. use regulation method and electromagnetic transient analysis method to study and analyze

Figure 1.

Figure 2.

Back-flashover model in LPTL

2.2.1 Lightning current waveform and lightning channel wave impedance The parameters of lightning current waveform include: lightning current amplitude, the wave head time and tail time. Statistical results showed that most of the lightning current wave head time is in the range of 1.0–5.0 μs, the average is about 2.0–2.5  μs [6]. In this paper, 2.6/50.0  μs double exponential wave is selected as a simulation of lightning current source. For back flashover simulation, lightning channel impedance is about 400 Ω.

Interface of software LPTL.

2.2.2 Model of transmission lines Under the impact of lightning, great changes of the electrical parameters of line will appear compared with the work frequency parameters. In the simulation, the frequency-dependent model of transmission line (based on domain transform) is used, this model can calculate the line impedance with frequency by inputing line structural parameters, corresponding to JMarti transmission line model in EMTP.

Model parameter setting page of LPTL.

2.2.3 Model of the tower The wave impedance method is to take the injected current wave as plane wave approximately after lightning strikes the tower, by using the lumped wave impedance to describe wave propagation injection current wave in the tower, and then the towel potential and injected component of the voltage across the insulator strings are obtained. Multi-wave impedance is based on the theory of wave impedance, depending on the structure of the tower, the method divides the tower into multiple segments wave impedance so that the tower reflection process of current wave is more consistent with the actual situation [7]. The tower multiimpedance equivalent model is shown in Figure 3.

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dL ⎡ u(t ) ⎤ = ku(t ) ⎢ − E0 ⎥ dt ⎣D L ⎦

(5)

In the formula, L(t) is the leader development length (m); u(t) is the voltage (kV) on the insulator strings; D is the gap length (m); E0 is the leader starting field strength (kV/m); k is the fitting experience factor obtained by the experiments results, which is related to insulator type, the applied voltage polarity and so on (m2/(s ⋅ kV2)). In this paper, the simulation uses k = 1.1, E0 = 500.0 kV/m. 2.2.5 Model of grounding resistance Impulse grounding resistance is influenced by flowing current amplitude and frequency, showing strong nonlinear characteristics. In this paper, IEC recommended formula (6) is used to calculate grounding impedance values when impulse current flows [9].

Figure 3. The tower multi-impedance equivalent model. In this Figure, hk is cross arm height, m; rTk is single conductor top equivalent radius, m; RTk is multi-conductor systems adjacent conductors spacing, m; RB is multiconductor systems adjacent conductor spacing, m; rB is single conductor bottom radius, m. ZAk is cross arm wave impedance, Ω; ZTk is trunk impedance, Ω; ZLk bracket portion impedance, Ω.

RT =

(1)

Ig =

And rek is equivalent radius of towel trunk.

(

rek = 21 / 8 rTk1 / 3rB2 / 3

1/ 4 3/ 4 ) (RTk1/ 3RB2 / 3 )

(2)

In Hara tower model, the transmission line length the bracket portion wave impedance is 1.5 times the trunk section. 3. Towel cross arm wave impedance ZAk = 60 ln

2 hk rAk

E0 ρ 2π R0 2

(7)

2.2.6 Calculation model of induced voltage In this paper, the induced voltage is calculated by the formula recommended by the State Grid Electric Power Research Institute [10]:

(3)

9ZTk

Ig

where ρ is the soil resistivity (Ω ⋅ m); E0 is the field strength when soil is ionized (kV/m).

2. Towel bracket wave impedance ZLk

(6)

And R0 is the impulse impedance under low frequency and low amplitude current (Ω); I is the magnitude of the impulse current flowing through the grounding (kA); Ig is the smallest current ionizing the soil (kA), which can be expressed as:

1. Towel trunk wave impedance ⎛ 2 2 hk ⎞ ZTk = 60 ⎜ ln − 2⎟ rek ⎝ ⎠

R0 1+ I

(

U i = 1.33 1.771 + 1.754 hc

)

hc 2

0.1706 hc + 1.935 × 10 −5 hc3 ⋅ I

0.232 .

⋅ (1 −

0

) (8)

(4)

where hc is the average height for wire-to-ground (m); I is lightning current amplitude (kA).

And rA is equivalent radius of the cross arm, one-fourth of cross arm width of the tower master connections node.

3

2.2.4 Insulator string flashover criterion The leader development length crossing through the gap can be used to judge insulator string flashover, CIGRE WG33.01 recommended leader development rate formula [8]:

A typical tower is shown in Figure 4 as the object for analysis, based on the model of LPTL, the factors influencing the back flashover trip-out performance of 220 kV double-circuit transmission line on the same tower.

ANALYSIS OF BACK FLASHOVER FACTORS

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Table  1. Impact of tower height on lightning back flashover performance (grounding resistance is 10Ω). Height (m)

24

30

36

42

48

Lightning back flashover level (kA) Back flashover trip-out rate

91

86

81

76

72

0.356

0.447

0.552

0.671

0.778

Figure 5. Curve of lightning back flashover levels and back flashover trip-out rate with tower height.

Figure 4.

3.1

Table 2. Impact of grounding resistance on lightning back flashover performance.

Schematic diagram of the tower.

Grounding resistance (Ω)

Impact of tower height on lightning back flashover performance

Lightning back flashover level (kA) Back flashover trip-out rate

In this paper, tower typical size as shown in Figure 4 is used; when the soil resistivity is 100  Ωm and grounding resistance is 10 Ω, the results of lightning back flashover level and trip-out rate with the tower height as 24 m, 30 m, 36 m, 42 m and 48 m are calculated. As can be seen from the results of Table 1 and Figure 5, the impact of tower height on lightning back flashover level of transmission line is obvious; with increase of tower height from 24 m to 42 m, lightning back flashover levels of the transmission line are significantly reduced. 3.2

5

10

15

20

25

100

81

73

69

66

0.399

0.486

0.608

0.728

0.861

Impact of grounding resistance on lightning back flashover performance

The analysis results of a typical tower grounding resistance are shown in Table 2 and Figure 6. Under the same height, with increase in the grounding resistance, lightning back flashover levels reduce and back flashover trip-out rate rises. Thus, the grounding resistance has impact on lightning withstand levels of the tower; therefore, reducing the tower

Figure 6. Curve of lightning back flashover levels and back flashover trip-out rate with grounding resistance.

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Table 3. Impact of soil resistivity on lightning back flashover performance. Soil resistivity (Ωm) Lightning back flashover level (kA) Back flashover trip-out rate

100

300

500

1000

2000

81

75

74

73

71

0.456

0.567

0.587

0.608

0.623

Table 4. Number of tower

Altitude (m)

Left inclination/°

Right inclination/°

50

3.006194

0.154539

−3.09186

Figure 8.

grounding resistance is an effective measure to raise the lighting back flashover level of the transmission lines and to reduce the back flashover trip-out rate. Impact of soil resistivity property on lightning back flashover performance

The analysis results of typical tower soil resistivity are shown in Table 3 and Figure 7. Under the same height, with the increase of soil resistivity, lightning back flashover levels reduce and back flashover trip-out rate rises. Thus, the soil resistivity has impact on lightning withstand levels of the tower; therefore, reducing the soil resistivity of the tower is an effective measure to raise the lighting back flashover level of the transmission lines and to reduce the back flashover trip-out rate. 4

Topography map of the tower’s location.

the transmission line department has lightning, the amplitude of lightning current is 262 A; field line patrol, we found 50 # phase-B insulator and equalizing ring have obvious burns. Based on the above information, we initially concluded that there was a trouble spot. The calculation and analysis of the tower with LPTL is applied, the lightning back flashover level of the tower phase B is 100.51kA, the back flashover trip is 0.3995. Connected with Google earth via LPTL, the tower topography is obtained, the results are shown in Table 4 and Figure 8. The results show that the tower location has flat terrain, the location is in farmland, which is not far from the river, and soil resistivity is low. According to information obtained via LPTL combined with results of lightning location and field patrol line, primarily the trouble was caused by lightning back flashover trip-out.

Figure 7. Curve of lightning back flashover levels and back flashover trip-out rate with soil resistivity.

3.3

Information of the tower topography.

5

CONCLUSIONS

In this paper, a lightning back flashover simulation model of 220 kV double-circuit transmission line on the same tower by using LPTL software is established, and on this basis, the various factors that affect lightning back flashover performance, including tower height, grounding resistance and soil resistivity are analyzed. The conclusions are as follows:

CASE APPLICATION

In Jiangsu province, phase-B zero sequence overcurrent segment of one 220 kV double-circuit transmission line on the same tower is acted, and then coincided successfully. According to lightning location system records, at that time 50 # tower of

1. Performance of lightning back flashover is weakening with tower height rising, and tower height is higher, the increasing magnitude of lightning back flashover trip-out rate is greater.

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2. Impulse grounding resistance has great impact on the performance of lightning back flashover, with the increase in grounding resistance, the lightning back flashover trip-out rate increases. 3. With the increase of soil resistivity, lightning back flashover levels reduce and back flashover trip-out rate has a rising trend. 4. Modeling for transmission line lightning protection computational analysis by LPTL is convenient, and the topography of the tower can be intuitively obtained by simplifying the modeling steps. It is convenient for the conduction of lightning protection calculation and analysis work of power transmission and transformation equipments.

[4] Gong Jian-gang, Tong Hang-wei. Evaluation of Lightning Trip-out Rates for Zhejiang Power Grid by using lightning location systems [J]. East China Electric Power, 2007, 35(1):73–75. [5] IEEE Working Group on Estimating Lightning Performance of Transmission Line. A Simplified Methods on Estimating Lightning Performance of Transmission Line [J]. IEEE Trans. on Power Apparatus and System, vol. 104, no. 4, pp. 919–931, 1985. [6] A. Ametani and T. Kawamura, “A method of a lightning surge analysis recommended in Japan using EMTP,” IEEE Trans. Power Del., vol. 20, no. 2, pp. 897–908, Apr. 2005. [7] T. Hara and O. Yamamoto, “Modeling of a transmission tower for lightning surge analysis,” Proc. Inst. Elect. Eng., Gen. Transm. Distrib., vol. 143, no. 3, pp. 283–289, May 1996. [8] Becerra M, Cooray V. A simplified physical model to determine the lightning upward connecting leader inception[J]. IEEE Trans. On Power Delivery, 2006, 21(2): 897–908. [9] Juan A. Martinez and Ferley Castro-Aranda, “Lightning Performance Analysis of Overhead Transmission Lines Using the EMTP,” IEEE Trans. Power Del., vol. 20, no. 3, pp. 2200–2210, Apr. 2005. [10] Nie Dingzhen, Zhou Peihong, Dai Min, etal. Study on lightning Performance for ±500 kV DC Double circuit Transmission lines [J]. High Voltage Engineering, 2007, 33(1):148–154.

REFERENCES [1] Wei Lie-xia-jin, Wu Wei-han. The Analysis of Lightning Protection for EHV and UHV Transmission Lines in Russia [J]. High Voltage Engineering, 1998, 24(2):76–79. [2] Huang Wei-chao, He Jun-jia, Lu Jia-zheng, et al. Calculation of lihtning tripout rate of transmission lines under real lightning sroke distribution [J]. High Voltage Engineering, 2008, 34(7):1368–1373. [3] Li Chang-xu, Yuan Zhong-jun, Zhang Hai-lomng. Study on EGM-Based 500 kV Double-circuit lines Flashover Rate [J]. Electric Power Construction, 2008, 29(02):15–18.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Fault analysis of Metal Oxide Varistor (MOV) for series compensation capacitor banks Y. Ma, Y. Liu, P. Li, G. Chen & Z.Z. Zhou State Grid Laboratory, Jiangsu Electric Power Company Research Institute, Nanjing, China

ABSTRACT: A fault of Metal Oxide Varistor (MOV) for series compensation capacitor banks is analyzed in this paper. The MOV is checked by visual inspection and electrical test, and the wave of fault recorder is analyzed by incorporating the simulation. The fault is due to the discharge of the capacitor, because the MOV is broken down while the phase–phase grounding fault appears. According to this fault, two improvement measures (change MOV not at will, replace MOV with a long history and performance degradation) are presented, so as to improve and enhance operation and maintenance situation of the MOV. Keywords: 1

series compensation capacitor banks; Metal Oxide Varistor (MOV); gap; fault analysis

INTRODUCTION

device is effectively input, the red light for switch on in A, B and C phase is on.

As the EHV power network develops in China, long distance and high-capacity AC line is applied more and more extensively. Because of restriction by transmission line corridor and environmental factors, improving capacity of the transmission line appears particularly important. Installing series compensation capacitor banks is one of the effective measures for improving capacity and saving of the transmission line. Metal Oxide Varistor (MOV) is one of the important protection equipments in series compensation capacitor banks. The overvoltage of a series capacitor is limited by MOV in parallel with a series capacitor. 2 2.1

2.2

Structure and parameter of faulted device

MOV, bypass gap, and bypass breaker are used as the protective device for a capacitor in the series capacitor compensation device. The basic wiring diagram of the compensation device is as shown in Figure 1.

FAULT INSTRUCTIONS Fault overview

As ground-fault in a line at a 500 kV substation, 18:46  in one day, series capacitor compensation device had operated for protecting. The device blocks perpetually series capacitor compensation. Line-to-earth fault is considered as external fault for the series capacitor compensation device; three phases of a bypass breaker of the device close. Display on screen of the device contains: protective operation of a series capacitor compensation, operation of over current of MOV in B phase, anti-trigger action of GAP in B phase, command for MOV to protect bypass breaker which is received by platform protection, perpetual blocking signal of a series capacitor compensation

Figure 1. Basic wiring diagram of the series capacitor compensation device.

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Table 1.

have been experimented on MOV of B phase and C phase of the series capacitor compensation device, and records of the 2 years experiment are as follows: From the test results and historical test we can know:

MOV’s parameters.

Rated voltage Maximum continuous operating voltage Overvoltage protection level Unbalanced current The number of paralleling sections in each phase The number per column in a MOV The number of resistance chips in a column Capability of release of pressure Production date

118 kV 94 kV 2.3 p.u. Not exceeding 10% of average current per set 19 (including 2 spinning reserve) 4

1. DC reference voltage of the fault MOV in B phase is less than non-faulty MOV and 0.75 time reference voltage of the fault MOV is increased, all of which indicate the existence of the internal defect that may be do to dampness after rain before the day of the test, and the MOV’s seal suffers damage. 2. 2  mA DC reference voltage of the non-faulty MOV in B phase and C phase reduces by 2%–3% compared to historical test (code requires the value to be no more than ±5%). This test result may be influenced by humidity and test result is not changed after shielding. 3. The value of leakage current under 0.75 times reference voltage of non-faulty MOV in B phase and C phase has a marked increase compared to the historical records. The result relates to field humidity as well as MOV resistor disc’s voltage– current characteristic has negative temperature coefficient in low current area. Because in summer, the test temperature is higher than the previous two times.

24 40 kA + discharge current of capacitor bank In 2000

The main elements of the compensation devices contain: 1, capacitor bank; 2, MOV; 3, trigger gap; 4, bypass switch; 5, damping reactor; 6, insulated platform. The capacitor bank is the main device of the series capacitor compensation device which is used to raise transmission capacity of the transmission line. MOV is used for limiting voltage of the capacitor bank. Trigger gap, which does not have strong capacity of arc blowout and needs to close the bypass switch or trip of line switch to quench the arc, is used to puncture rapidly the bypass capacitor bank and MOV to prevent overheating damage of the MOV and protect capacitor bank from suffering overvoltage damage. Bypass breaker is used for switching series capacitor bank and quenching arc by shorting gap. Damping reactor is used for limiting high amplitude and frequency oscillating current which is from discharge of capacitor bank when breakover of trigger gap due to discharge or bypass breaker is switched on[1]. The key parameters of MOV are shown in Table 1.

3

3.3

1. MOV operates usually under 10% redundance hot bank Ambient temperature, measurement error, unbalanced current, utilization rate, and so on have influence on current distribution and capacity value in the process of manufacture and assembly of MOV’s resistor disc. MOV’s capacitance should consider a certain margin based on calculated value and 20% margin when calculating maximum capacity, and this value is used as rated capacity of resistor disc[3]. In addition, because of probable damage while the MOV is running, replaced new components are difficult to guarantee that its characteristics match with operating components, therefore, considering 10% margin usually in the way of hot bank to guarantee that all MOVs have the same characteristic[4–5]. According to the ability of energy absorption per cubic centimeter from the producer, the value of energy absorption of 17 MOV which combines into a set of device is 56 MJ that meets rated capacity 50 MJ. Considering 10% margin of hot bank, each phase has two MOV sections for spinning operation.

INSPECTION AND ANALYSIS OF FAULT EQUIPMENT

3.1

Visual inspections

From the result of visual inspection, we can see that the MOV has broke down and the large fault current flowed through the MOV that lead to internal high energy burst the bursting disk open so that the operation of pressure release equipment and cause damage of external insulator shed at outlet of releasing pressure. 3.2

MOV design requirement of the series capacitor compensation device

Electrical test

A 2  mA DC reference voltage test and leakage current test under 0.75 times reference voltage

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Figure 2.

Table 2.

Result of fault MOV’s visual inspection.

Test results of the fault series capacitor compensation device.

Test time

Equipment

2 mA DC reference voltage/kV

2014.7.31

Fault MOV of B phase Non-faulty MOV of B phase Non-faulty MOV of C phase Each section of MOV of B phase Each section of MOV of B phase

154 164.1–164.8 163.8–164.4 168–169 169.3–169.8

2014.4.10 2013.3.2

Leakage current test under 0.75 times reference voltage/μA

Field temperature/°C

Field humidity/%

147 104–122 111–126 50–70 45–75

31 31 31 24 15

73 73 73 20 24

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2. Each phase itself should have auxiliary section which does not recommend misuse. From GB/T 6115.2–2002/IEC 60143: 1994 Series Capacitors For Power Systems— Part 2: Protective Equipment For Series Capacitor Banks we know: recommend varistor section should be installed and energized with other sections. This recommendation will guarantee that all varistors, including auxiliary sections, can uniformly age and maintain current distribution deviation. Every phase will have electrical experience, therefore, do not recommend auxiliary sections of different phase misuse. Each phase itself should have auxiliary section. The series capacitor compensation device is operated for more than 10 years, and there is difference for every phase series capacitor compensation device on GAP trigger and artificial earth short circuit test. These fault MOV sections are not recommended to be used for huge different operating experience to avoid triggering new faults.

Table 3.

4

FAULT ANALYSIS

4.1 Protection record wave analysis Protection record is as follows: 18:46:57.574, short circuit grounding fault between A phase and B phase at a line (fault point is 270 km away from the series capacitor compensation) two sets of protective devices for fault line are rapidly operating and switching off the three-phase switch of the series capacitor compensation device; 18:46:57.574, MOV over current protection, MOV unbalanced protection and GAP antitrigger protection in B phase of the compensation device are operating, and three-phase bypass break switch on to stop operation of the compensation device and the device blocks perpetually. SOE incident record of the series capacitor compensation device is shown in Table 3. Fault record wave of the series capacitor compensation device as shown in Figure  3. From the record wave we know:

SOE incident record of fault series capacitor compensation.

Time

Type

Source

Content

(a) A system 18:46:57.596 18:46:57.596

Fault Incident

MOV protection Platform protection

18:46:57.825 18:46:57.686 18:46:57.627

Incident Incident Incident

DI case MOV protection Platform protection

18:46:57.686

Incident

Platform protection

18:46:57.687

Incident

Platform protection

18:46:57.735 18:46:57.738 18:46:57.741

Incident Incident Incident

Platform protection Platform protection Platform protection

1808-MOV over current protection in B phase is operating 134-platform protection receives order for protecting bypass breaker 116-restor trigger signal of platform GAP in B phase 1814-GAP anti-trigger protection in B phase is operating 140-perpetual blocking signal of series capacitor compensation device is effectively input 134-platform protection receives order for protecting bypass breaker 134-platform protection receives order for protecting bypass breaker 126-B phase breaker switches on 128-C phase breaker switches on 124-A phase breaker switches on

18:46:57.596 18:46:57.596

Fault Incident

MOV protection Platform protection

18:46:57.596 18:46:57.596

Incident Incident

MOV protection Platform protection

18:46:57.686 18:46:57.596

Incident Incident

MOV protection Platform protection

18:46:57.597

Incident

Platform protection

18:46:57.716

Incident

Platform protection

(b) B system 1808-MOV over current protection in B phase is operating 134-platform protection receives order for protecting bypass breaker 1820-MOV unbalance protection in B phase is operating 134-platform protection receives order for protecting bypass breaker 1814-GAP anti-trigger protection in B phase is operating 134-platform protection receives order for protecting bypass breaker 134-platform protection receives order for protecting bypass breaker 140-perpetual blocking signal of series capacitor compensation device is effectively input

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1. The maximal current value of B phase is 70700 A (the maximal value before it is 3227  A), and current value is more than 10000  A (constant value of over current) for four consecutive times which lead to operation of MOV over current protection. However, there is no current in GAP of B phase that lead to operation of anti-trigger protection. 2. The value of CT test current for branch 2 of MOV in B phase is about 0.64 A (primary current is 1600 A), unbalance protection in B phase is operating. 3. After operation of MOV over current protection, GAP of B phase of the compensation device is triggered. This fault is actually external (short circuit grounding fault between A phase and B phase), therefore, the voltage between the two poles of GAP in B phase may not be up to trigger voltage so that the GAP does not conduct and the GAP anti-trigger protection is operated. 4. In the process of fault operation, the MOV current value in B phase meets operating condition of MOV over current protection, and the CT does not detect current, and GAP antitrigger protection in B phase is operating. Two branches’ current are critical unbalance in B phase of the series capacitor compensation

Figure  3. Fault MOV current of series compensation capacitor banks.

Figure 4.

device which lead to the operation of MOV unbalance protection. Establishing mold and calculate the voltage and current of the series compensation capacitor banks’ section while simulate the short circuit grounding fault between A phase and B phase. The results are as follows: 1. The peak current of MOV in B phase is 3259.8 A when MOV is running normally (the peak current of fault record wave is 3227  A), and the voltage between the two poles of GAP is 200 kV which is not up to trigger condition of GAP. 2. If in one section of MOV short circuit occurred when external fault current flows through it, the peak current of MOV in B phase is up to 66 kA (the current displayed on the fault wave picture is 70.7 kA), the two poles of GAP are short and the voltage is about 0 which is much less than the value of trigger. 5

CAUSE ANALYSIS FOR FAULT AND RECOMMENDATION

Combining with equipment inspection, fault record wave and results of fault simulation, fault analysis of the series compensation capacitor banks is as follows: 1. Internal defects of fault MOV of the series compensation capacitor banks caused MOV failure when external fault occurs which cause rise of current flowing through MOV. 2. While MOV failure, capacitor banks discharge through a fault MOV which cause discharge current is up to 70 kA that meets the constant value of over current protection (10 kA), however, the two poles of GAP is short and the voltage is about 0 which much less than the value of trigger, therefore, MOV over current protection is operating and GAP protection refuse to operating.

Calculated results of current simulation for MOV external fault.

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with long-time-operating and degraded electrical performance to improve operational reliability of device.

Through inspection of appearance and electrical test and so on, we recommend the following: 1. Considering that there are differences for every phase series capacitor compensation device on GAP trigger and artificial earth short circuit test, do not recommend to use other MOV elements to replace element of this fault MOV in B phase of series capacitor compensation banks. 2. There are 18 MOV sections in B phase can be used (including one auxiliary section), which means the total capacity meet demand, but, considering that there are many similar faults in china, and the MOV for series compensation capacitor banks have operated for many years which may cause the existence of cracking (decline of DC reference voltage). Therefore, we recommend to replace rapidly the fault MOV for series compensation capacitor banks. 3. We cannot to do electric check on series capacitor compensation device because of its particularity, therefore, we advise that replace device

REFERENCES [1] Yang Yu-juan. Development and Characteristics of Metal Oxide Nonlinearity Resistor (MOV) for Series Compensation Capacitor Banks [J]. Insulators and Surge Arresters, 2011, (243):100–106. [2] Deng Yong-jie, Zhang Jie. Analysis of a MOV Internal Fault of Series Compensation [J]. Guangxi Electric Power, 2013, 36(4):72–74. [3] Cheng Zhen-he. Research on the Series Compensation Equipment Optimization in 500 kV UHV System [D]. Nanjing University Of Science and Technology, 2003. [4] Ye Bo, Cheng Zhong-wei, Huang Kang-jia. Analysis and Diagnose of MOV Fault in Series Compensation Station [J]. Guangxi Electric Power, 2010, 133(5):55–57. [5] Chen Xi-peng. Study on Overvoltage of 500 kV Series Compensator [D]. South China University of Technology, 2010.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Experimental study of the human retinal security by LED light Li Zhao, Liu Lu, Shu-Yan Ren, Li-Qing Geng & C.T. Li Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin, China

ABSTRACT: Medical experiments by LED light had been done to explain the influence on visual function. White LED was regarded as light source. Experiments about retinal security of rats had been carried out under different illumination, and test results of flow cytometry, visual electrophysiology, light and electron microscopy were obtained. After 24 hours under 3000 LUX light irradiation, apoptosis rate is 87.6%. After 7 days under 500 LUX light irradiation (alternating light and dark for 12 hours), electroretinography is abnormal. From the test results of light and electron microscopy, the conclusions of retinal damage can also be observed. Experimental study of the human retinal security by LED light will provide theoretical basis to indoor application of LED light. Keywords: 1

ERG; retinal security; flow cytometry; LED light

INTRODUCTION

The sensitivity of the human visual perception to different wavelengths of light is different. In the radiation wavelength range of light diode, radiation energy distribution of each different wavelength will cause adaptive visual response to humans. The illumination of a point on the surface is defined as the ratio of the incident flux and the unit area, and low illumination will cause eye fatigue, and high illumination will cause glare and waste of electricity. Using LED lights, there are two irradiation methods, which are direct and oblique. Under these methods, human visual perception is different, as that under different lengths of exposure time. Studies have shown that prolonged exposure to bright light can cause adverse effects on retinal function. Many scholars used a variety of experimental animals to demonstrate the effects of light on the retina and its mechanism [1–3]. The causes of light impact on the retina are deduced to three aspects, which are thermal damage, mechanical damage and photochemical damage [4–6]. Thermal damage is produced from the high energy absorbed by the tissue, which makes local tissue temperature to increase. When the tissue temperature exceeds a certain limit, all kinds of protein components within the organization produce solidification and conduct damage. Mechanical damage is that tissue receives strong light (such as laser) in a very short time, and organization instantaneous changes by

the photon changing in an instant to mechanical damage tissue. Photochemical damage is pathological changes in retinal tissue which is caused by not obvious temperature rising, low energy and relatively long time illumination. LED semiconductor light-emitting device as the fourth generation of light source will soon be widely used in indoors, which can realize lighting and communication functions at the same time. Exploring healthy and comfortable designing methods of indoor LED lighting, or providing theoretical basis and technical support is important to improve the general health level of human beings and has enormous social benefits. In this paper, white light LED is used as the light source to do medical research to find the LED light source effect on the human visual function. Under different illumination, experiments were made to study retinal safety of rats, and confirmed that the intensity of LED light source has a certain degree of damage to the retina. 2

THE INTERACTION PRINCIPLE OF LIGHT AND BIOLOGICAL TISSUE

Rod cells and cone cells are two types of visual cells in the human eye retina, and these cells have a photosensitive substance called sensitive pigment. Based on the mechanism of interaction between light and biological tissues [7], the visual cells generate pulses by the light. These pulses are posted to the brain’s visual centers by the optic

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nerve. When photosensitive pigments of the visual cells were stimulated by visible light, photons of a certain wavelength would be absorbed. The electronics within the biological molecules jumped to a certain level and caused the variation of the biological molecules. When these effects are not strong enough, damage does not emerge, and some beneficial effects of biological stimuli have been produced. However, when the stimulus is too strong, it will cause irreversible damage to the visual cells [8–9]. In order to assess the extent of the damage, the techniques of Flow Cytometry (FCM), visual electrophysiological analysis and light microscopy have been adopted in this paper to study the reaction of the retina after stimulation by light. 3

BASIC MEDICAL EXPERIMENTS

According to the experimental requirements, cages of rats and light sources are designed. Selecting white LEDs as light source, the light-emitting surface is 400 mm * 600 mm. Unless adding a piece of glass as a scatter to enhance the uniformity of illumination, the method of segmentation controlling is used to ensure the stability of photoluminescence. By 12 hours light and 12 hours dark illumination mode, samples of rats were taken for basic medicine experiment after continuous irradiation for 7 days. A group of rats without irradiation is selected as normal control group subjected to the same medical experiments. 3.1

Apoptosis in normal control group.

Figure 2.

3000 LUX light cell apoptosis in 24 hours.

Flow cytometry

FCM is a detection method for quantitative analysis and separation of single cells or other biological particles in function level [10]. It can analyze tens of thousands of cells by high-speed and get more parameters simultaneously from one cell. Compared with the conventional fluorescent microscopy, FCM has the advantages of high speed, high precision, and so on. The results of cell apoptosis obtained by FCM are shown in Figures 1 and 2. Apoptotic response is a kind of characteristic changes that occur in the level of the retinal cells, sub-cellular and molecular. Apoptosis in the control group was 2.44%, while in the 3000 LUX light irradiation after 24 hours, apoptosis was 87.6%. So damage is very greatly under the condition of the irradiation. 3.2

Figure 1.

Visual electrophysiology

After the human eye’s retina is stimulated by light, photochemical and photoelectric reactions will

occur in the visual receptors, which produce potential changes and form nerve impulses passing to bipolar cells, ganglion cells, optic nerve, optic chiasm, optic tract, and lateral geniculate body. Optic radiation terminates in the cerebral cortex of the calcarine visual cortex. After the retina is stimulated by the full-field (Ganzfeld) flash, the sum of electrical responses of the retina neurons and nonneuronal cells is recorded by cornea. It represents the electrical activity of the retinal cells of every layer. Diagram of electroretinogram (ERG) [11,12] is shown in Figure 3.

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Table 1.

Dark-adapted rod reaction.

b wave latency B wave amplitude

Figure 3.

Schematic diagram of ERG component.

Table 2.

The common clinical abnormal ERG is mainly embodied in five different types. The amplitude of B wave of hypertypic ERG exceeds the normal average value over 30%. a and b wave amplitude of low ERG decreases, and the range should be lower than the normal average value over 30%. For the negative wave of ERG, a wave is deep and wide, and b wave is small or disappeared. For delayedtype ERGs, a and b wave amplitude is normal, but the peak time will delay by standard flash recording. The composition of flat type ERG will be submerged in the baseline noise. In the visual electrophysiological experiments, RolanConsult’s eye electrophysiological systems and Retisystem 2.26 software are used to measure ERG. Corneal electrode and the reference electrode and the ground electrode are made of stainless steel needle. Comparison of visual electrophysiological parameters in the normal control group and the group illuminated by 500  LUX continuous light for 12 hours is shown in Tables 1 and 2, in which the incubation period unit is ms and amplitude of the unit is mv. Compared with the normal control group, retinal electrophysiology dark adaptation biggest mixed reaction a and b wave latent period of rats in 500 LUX light group were basically not changed, but the amplitude decreased, and the rest of the wave amplitude decreased. Dark adaptation rod response b wave amplitude decreased by 45%, and the largest mixed dark-adapted response of b wave amplitude decreased by 14%, and a wave amplitude decreased by 41%. Experimental results show that the ERG of the rats in 500 LUX light group is abnormal. 3.3

Light microscopy and electron microscopy experiments

Dying characteristics of light microscopic examination of biological tissue is used to show the morphological structure of tissues and cells, and to study the pathological changes of the disease and the development process. Normal rat retinal

Normal control group

500 LUX light group

67.87 ± 6.63 86.00 ± 9.01

442.13 ± 40.84 244.00 ± 29.83

Dark-adapted largest mixed reaction.

b wave latency b wave amplitude a wave latency a wave amplitude Ops total amplitude

Normal control group

500 LUX light group

66.67 ± 5.55 674.00 ± 32.12 16.33 ± 2.24 242.47 ± 28.67 325.07 ± 35.71

66.29 ± 5.96 402.00 ± 25.10 17.43 ± 1.62 142.29 ± 18.42 142.57 ± 17.65

Figure 4. HE staining pattern of normal control group.

outer nuclear layer nucleuses arrange regularly, and the thickness is an average of about 10∼12 nucleuses. The inside and outside section structure of photoreceptor cells is clear and colored uniformly, HE staining of the control group is shown in Figure  4. Under 2000  LUX illumination, the outer nuclear layer cell nucleus became thin, and arranged loosely, and the thickness is an average of about 2–4 cells, as shown in Figure 5. Experimental results show that the apoptosis of the retinal photoreceptor cells increases in the light stimulation, so the thickness of the outer nuclear layer is reduced. Electron microscopy is a typical method to observe the cell morphology, by which micro structure of the nucleus is clear. Therefore, combination of the electron microscope and digital image processing system can be adopted to

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detect more subtle changes. Electron microscopy results of the experimental group and the control group under 2000  LUX illumination are shown in Figures 6 and 7, and the injured parts revealed myelin structure, cell gap is mild to moderate edema and a small amount of cystic cells are expanse. 4

Figure 5.

CONCLUSION

From the test results of FCM, visual electrophysiology, optical microscopy and electron microscopy, compared with the normal control group, the retina of rats presented different levels of apoptosis under different illumination of light source from 500  LUX to 3000  LUX by the fixed exposure mode. Therefore, the LED source is subject to be strictly tested before entering the indoor lighting, and brightness should be controlled in a certain range.

HE staining under 2000 LUX illumination.

ACKNOWLEDGEMENTS The National Natural Science Fund Project “LED light source of the retina medical security and visual function influence mechanism research” (No. 61178081). The Research and Development fund “Special LED lighting and control system design for plant growth” (No. KJY11-10). Figure 6. Electron microscopy results of normal control group.

REFERENCES

Figure  7. Electron microscopy test results of control group under 2000 LUX illumination.

[1] Desmet K.D., et al. 2006, Clinical and experimental applications of NIR-LED photobiomodulation [J]. Photomed Laser Surg, 24(2): 121–128. [2] Jeong Jun Lee. 2005, Three dimensional eye gaze estimation in wearable monitor environment [D], Yonsei University. [3] Suzdaļenko A., Galkins I. 2010, Research and Development of LED Driver with Power Line Communication for Intelligent Lighting [J], Proceedings of NEXT 2010, Finland, Turku, 78–85. [4] Komine Toshihiko, Haruyama Shiniehiro, Nakagawa Masao. 2005, A study of shadowing on indoor Visible-light wireless communication utilizing Plural white LED lightings [J]. Wireless Personal communications, 34(l–2): 211–22. [5] Penn J.S., Anderson R.E. 1991, Effect of light history on the rat retina [J]. Prog Ret Res, 11:75. [6] Young R.W. 1988, Solar radiation and age-related macular degeneration [J]. Sury Ophihalmol, 32:252. [7] Ash C., Town G. and Bjerring P. 2008, Relevance of the structure of time-resolved spectral output to light-tissue interaction using intense pulsed light [J]. Lasers Surg Med, 40(2): 83–92.

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[8] Dall Agnol, et  al. 2009, Comparative analysis of coherent light action (laser) versus non-coherent light (light-emitting diode) for tissue repair in diabetic rats [J]. Lasers Med Sci, 24(6): 909–916. [9] Leal Junior E.C., et al., 2009, Effect of cluster multidiode Light Emitting Diode Therapy (LEDT) on exercise-induced skeletal muscle fatigue and skeletal muscle recovery in humans. Lasers Surg Med, 41(8): 572–577. [10] Wu Xiaona and Jiang Hongbing. 2011, Working principle and clinical application of flow cytometry, Chinese medical equipment, 26(3): 91–93.

[11] Li Qiaolian, Tang Renhong and Yi Junhui. 2013, Application ERG assess changes in retinal function with high myopia, Chinese and foreign medical research, 11(12): 151–153. [12] Wang Hui Wu Xingwei Zhu Jianfeng Chen Fenge Wu Ying Sun Yong Gong Yuanyuan. 2008, Determination of [J]. high myopia macular retinal thickness and multifocal ERG. Journal of Optometry. 10 (05): 332–334.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis of the phenomenon in LFO of AC electric locomotive J.J. Ding, Y. Tu & S.B. Gao School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

ABSTRACT: In order to analyze the mechanism and key factors of the stability of the traction power supply system and the high-power electric traction system which is influenced by Low Frequency Oscillation (LFO), this article uses the Matlab/Simulink simulation software to build the locomotive–traction network coupling model which contains HXD3 locomotives and traction network. By changing the length of traction power supply arm, the number of locomotives and the condition of locomotive in full-load or un-load, we can get different simulation results under various conditions. Combining the actual measure results and simulation results, we can find the main reasons which cause the LFO of the electric locomotive and traction network. If we let the length of the power supply section become longer, the oscillation will be more obvious. Besides, the number of the locomotives which is running together can determine whether the oscillation is shown. The locomotive and traction network LFO will appear when the locomotive is only in full load operation but not in no load operation. Keywords: 1

traction network; electric locomotive; LFO; stability analysis

INTRODUCTION

2

For rapidity and traffic density of trains on highspeed passenger special railway, it may exist the possibility that many locomotives are accessing the traction network at the same time in the same railway section. It leads to the problem that the coupling relationship between the electric locomotive and traction network becomes more and more serious. The spreading of the harmonic power flow in the traction network may cause harmonic resonance between the locomotive and traction network. On December 26, 2007, there were many locomotives affected by the Low Frequency Oscillation (LFO) of the traction network-voltage when they are servicing together in the Hu Dong locomotive depot, and the locomotives could not work normally because of the traction blockade. It caused the locomotives can’t entry and out of the garage normally and disturb the traffic order seriously. In this paper, through the establishment of the all-parallel AT traction network model and the multi HXD3-locomotives model, using the Simulink simulation tool, the different conditions of the LFO which is influenced by the different length of power supply section and different working conditions about one or several locomotives were simulated and analyzed. According to the simulation results, we can reproduce the accident in the Hu Dong locomotive depot and find the main reasons which cause the LFO of the electric locomotive and traction network.

THE MODELING OF AT TRACTION NETWORK

In the traction network, for the AT power supply mode has the advantage that not only can reduce the interference from electrified railway to communication line but also other supply systems don’t have, so it is used by many countries. The three lines equivalent AT traction power supply mode in this paper includes contact wire, messenger wire, return line, rail and positive feeder. The contact wire and messenger wire can be simplified as traction line (T line). The return line and rail can be simplified as return circuit line (R line), and the positive feeder line is recorded as “F line”. We can establish the traction network simulation module with the line model provided by Matlab, the “Series RLC Branch” module and “Mutual inductance” module in “SimPowerSystems” that

Figure 1.

The AT traction network model.

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can, respectively, represent self-impedance and mutual impedance. The AT transformer can be realized by single-phase and two-winding transformer. Finally, the AT traction power supply system with mutual inductance can be established according to the requirements of the prototype system. The model is shown in Figure 1. 3

THE MODELING OF HXD3 LOCOMOTIVE

The simulation model of a locomotive in this paper is established with reference to the main circuit of China Railway HXD3 (HXD3). The model includes the main traction transformer, two-level rectifier bridge, modulation wave generation module, PWM control signal module, the intermediate DC voltage link, the second filter, and variable resistance load. Because the phenomenon of LFO of electric locomotive and traction network has close ties to the rectifier circuit but not to the inverter circuit, the integral part of the locomotive model is rectifier circuit and we can simplify the part of inverter circuit and traction motor as a variable resistance load. The final locomotive model is shown in Figure 2. The parameters of the rectifier circuit model established in this section are as shown in Table 1. 4

THE SIMULATION OF LOCOMOTIVE AND TRACTION NETWORK COUPLING SYSTEM

Through the simulation  of locomotive–traction network system under different conditions, we

observe the phenomenon of LFO.  And through the analysis and comparing of the different simulation results, we can find the reasons and the related factors which lead to the LFO. For the traction network, it is important to analyze the influence of the current and voltage waveform of locomotive and traction network which is caused by the change of the traction power supply arm length from 15 km to 45 km. For the locomotive, this article analyzes the current and voltage waveform of the locomotive and traction network when there are different number of locomotives accessing the traction network, such as one, two, and seven locomotives. Besides, through change the working state, change from full-load state to no-load state, we can observe the appearance of the LFO. 4.1

The simulation of one locomotive working

When only one locomotive is working on the traction network which has the length of power supply arm about 15 km and 45 km, the traction current and voltage simulation waveform are shown in Figures 3 and 4. It is easily to find that no matter the length of power supply arm are 15 km or 45 km, it can’t lead to the appearance of the LFO when single locomotive access the traction network. 4.2

The simulation of two locomotives working

Let the two locomotives access the traction network in turn, setting the length of the power supply arm as 45 km and measure the network current

Figure 3. Current and voltage waveform of single electric locomotive with 15 km power supply arm. Figure 2.

The simulation model of electric locomotive.

Table 1.

The parameters of the model.

Rated capacity Rated voltage input Rated frequency input Rated voltage output Power factor

1400 kVA 1400 V 50 Hz 2800 V ≥98%

Figure 4. Current and voltage waveform of single electric locomotive with 45 km power supply arm.

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and voltage. The TIME module of the circuit breaker is set as 0.5 s, which simulate that one locomotive access to the system at the first, but another access to the system delaying 0.5 s. The simulation waveform is shown in Figure 5. According to the result of the simulation, the traction voltage waveform appears as sharp fluctuations when the second locomotive accesses the network. So, we can find that when the locomotive accesses the network, the traction voltage waveform will appear as oscillation lasting about 0.5 s and the difference between the maximum and minimum value is about 10,000 V. From 1.4 s, when the system become stable running, the voltage and current appear the light LFO with the amplitude of 1000 V and the frequency of 6 Hz. Therefore, the accessing of the two locomotives will make the system become unstable and lead to the LFO. What is more, more and more locomotives access the traction network, the phenomenon of the LFO becomes evident. 4.3

Figure 6. Current and voltage waveform of seven fullload electric locomotives with 15 km and 45 km power supply arm.

The simulation of multi-locomotives working

For reproducing the field conditions accurately, the paper changes the number of locomotives from one to seven to observe the waveform. In this section, using the seven locomotives as the major subject, we can change the power supply arm from 15 km to 45 km and change the locomotive condition from full-load state to no-load state, and get different simulation results. Setting the locomotive accessing time as 0, 0.25 s, 0.5 s, 0.75 s, 1 s, 1.25 s, 1.5 s. When the locomotives are running full-load, the resistance R of the DC circuit is set to the value about 6.8Ω that is equal to the value when the inverter circuit is running full-load. Simulating and analyzing the case that the length of power supply arm equal to 15 km and 45 km, respectively. Figure  6  shows the current and voltage waveform of the traction when the length of the power supply arm is 15  km and 45  km. It is very clear that the value of current and voltage is stable, the

Figure 5. Voltage waveform of two electric locomotives with 45 km power supply arm.

Figure  7. Current and voltage waveform of seven  noload electric locomotives with 15 km and 45 km power supply arm.

harmonic content of the circuit is low and the phenomenon of LFO does not appear. So, we can understand that the full-load locomotive cannot cause the LFO. When we change the working state of the locomotives to no-load, and set the value of the load resistance as 10,000 Ω, the waveform is shown in Figure 7. The above waveform shown in Figure  7 is setting the power supply arm as 15 km, and another is setting the power supply arm as 45 km. It is easy to find that LFO appears in the two systems after 3 s. The longer the power supply arm length is, the more increasing the amplitude of the oscillation is. We can analyze the condition that seven locomotives are running together and the length of the power supply arm is 45 km in detail. Figure  8  shows the traction voltage and current waveform with 45  km power supply arm

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Figure  8. Current and voltage not steady waveform of seven electric locomotives with 45  km power supply arm.

Figure  9. Steady voltage waveform of seven electric locomotives with 45 km power supply arm.

length when seven locomotives are instable running. From 0 s to 1.5 s, the locomotives access the traction network one by one. Before 3  s, the net voltage fluctuation is very large and the difference between the maximum and minimum value is about 25,000 V. The overvoltage may lead to the blockade of the traction during the time. After 3  s, the voltage waveform of traction appear the oscillation as shown in Figure 9. The amplitude is about 2000V and the frequency is about 6 Hz, so it is easy to see that it is similar to the waveform of the field accident which belongs to the LFO. 5

more the number of locomotives, the more obvious the phenomenon of the LFO. When the locomotives access the traction at the same time, the current of the locomotives is in the same phase and make synchronous change. At this time, the locomotive and traction system turn to synchronous state. On the one hand, the superposition of the current of synchronous oscillation makes the traction voltage oscillate; on the other hand, the oscillation of the voltage aggravates the oscillation of the current similarly. It leads to the condition of the LFO. 2. Comparing the waveform when the power supply arm length is 15 km or 45 km. The length of the traction power supply arm determines the values of the distributed capacitance, unit impedance, and unit inductance. So, it is the important factor which influences the LFO. The longer the length of the traction power supply arm is, the higher the values of distributed capacitance, unit impedance and unit inductance are. The amplitude of the LFO voltage is larger. 3. Analyzing the simulation results when seven locomotives access the traction network, we can find that the LFO disappears when the locomotives are in full-load. On the contrary, the running of the no-load locomotives may cause the appearance of the LFO.

ACKNOWLEDGEMENTS The authors acknowledge  the support  of  the National Natural Science Foundation of China (Grant Nos 50907055 and U1134205).

SUMMARY

This paper uses the traction power supply system and transmission system as the major research object based on the Simulink software. By changing the length of traction power supply arm, the number of locomotives and the condition of locomotives in full-load or un-load, we get the different simulation results under various conditions and analyze the necessary condition leading to the LFO. We can reach the conclusions as follows: 1. Through analyzing the field accident, we can find that the phenomenon of LFO is caused by the condition that multi-locomotives are running together. Using the simulation software to simulate this condition, it can be found that at least two locomotives access the traction which will lead to the light LFO, and the

REFERENCES [1] Shibin Gao. Study on novel protective schemes of traction power supply systems for high speed railways [D]. ChengDu Southwest Jiaotong University, 2004:14–37. [2] Qionglin Zheng. Cause analysis and countermeasures of the HXD1 AC locomotive harmonic oscillation [J]. The World of Inverters, 2009, (05):44–45. [3] Zhihua Yang, Liqin Zhu. Cause analysis and countermeasures of traction harmonic of AC locomotives [J]. Electric Locomotives & Mass Transit Vehicles, 2003, 26(02):8–11. [4] Haitao Hu, Min Zhang, Chenghao Qian, Lei Fang. Research on the Harmonic Transmission Characteristic and the Harmonic Amplification and Suppression in High-Speed Traction System [J]. Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific.

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[5] Qiling Yang. The Coupled oscillation between catenary system and the traction drive system in High-Speed Train [D]. Beijing Jiaotong University, 2012, 06. [6] Chaopeng Zhao, Xudong Tian. The Influence of AC-DC-AC electric traction harmonic [J]. Electric Railway, 2010, (03):6–8. [7] Erik Mollerstedt. Out of control because of harmonics-an analysis of the harmonic response of an inverter [J]. Control Systems, IEEE, Aug. 2000:70–80. [8] B. I. Khomenko, K. N. Suslova. Unified converter for electric locomotives of direct and alternating current [J]. Russian Electrical Engineering, 2009, Vol.80 (6)

[9] Wensheng Song, Zhiming Liu, Xiaoyun Feng. Controling and Simulation of 4-Quadrant Converter [J]. Electric Locomotives & Mass Transit Vehicles, 2007, 30(02):34–37. [10] Katsuhiro Shimada. A control method of matrix converter for plasma control coil power supply [J]. Fusion Engineering and Design, 2007, 82(5). [11] L. Malesani and P. Tomasin. “PWM current control techniques of voltage source converters-A survey”. Proc. IECON’93.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The prototype of fatigue damage detection by the method of Metal Magnetic Memory Testing X.Y. Yu Harbin Institute of Technology, Harbin, Heilongjiang, China

X.Y. Fu Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China

ABSTRACT: Metal Magnetic Memory Testing (MMMT) is one of the most potential non-destructive testing technology, which can effectively distinguish the stress concentration zone. And in most situations, it can be used to provide early diagnosis and dynamic analysis for casing damages. Based on this method, we create an integrated prototype equipment for detection. And the prototype is proven to be effective and reliable through the experiment analysis. Keywords: Metal Magnetic Memory Testing (MMMT); fatigue damage detection; anti-interference circuit; data analysis based on the Matlab 1 1.1

INTRODUCTION General introduction

In the last few years, the non-destructive testing technology has a tremendous development. But the traditional non-destructive testing technology still has some limitations, such as some requirements of the surface pretreatment or excitation source device. With the increasing industrial level, a new testing technology by metal magnetic memory is gradually discovered and becoming the mainstream. It’s all based on the own magnetic leakage field, caused by the geomagnetic field and the magnetostriction in ferromagnetic products. And the abnormal magnetic signals can be detected on the defect location. Based on the principle of MMMT, we have created an integrated prototype. We use PCB to support and integrate different kinds of functional modules. The magnetoresistive sensor with high sensitivity is used for collecting the weak magnetic signals. And we also design the set/reset circuit and electromagnetic shielding to achieve anti-interference. The lownoise amplifier circuit amplifies the weak signals. The PIC controls the data after A/D conversion in order to achieve the preservation, communication and some other extensible functions. 1.2

Organization

In Section II, we will show the system design block diagram of prototype and the detailed workflow.

In Section III, we will explicate the general design of some main modules. In Section IV, we will make a GUI program as a display by Matlab software and use it to prove the reliability of the prototype through the experiment. 2

SECTION II

First of all, the magnetic signal above the surface of ferromagnet can be detected by the magnetoresistive sensor, and fast converted into the analog electrical signal. Then the new signal will be transmitted to the signal processor through the filtering and amplifying circuit. And the processing signal can be sampled by the data acquisition part, which has a built-in 24-bit high-precision ADC. Finally, the electrical signal is restored to the magnetic signal, and transmitted to the host directly through RS485 communication for the further signal processing in GUI. 3

SECTION III

3.1 The sensor circuit Because the working environment is under the weak magnetic fields, we can choose the magnetoresistive sensors with the type of Honeywell HMC1001/1002. Configured as a 4-element wheatstone bridge, these types of magnetoresistive sensors convert magnetic fields into a differential

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Figure 1.

(a) Design principle diagram.

Figure 1.

(b) The entity of detector part.

Figure 3.

The set/reset circuit.

Figure 4.

The filtering and amplifying circuit.

a magnetic switching technique can be applied to the MR bridge that will eliminate the effect of the magnetic history. The Set/Reset strap can restore the MR sensor to its high sensitivity state for measuring magnetic fields. This is done by pulsing a large current through the S/R strap. The circuit in Figure  3  generates a strong set/ reset pulse ( 4 Amp) under microprocessor control. The S/R signals are generated from a microprocessor and control the P/N channel HEXFET drivers (IRF7105). The purpose of creating the TRS and the TSR delays are to make sure that one HEXFET is off before the other one turns on. Basically, a break-before-make switching pattern. And the current pulse is drawn from the 4.7 μF capacitor. 3.3 The filtering and amplifying circuit Figure 2.

The sensor circuit.

output voltage, capable of sensing magnetic fields as low as 30  μgauss. This MR offer a small, low cost, high sensitivity and high reliability solution for low field magnetic sensing. 3.2

In the filtering circuit part, we decide to choose the passive-RC low-pass filter, which makes the better effect. And in the amplifying circuit part, we choose the AD8221 chip to amplify the signal. The Figure 4 shows the detailed design of circuit. 4

The set/reset circuit

Most low field magnetic sensors can be affected by the large magnetic disturbing fields, which may lead to output signal degradation. In order to reduce this effect and maximize the signal output,

EXPERIMENT

We use a 3 m iron pipe with one fatigue damage for the experiment. The damage is artificial and typical. The Figure 5 (a) and (b) show the waveform of the magnetic field intensity and gradient.

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of Metal Magnetic Memory Testing (MMMT) effectively. And based on this method, we have created the prototype for detection. Finally the prototype is proven to be effective and reliable through the experiment.

REFERENCES

Figure 5. (a) The magnetic field intensity. (b) The magnetic field gradient.

We can see that the abnormal magnetic signals occur once at the same location, where is just same as the real damage point. We repeat this experiment many times, and finally get the same result. Therefore, we can prove the reliability of the prototype. 5

CONCLUSION

The stress concentration zone on the ferromagnetic object can be distinguished by the method

[1] Dubov A.A. Principal Features of Metal Magnetic Memory Method and Inspection Tools as Compared to Known Magnetic NDT Methods [C]. Proceedings of the 16th Annual World Conference on NonDestructive Testing, 2004 (9): 112–114. [2] Wang B.B, Liao C.R, Shi X.C. Study on Magnetic Flux Leakage Model and Experimental Apparatus of Metal Magnetic Memory Testing [C]. International Symposium on Test Automation and Instrumentation, 2010 (4): 1497–1503. [3] Tanasienko A.G, Suntsov S.I, Dubov A.A. Monitoring Chemical Plant by A Metal Magnetic Memory Method [J]. Chemical and Petroleum Engineering, 2002 (38): 9–10. [4] Dubov A.A, Kolokolnikov S, Evdokimov M. Estimation of Gas and Oil Pipelines Condition Based on the Method of Metal Magnetic Memory [C]. 17th World Conference on Nondestructive Testing, 2008: 35–38. [5] Lin J.M, Lin F.B, Lin C.J, et al. Nondestructive Testing New Technology of 21 Century Magnetic Memory Metal Diagnostic Technique [J]. Proceedings of III International Symposium on Tribo-Fatigue, 2010: 648–650. [6] Analog Devices. ad8221 datasheet, 1–22. [7] HMC1001 datasheet, 1–15.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Rotor composite faults diagnosis of asynchronous motor based on complex power Zhen Wang, Zhi-Fang Lin & Cheng Li Huazhong University of Science and Technology, Wuhan, Hubei, China

Zhi Zeng Xianning Vocational Technical College, Xianning, Hubei, China

ABSTRACT: In order to diagnose rotor composite faults of a broken bar and air-gap eccentric in Squirrel Cage Induction Motors (SCIMs), an approach was proposed based on the complex power modulus. Complex power modulus (apparent power) of SCIM’s stator was derived on the condition of normal motor and the rotor composite faults. Theoretical analysis indicates that the fundamental component is eliminated in apparent power, and broken bar and air-gap eccentric fault characteristic frequency are gained, which is beneficial to the separation and diagnosis of mixed faults. Experimental results demonstrate that the amplitude of fault characteristic frequency is high in the early fault diagnosis, which shows the approach is effective to rotor composite faults of early fault diagnosis. The amplitude of 2sf1 raises linearly along with the increase of broken bar for the case of the rotor composite faults, which is useful to the quantification of the fault extent. Keywords: Squirrel Cage Induction Motors; the rotor composite faults; apparent power; the fault characteristic frequency; on-line monitoring 1

INTRODUCTION

Squirrel Cage Induction Motors (SCIMs) have been widely applied to the industrial production system. Motor faults always result in paralysis of production line, which causes great economic loss beyond the value of the motor itself [1–3]. Broken rotor bars and air-gap eccentricity are the typically common faults, which accounts for a large part of total failures in SCIMs [4–6]. Motor current signature analysis is one of most widely used to diagnose SCIMs, due to its advantages that it

can be made into a non-immersion and suitable for online monitoring. For broken rotor bars and air-gap eccentricity, the fault characteristic components near fundamental frequency appear in the stator current. In the traditional spectrum analysis of SCIM, the fault characteristic frequency of a broken rotors bar fault is easily submerged by the fundamental frequency, thus it is difficult to distinguish the fault [7], as is shown in Figure 1. The study of single fault is currently more and the study of composite fault is few at home and abroad. In this paper, the fault characteristic frequency of rotor composite faults was extracted based on the complex power modulus (apparent power). The approach separates characteristic frequency of the rotor composite faults and, therefore, it is a novel and effective method. 2

APPARENT POWER SIGNAL ANALYSIS FOR ROTOR COMPOSITE FAULTS

2.1 Complex power Complex power is defined as [11,12] s

Figure  1. Spectrum of stator current for the case of three broken bars.

 * (uα + ju ui juβ )(iα jjiiβ ) = (uα iα + uβ iβ ) + j (uβ iα uα iβ )

(1)

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2.3

Eq. (1) can be expressed as s = p + jq ,

(2)

where p is active power, and q is reactive power. Clearly, the apparent power is expressed as s= 2.2

p2 + q 2 .

2s

1 ⎤ 2 ⎥ ⎥. 3⎥ − 2 ⎥⎦ −

ub uc

U cos ωt 2π ⎞ ⎛ U cos ωt − ⎟ ⎝ 3 ⎠ 2π ⎞ ⎛ U cos ωt + ⎟ ⎝ 3 ⎠

3 U cos ωt 2 3 U sin ωt 2

uβ

3 I1 cos(ωt ϕ ) 2 3 I1 sin(ωt ϕ ) 2

iβ

(5)

3 UII1 . U 2

(10)

(6)

(7)

(8)

Equation (8) indicates that the spectrum of the apparent power has only DC component under normal condition.

∞

∑ {Il ,k c ⎡⎣( − k b )t − ϕ l ,k ⎤⎦ k =1 + I r,k cos ⎡⎣(ω1 + kω b )t ϕ r ,k ⎤⎦}

I cos(ω t

+

)

∞

∑ {Il ,m cos ⎡⎣(ω

m =1

where I1 denotes phase current and ϕ denotes phase difference. Combining (6) and (7) gives s0

(9)

where m is 1, 2, 3…, and s is the rotational frequency of the motor. When broken bars and eccentricity appear simultaneously, currents in stator current is [13–14] ia

and the stator currents in two-phase static coordinates are iα

kf kf b

f1 ± mffr

fecc

where U denotes phase voltage. Using the coordinate transformation matrix, the stator voltages can be expressed in two-phase static coordinates as follows: uα

ksf k sff1 = f1

(4)

The stator voltages in three-phase coordinates are expressed as ua

f1

where k = 1, 2, 3…; f1, 50 Hz, is power supply frequency, and s is the slip of the motor. For air-gap eccentric of SCIM, the fault characteristic frequency appears in the stator current spectrum [13]

The coordinate transformation matrix is

C3s

For a broken rotor bars fault of SCIM, the fault characteristic frequency appears in the stator current spectrum [13].

(3)

The expression of apparent power under normal condition

1 ⎡ 1 − 2⎢ 2 ⎢ = 3⎢ 3 ⎢⎣0 2

The expression of apparent power for the case of the rotor composite faults

+ I r,m cos ⎡⎣(ω1 +

mω r ) t − ϕ l ,m ⎤⎦

)t − ϕ r,m ⎤⎦}

2π ⎞ ⎛ ib = I1 cos ω1t − ϕ − ⎟ ⎝ 3 ⎠ ∞ ⎧ 2π ⎡ + ∑ ⎨I l ,k cos ( 1 − )t − ϕ l ,k − 3 ⎤⎥ ⎣ ⎦ k =1 ⎩ ⎫ 2 π ⎡ ⎤ + I r,k cos (ω1 kω b )t − ϕ r ,k − ⎬ 3 ⎥⎦ ⎭ ⎣ ∞ ⎧ 2π ⎡ + ∑ ⎨I l ,m cos ( − )t − ϕ l ,m − 3 ⎤⎥ ⎣ ⎦ m =1 ⎩ 2π ⎤ ⎫ ⎡ + I r,m cos (ω1 + mω r ) t − ϕ r,m − ⎬ 3 ⎥⎦ ⎭ ⎣ 2π ⎞ ⎛ ic = I1 cos ω1t − ϕ + ⎟ ⎝ 3 ⎠ ∞ ⎧ 2π ⎤ ⎡ + ∑ ⎨I l ,k cos (ω1 − kω b )t − ϕ l ,k + 3 ⎥⎦ ⎣ k =1 ⎩ 2π ⎤ ⎫ ⎡ + I r,k cos (ω1 + kω b )t − r,k + ⎬ 3 ⎥⎦ ⎭ ⎣ ∞ ⎧ 2π ⎡ + ∑ ⎨I l ,m cos ( − )t − l ,m + 3 ⎤⎥ ⎣ ⎦ m =1 ⎩ 2π ⎤ ⎫ ⎡ + I r,m cos (ω1 − mω r )t − ϕ r ,m + ⎬ 3 ⎥⎦ ⎭ ⎣ (11)

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where ω1 ± kω b = 2π f1 ± kffb ) , ω1 ± ω r = 2π (f1 ± mfr), I l ,k , and ϕ l ,k are, respectively, the peak value and phase of ω1 kω b ; I r,k and ϕ r,k are, respectively, the peak value and phase of ω1 + kω b ; I l ,m and ϕ l ,m are, respectively, the peak value and phase of ω1 ω r; I r,m and ϕ r,m are, respectively, the peak value and phase of ω1 + ω r . Using matrix (4), currents of stator current in two-phase static coordinates can be gained easily iα

3

3 I1 cos(ω1t ϕ ) 2 ∞ 3 +∑ I l ,k cos ⎡⎣(ω1 kω b )t − ϕ l ,k ⎤⎦ k =1 2

}

+ I r,k cos ⎡⎣(ω1 + kω b )t − ϕ r,k ⎤⎦ +

∑

m =1

{

3 I l ,m cos ⎡⎣(ω1 − mω r )t − ϕ l ,m ⎤⎦ 2

+ I r,m cos ⎡⎣(ω + k iβ

r )t

EXPERIMENT RESULTS

Experiments are performed on a three-phase SCIM, Y100  L1–4. A rated mechanical load was supplied with a separately excited 2.3  kW DC generator. Motor rated speed is 1440 r/min, and rated load is 2.3 kw. At the same time, rated voltage is 380 V and rated current is 5  A. Validating the effectiveness of this approach, different motor fault cases have been set. The cases contain normal rotor with eccentricity, one broken bar with eccentricity, two broken bars with eccentricity, as well three broken bars with eccentricity. The following results can be received from Figures 2–6.

{

∞

where sd is DC component, skω b is frequency kω b in apparent power due to the fault frequency of broken bars, and smω r is frequency mω r in apparent power due to the fault frequency of air-gap eccentric. For rotor composite fault, the fault characteristic frequency of broken bars is kω b , and air-gap eccentric is mω r , which is useful to the separation and diagnosis of a rotor composite fault.

}

− ϕ r,m ⎤⎦

3 I1 sin(ω1t ϕ ) 2 ∞ 3 +∑ I l ,k sin si ⎡⎣(ω1 kω b )t − ϕ l ,k ⎤⎦ k =1 2

{

}

+ I r,k si ⎡⎣(ω1 + kω b )t − ϕ r,k ⎤⎦ ∞ 3 +∑ I l ,m sin ⎡⎣(ω1 − )t − ϕ l ,m ⎤⎦ m =1 2

{

}

+ I r,m si ⎡⎣(ω1 + kω r )t − ϕ r ,m ⎤⎦

(12)

Combining (6), (12), and (1), active and reactive power can be obtained p

3 ⎧ U ⎨I1 cosϕ 2 ⎩

∞

(k ∑ ⎡⎣Il ,k cos(k k =1

bt +

l ,k )

Figure 2. Spectrum of complex power modulus for the case of eccentricity fault.

∞

∑ ⎡⎣

+ I r,k cos( kω bt − ϕ r,k )⎤⎦

c ( ,m cos(

r

ϕ l ,m )

m=1

⎫ + I r,m cos(( kω rt − ϕ r ,m )⎤⎦ ⎬ ⎭⎪ ∞ 3 ⎧ q U ⎨I1 si ϕ ∑ ⎡⎣I l ,k sin i kω bt + ϕ l ,k 2 ⎩ k =1

(

)

∞

− I r,k si ( kω bt − ϕ r,kk )⎤⎦

∑ ⎡⎣Il ,m si

( m rt

l ,m )

m=1

}

− I r,m sin( mω rt − ϕ r ,m )⎤⎦

(13) From (3) and (13), apparent power for the case of the rotor composite fault can be expressed as sb = sd + skω b + smω r

(14)

Figure 3. Spectrum of complex power modulus for the case of one broken bar and eccentricity.

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to the separation and diagnosis of mixed faults. Especially, the amplitude of the fault characteristic frequency is high in the early fault diagnosis, which shows the approach is effective to rotor composite fault of early fault diagnosis. The amplitude of 2sf1 raises linearly along with the increase of the broken bar for the case of the rotor composite fault, which is beneficial to the quantification of the fault extent, as is shown in Figure 6. 4 Figure 4. Spectrum of complex power modulus for the case of two broken bars and eccentricity.

Figure 5. Spectrum of complex power modulus for the case of three broken bars and eccentricity.

CONCLUSION

In this paper, an approach is proposed based on apparent power for detecting rotor composite fault of the broken bar and air-gap eccentric. Theoretical analysis and experimental results indicate that broken bar and air-gap eccentric fault characteristic frequency are, respectively, kfb and mfr in the spectrum of apparent power, thus the characteristic frequency of the rotor composite fault is separated. The amplitude of the fault characteristic frequency is high in the early fault diagnosis, for example, one broken bar, which shows the approach is effective to rotor composite fault of early fault diagnosis. The amplitude of 2sf1 raises linearly along with the increase of the broken bar for the case of the rotor composite fault, which is useful for the quantification of the fault extent and construction of diagnostic rules. ACKNOWLEDGMENT The authors wish to acknowledge the contribution of Zhi Zeng, the second author, who is also the corresponding author of this paper. REFERENCES

Figure  6. The relationship between fault number and amplitude of 2sf1 in apparent power.

1. Figure 2 shows that the fault characteristic frequency of eccentricity is mfr in the spectrum of apparent power. Obviously, when m is 1, the amplitude of characteristic frequency is highest and the fault is diagnosed easily. 2. Figures 3–5 show that the broken bar and airgap eccentric fault characteristic frequency are, respectively, fb (k = 1) and mfr, which is beneficial

[1] Nandi, S. Toliyat, H. A. & Li, X. 2005. Condition monitoring and fault diagnosis of electrical motors—A review. IEEE Trans. Energy Converts 20(4):719–729. [2] Sun, L.L. Xu, B.Q. & Li. Z.Y. 2012. A MUSIC-SAA Based Detection Method For Broken Rotor Bar Fault in Induction Motors. Transactions of China Electrotechnical Society 27(12):205–212. [3] Bellini, A. 2009. Quad demodulation: A time-domain diagnostic method forinduction machine. IEEE Trans. Ind. Appl. 45(2):712–719. [4] Liu, Z.X., Yi, X.G. & Zhang, Z. 2003. On-line rotor fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors. Proceedings of the CSEE 23(10):148–152. [5] Blödt, M., Regnier, J. & Faucher, J. 2009. Distinguishing load torque oscil-lations and eccentricity faults in induction motors using stator current Wigner distributions. IEEE Trans. Ind. Appl. 45(6):1991–2000.

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[6] Arthur, N. & Penman, J. 2000. Induction machine condition monitoring with higher order spectra. IEEE Trans. Ind. Electron. 47(5):1031–1041. [7] Khezzar, A., Kaikaa, M. Y., Oumaamar, M. E. Boucherma, M. & Razik, H.2009. On the use of slot harmonics as a potential indicator of rotor bar break age in the induction machine. IEEE Trans. Ind. Electron. 56(11):4592–4605. [8] Faiz, J. & Ojaghi, M. 2009. Instantaneous-power harmonics as indexes for mixed eccentricity fault in mains-fed and open/closed-loop drive-connected squirrel-cage induction motors. IEEE Trans. Ind. Electron. 56(11):4718–4726. [9] Drif, M. & Cardoso, A.J.M. 2009. The use of the instantaneous-reactive-power signature analysis for rotor-cage-fault diagnostics in three-phase induction motors. IEEE Trans. Ind. Electron. 56(11): 4606–4614. [10] Mirafzal, B. & Demerdash, N.A.O. 2006. On innovative methods of induction motor interturn and broken-bar fault diagnostics. IEEE Trans.on Industry Application. 42(2):405–414.

[11] Jung, J.H. & Kwon, B.H. 2006. Corrosion model of a rotor-bar-under-fault progress in induction motors. IEEE Trans. Ind. Electron. 53(6):1829–1841. [12] Drif, M. & Cardoso, A.J.M. 2008. Airgap-eccentricity fault diagnosis, in three-phase induction motors, by the complex apparent power signature analysis. IEEE Trans. Ind. Electron. 55(3):1404–1410. [13] Liu, Z.X., Wei, Y. & Zhao, M. 2006. Fault Diagnosis Way Based on RELAX Spectrum Analysis in Squirrel Cage Induction Motors. Proceedings of the CSEE. 26(22):146–150. [14] Liu, Z.X., Yi, X.G. & Zhang, Z. 2003. On–line Monitoring and Diagnosis Way Based on Spectrum analysis of HILBERT Modulus Induction Motors. Proceedings of the CSEE. 23(7):158–161.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Commissioning and operation of Heze some leather wastewater treatment plant K. Wang, B. Liu, H.D. Zhang & W. Zhao Shandong Academy of Environmental Science, Jinan, China

ABSTRACT: Main disposal process of a leather sewage factory in Heze includes coarse and fine screen + ash sump + regulation pool + flocculating chamber + primary biological treatment + secondary biological treatment + Fenton oxidation. The time of the debug was 45 days, and the debugging results show that the flocculation precipitation using the combination of FeSO4 + PAM can not only remove about 30% of COD, but can also realize desulfurization. Biochemical treatment part was composed of two levels of A/O, not only to improve the treatment effect, but also to have good resistance to impact load capacity. During the debugging, the effluent COD and ammonia nitrogen were 80–150 mg/L and 0.5–2.5 mg/L, respectively, the total removal rate reached 90% and 98%, respectively. The dosage of FeSO4 and H2O2 of Fenton oxidation was 250 mg/L and 500 mg/L, respectively. Under this condition, the effluent COD was 38–52 mg/L and the removal rate reached 54–64%. Keywords: 1

leather wastewater; COD; ammonia nitrogen; A/O; flocculation; Fenton

INTRODUCTION

There were more than 2000 leather enterprises which produce more than 138  million tons of wastewater. Due to the large number of chemicals used in the leather production process [1], leather wastewater was considered to be one of the world’s most polluted wastewater [2]. Because of this, governance and solving the leather wastewater pollution were the urgent affairs of the leather industry. It has become important factors which affect the sustainable development of the leather industry. Leather wastewater treatment methods mainly included physical and chemical methods [3] and biochemical methods [4–5]. Physical and chemical methods mainly included flocculation precipitation, reverse osmosis, electrochemical, ozone and Fenton oxidation, etc. Biochemical methods mainly included SBR, A/O, and the MBR, etc. In order to make the leather wastewater meet the processing requirements, people often used the combination of physical and chemical methods and biological method combined process. Physicochemical method is commonly used in pretreatment or deep processing phase, and main technology was biochemical method which could remove most of COD and ammonia nitrogen of the wastewater. At present, most studies of leather wastewater were done in a small or pilot level. Reports on the

whole process which is applied to the actual water processing and to achieve good treatment effect were little. This article introduced in detail the whole operation of a leather sewage plant in Heze, including primary physicochemical treatment, the operation of the secondary biochemical treatment and advanced treatment. 2

THE BASIC INFORMATION OF THE PLANT

2.1 Main technological processes of the plant The leather industry sewage plant mainly disposes the water produced in the process of leather production. The design flow rate was 3000 m3/day. Pretreatment of the plant level included coarse and fine screen, ash sump, regulation pool and flocculating chamber. The secondary processing mainly included primary biochemical pool and secondary biochemical pool. Deep processing was Fenton and its function was to dispose hard biodegradable organic matter by strong oxidation. 2.2

Leather wastewater

The design of water quality and the actual water quality of leather wastewater is shown in Table 1. Table  1  shows that the actual water quality has been beyond the design water quality except pH.

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Table 1.

Comparison of design quality leather wastewater and the actual water quality.

Index

COD

BOD

NH4+-N

SS

Sulfide

pH

Design quality Actual quality

3000 2800–5400

2000 1800–3000

200 188–513

3000 2500–3400

60 56.3–82.5

6–10 8.9–7.7

Note: The unit is mg/L except pH.

3.

THE RESULTS AND DISCUSSION

3.1

Pretreatment

Pretreatment preprocessing included coarse and fine screen, ash sump, regulation pool and flocculating chamber. The effect of primary treatment of wastewater is shown in Figure 1. Figure  1  shows that in the debugging process, the removal rate of SS has been stable at more than 95%. Ten days before debugging, the flocculants were PAC and PAM, the dosages were 300  mg/L and 5 mg/L, respectively. Debugging results showed that the COD removal rate of PAC and PAM was low, which was 15%–20%. Not only that, because PAC has no desulfurization, the removal rate of sulfur ions of wastewater is only about 10%. In order to reduce the follow-up biochemical oxygen consumption of the process, reduce the processing cost, starting from the 11th day, the flocculant changed to ferrous sulfate and PAM and the dosages were 300  mg/L and 5  mg/L, respectively. Because the iron in ferrous sulfate ion can react with sulfur ions in waste water, starting from the 11th day, removal rate of the sulfur concentration in wastewater was around 95% and sulfur ion concentration in water was stable at 5 mg/L. Due to the vast majority of sulfur ions in waste water has been removed, the effluent COD was greatly reduced. The removal rate was from the original 15% to 35%. Thus, for leather wastewater, a combination of ferrous sulfate and PAM treatment effect was better than the combination of PAC and PAM. 3.2

Biochemical treatment

Biochemical was the core part of the sewage treatment plant. In biochemical treatment, most of the organic matter and ammonia nitrogen was removed. It included primary and secondary A/O. Due to large change of leather wastewater, the primary A/O was oxidation ditch form, and secondary A/O was traditional push streaming. 3.2.1 The effect of primary A/O During debugging, the sewage plant sludge concentration of A/O, the water inflow, the concentration of ammonia nitrogen and COD in influent and effluent are shown in Figure  2. Figure  2  showed that in order to accelerate the debugging and domestication, after the start of debugging, acti-

Figure 1.

Effect of pre-treatment.

vated sludge was added to primary A/O every day as a kind of seed sludge. The process sustained nine days. The seed sludge came from Heze first city municipal sewage plant and the sludge concentration was 100  g/L. The volume of addition was 25 m3 per day. In the fifth day, the seed sludge came from Heze third city municipal sewage plant which was pressure filtration activated sludge. Therefore, sludge concentration increased rapidly and there are 4  days of 1539  mg/L and up to 5  days of 2516 mg/L. On the ninth day, the sludge concentration of A/O pool has reached 3784 mg/L, and the adding of seed sludge was stopped. Because leather wastewater has certain toxicity, the water inflow was increased step by step. In that condition, the activated sludge gradually adapts to the water quality of leather wastewater. Before the commissioning of 13th day, because of the water inflow was not so much, the water inflow was intermittent. Artificial additive glucose was added at the same time in order to make sure of organic matter content in the wastewater. Before the 13th day of debugging, the effluent COD and ammonia nitrogen were around 200 mg/L and 3 mg/L, the removal rate reached 90% and 95%, respectively. From 14th day, the water inflow reached 1200 m3. Compared to the 13th day, the volume of water inflow increased by 150%. The successive way of inflowing was used. Because of the increase of the wastewater, the effluent COD and ammonia nitrogen have some fluctuation; the effluent COD and ammonia nitrogen were 521 mg/L and 13.5 mg/L, respectively, i.e. more than double compared to the previous data. This showed that the increase of water inflow has a certain impact on

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water flow was reduced to 2000 m3. After reducing the water flow, the effect of treatment was recovered quickly. From the 37th day, the water flow was 3000  m3 and the effect of treatment was not deteriorated. Until the end of commissioning, the operation of the system has been very stable, sludge concentration remained at around 4000 mg/L, the removal rate of COD and ammonia nitrogen has remained steady at around 95%. To sum up, at the beginning of debugging, it was a good strategy to add a large amount of activated sludge and carbon source. At the same time, combining with the gradient of the inflow, the activated sludge could rapidly adapt to the water quality of leather wastewater. After the system was subjected to shock loading, the quality of the efflux was deteriorated, but recovered quickly. It suggested that oxidation ditch form has a strong impact resistance capacity. If the water inflow was more than 50% of the design inflow, the system will collapse. But, after reduction of water, the effect of treatment was recovered rapidly. In the end, the concentration of A/O effluent COD and ammonia nitrogen was 200  mg/L and less than 5  mg/L, respectively and removal rate reached 95% and 98%, respectively. Figure 2. Secondary biochemical sludge concentration in water and wastewater treatment effect. (a) Sludge concentration, water inflow, COD and ammonia nitrogen variation. (b) The removal rate of COD and ammonia nitrogen.

the system, leading to the reduction of removal rate of COD and ammonia nitrogen. But then the system returns to normal soon, the removal rate of COD and ammonia nitrogen returns to more than 90%. This showed that oxidation ditch in the form of A/O process has a strong resistance for impact load. Even the organic load was more than double, the system would not lead to crash. On the 20th day of debugging, the water inflow was increased to 1800 m3 and the system performance was good. Because the resistance ability of impact was strong, the water inflow increased every 3 days. On the 32th day of debugging, the water inflow had reached the designed water quantity, 3000  m3 per day. It was important to note that on the 35th day of debugging, due to human error, the water inflow suddenly increased to 4800  m3/d, which lasted for about 12 h. The sludge concentration of the system soared to more than 6000 mg/L and the color of the activated sludge had changed from amber to dark brown, while the DO of the aerobic pool was under 0.2 mg/L. These phenomena show that the system was beyond the scope of affordability. Subsequent testing also shows that under the water inflow, there is an obvious higher effluent COD and ammonia nitrogen, removal rate fell sharply. In order to make the system quickly recover, on the 36th day,

3.2.2 Secondary A/O From the 21th day of debugging, secondary A/O was started. The main role of the secondary A/O was to remove organic matter and ammonia nitrogen of wastewater further. Due to the actual operation process, nitrification was good, the ammonia of the inflow of secondary A/O was low. The function of secondary A/O was to remove COD. The result of secondary A/O is shown in Figure 3. In order to improve the efficiency of debugging, at the beginning of debugging the secondary A/O also added foreign activated sludge as seed sludge. After adding the first time, the sludge concentration reached 700  mg/L. In order to ensure the treatment effect, debugging on 26th day, more seed sludge was added. Eventually sludge concentration of secondary A/O maintained at about 1500 mg/L.

Figure 3.

Secondary A/O treatment effect.

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Table 2.

Test in Fenton experimental results.

Raw water COD = 130 mg/L

First

Second

Third

Fourth

Fifth

Dosage of ferrous sulfate Dosage of H2O2 COD (eff) Removal rate

0.25 kg/m3 0.5 kg/m3 76 mg/L 41.5%

0.5 kg/m3 0.25 kg/m3 46 mg/L 64.6%

0.5 kg/m3 0.5 kg/m3 40 mg/L 69.2%

0.5 kg/m3 0.8 kg/m3 15 mg/L 88.4%

1 kg/m3 0.8 kg/m3 10 mg/L 92.3%

Because of the concentration of wastewater was low, the secondary A/O the COD removal rate was not high, generally between 9% and 50%. And effluent COD was generally between 80 and 150 mg/L. It is important to note that on the debugging 35th day, the COD of secondary A/O inflow increases suddenly which reached 612 mg/L. But the COD of effluent of secondary A/O keeps within 150 mg/L. Thus, the secondary A/O not only has the effect of deep processing. When primary A/O has operation problems, it could also be used as a buffer pool, to ensure the biochemical effluent water quality. 3.3

Oxidation treatment

Figure 4.

Through biochemical processing, leather wastewater effluent COD was between 80 and 150 mg/L, which could not meet the local COD emission standards (COD < 60 mg/L). So the wastewater needed further in-depth processing. The choice of technology was Fenton oxidation. In order to improve the efficiency of debugging, we have done some tests to determine the reaction parameters. The experimental results are shown in Table 2. Table 2 shows raw water COD was 130 mg/L, higher than the average concentration of the water. The experiment was divided into five groups, all the raw water add hydrogen peroxide and ferrous sulfate after pH to about 4, under the condition of stirring reaction for an hour. After the reaction, the pH was increase back to 7, and then PAM (3 mg/L) was added. Experimental results suggested the lowest COD removal rate was 41.5% which the water COD was 76  mg/L and the highest COD removal rate was 92.3% which the effluent COD was 10 mg/L. Considering the actual emission standard and economic factors, determination of ferrous sulfate and hydrogen peroxide dosage of 0.5 kg/m3 and 0.25 kg/m3. Fenton oxidation in the actual debugging process of the effect of the secondary biochemical process is shown in Figure 4. Figure 4 shows that the Fenton process was started on the 31th day of debugging. The secondary biochemical effluent has been relatively stable which could deal with the actual effect of the real reaction. Within 15 days of the debugging process, in accordance with the test parameters, namely, ferrous sulfate and hydrogen peroxide, 0.5 kg/m3 and 0.25  kg/m3, respectively. The stable effluent COD within 60 mg/L, removal rate of 54–64%.

4

COD removal rate of Fenton.

CONCLUSION

Heze, a sewage plant, uses coarse and fine screen + ash sump + regulation pool + flocculating chamber + primary biological treatment+secondary biological treatment + Fenton oxidation process to dispose leather wastewater. The COD and ammonia nitrogen were 2800–5400  mg/L and 188–513  mg/L, respectively. Through 45 days of commissioning, start-up success and effluent COD and ammonia nitrogen are lower than 60 mg/L and 5 mg/L, respectively.

REFERENCES [1] Amel Benhadji, Mourad Taleb Ahmed, Rachida Maachi. Electrocoagulation and effect of cathode materials on the removal of pollutants from tannery wastewater of Rou ba [J]. Desalination, 2011, 277(1–3): 128–134. [2] Alberto Mannucci, Giulio Munz, Gualtiero Mori. Anaerobic treatment of vegetable tannery wastewaters: A review [J]. Desalination, 2010, 264(1–2): 1–8. [3] Sajjad Haydar, Javed Anwar Aziz. Coagulation–flocculation studies of tannery wastewater using combination of alum with cationic and anionic polymers [J]. Journal of Hazardous Materials. 2009, 168: 1035–1040. [4] R. Ganesh, G. Balaji, R.A. Ramanujam. Biodegradation of tannery wastewater using sequencing batch reactor-Respirometric assessment [J]. Bioresource Technology, 2006, 97: 1815–1821. [5] G. Farabegoli, A. Carucci, M. Majone. Biological treatment of tannery wastewater in the presence of chromium [J]. Journal of Environmental Management, 2004, 71: 345–349.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Engineering depth treatment of pharmaceutical wastewater by magnetization catalytic oxidation-A2O-MBR combined process W. Zhao, B. Liu, H.D. Zhuang, K. Wang & H.D. Ji Shandong Academy of Environmental Science, Jinan, China

ABSTRACT: The pharmaceutical wastewater, after secondary biochemical process, has the characteristics of poor biodegradability, high ammonia, and high concentration of suspended solids. In order to solve the above problems, in this project, the magnetized catalytic oxidation-A2O-MBR process was explored for the deep treatment of pharmaceutical wastewater, which improved the biodegradability and effect of the removal of ammonia of the wastewater, as well as reduced suspended solids, effectively. Due to the application of the magnetized catalytic oxidation-A2O-MBR process, the COD of the effluent was below 250 mg/L with a removal rate of 93%, and the ammonia was below 15 mg/L with a removal rate of 97%. Moreover, the concentration of suspended solids was as low as 5 mg/L, and the removal rate of suspended solids was over 99.5%. The process has a series of advantages such as good effect, shock load capability, and low sludge production, which provides a potential reference for this type of wastewater. Keywords: 1

pharmaceutical wastewater; magnetized catalytic oxidation; A2O; MBR

INTRODUCTION

Since the time of reforming and opening up of China, the pharmaceutical industry has been developing rapidly and the scale of production has been expanding gradually, which accordingly made the pharmaceutical wastewater becoming one of the major industrial wastewater. Due to the difference of categories and manufacturing process of drugs, the treatment of pharmaceutical wastewater has become a major problem in the environmental protection for the characteristics of high toxicity, poor biodegradability and high salinity[1–3]. 2

PROJECT OVERVIEW

A pharmaceutical group in Shandong Province is mainly engaged in researching, manufacturing, and selling of active pharmaceutical ingredients, intermediates, and sterile preparation containing cephalosporins, penicillins, anti-cancer, and Table 1.

cardiovascular drugs and so on. For the large scale production in the enterprise, the discharge waste water is continuous, and the quality and quantity are stable after adjusting. However, such pharmaceutical wastewater has the characteristics of complicated composition, high toxicity, and salinity, and poor biochemical process. The direct discharge, even after the two-stage biochemical treatment, would still cause serious pollution to the surrounding water, because of high toxicity, poor biochemical treatment and high COD and ammonia nitrogen of the effluent. Through the experimental research, we finally determined the deep treatment process as the suitable process for this kind of wastewater, which also achieved good effect in the practical engineering application. The effluent meets the secondary emission standard of national integrated wastewater discharge standard (GB8978-1996) after the treatment by this deep treatment process. The comparison of indicators of influent and effluent and the secondary emission standard is as shown in Table 1.

Comparison of indicators of influent and effluent and the secondary emission standard.

Setting

COD (mg ⋅ L−1)

BOD (mg ⋅ L−1)

Ammonia (mg ⋅ L−1)

SS (mg ⋅ L−1)

pH

Influent Effluent The secondary emission standard

3000 ≤250 300

250 ≤30 60

650 ≤15 50

1100 ≤5 150

8.5 7.0 6∼8

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3

PROCESS SELECTION

3.1

Process

In view of the poor biodegradability, high concentrations of nitrogen and SS of this kind of pharmaceutical wastewater, the deep treatment process, that is the magnetization catalytic oxidation-A2OMBR process, were proposed on the basis of tests in the laboratory and engineering experience in pharmaceutical wastewater treatment. This process has significant effects on improving biodegradability, denitrification and phosphorus removal, and intercepting suspended solid, and also providing reference for this kind of wastewater. Process is shown in Figure 1. 3.2

The main process description

Technological process mainly includes the magnetization catalytic oxidation treatment unit, A2O and MBR treatment unit. 1. Magnetization catalytic oxidation treatment unit After biochemical reactions, there are macromolecular organic matters which are difficult for biochemical degradation in the pharmaceutical wastewater. The establishment of this unit is to remove part of the organic matter, as well as to improve biodegradability of the wastewater.

The main residual organic pollutants in the biochemical treated wastewater are the small molecules polar organic matter with negative charge, in which the electronegative active points were packed in unsystematic arrangement of water molecules under the normal state. After magnetization treatment, water molecules group collapsed and rearranged according to the direction of magnetic field lines, which reduced the collision barriers between the polarity of organic activity and elixir molecule, and increased the speed and degree of chemical reaction significantly. When the effluent from secondary sedimentation tank flowed into the hybrid reactor, the processes such as combining of drugs, activating of catalyst and organic pollutant molecule would be conducted. Then, the effluent flowed into the catalytic polymerization reaction zone, the hard biodegradable macromolecular pollutants can be degraded into small molecules through chemical oxidation, and thus improved the biodegradability. 2. A2O and MBR treatment unit The process of degradation of organic pollutants, nitrogen and phosphorus removal of pollutants of the wastewater would proceed in the biochemical tank. Then, the effluent would flow into the membrane processing unit to strengthen the biochemical function. The wastewater treated by the membrane filtration system can be discharged or entering the subsequent processing system. 3.3

The main structure and design parameters

The main structure and design parameters are shown in Table 2. 4 4.1

Figure 1.

Table 2.

OPERATING RESULTS Magnetization catalytic oxidation treatment unit

When the effluent from the secondary sedimentation tank flowed through the magnetizer, catalytic

Figure of process.

The main structure and design parameters.

Serial number

Structures

Specifications

Size

Quantity

Parameters

1 2 3 4 5

Sump Catalytic oxidation pond Sedimentation tank Biochemical pool Membrane pool

Brick Brick Brick Brick Steel

15 × 10 × 5 m 10 × 10 × 5 m 10 × 10 × 5 m 30 × 20 × 5 m 10 × 10 × 5 m

2 1 1 1 1

HRT = 24 h HRT = 8 h HRT = 8 h HRT = 48 h HRT = 8 h

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oxidation reaction would occur. The test and actual engineering application indicating the catalytic oxidation effect and biodegradability increased significantly after magnetization. Due to the main aim of this unit is to improve the biodegradability but not to degrade all organic matter, less dosage was used to save cost. The inflow of this deep processing system is 1500  m3/d, the COD, BOD, ammonia and SS of the inflow are 2800–3200  mg/L, 600–700  mg/L, 1000–1200  mg/L, respectively. After wastewater is let into the magnetizing apparatus, sulfuric acid and 0.25  kg/m3 of ferrous sulfate were used to adjust pH to 4–5. Afterwards, 0.5 kg/m3 of H2O2 was added and reacted fully, then, NaOH solution was added to adjust the pH to be 7∼8. After precipitation, the COD and SS of the supernatant can reduce to 1500–1800 mg/L and 300–400 mg/L, respectively, whereas the BOD of supernatant rises to 400–600  mg/L, and ammonia nitrogen almost remains the same. After a period of debugging, the parameters of the magnetized catalytic oxidation unit become stable, 30  days was selected to observe the trend of the change of COD and BOD. As can be seen from Figure 2, both the COD and BOD of inflow and outflow keep stable, COD removal rate was about 45%, and BOD increased obviously. As can be seen from Figure 3, the ratio of BOD and COD increased from 0.07 to 0.07, and the biodegradability improved apparently. All of which demonstrated the potential in providing a good condition for the subsequent biochemical unit. 4.2

The ratio of BOD and COD ratio.

Figure 4. Before and after biochemical pool COD and ammonia nitrogen concentration.

A2O processing unit

Aiming at the problem of high ammonia nitrogen in wastewater, the A2O processing units were set up to achieve the purpose of removing nitrogen and

Figure  2. reaction.

Figure 3.

Compare COD and BOD before and after

phosphorus and degrading organic matter at the same time. The ammonia nitrogen content in the wastewater is as large as 600–700  mg/L, it would damage the activated sludge if the wastewater was directly discharged into the biochemical system. Thus, the inflow must be mixed with the wastewater treated by the MBR membrane pool with volume of 1:1 before letting into the biochemical system. In order to maintain the best condition of alkalinity and pH of the biochemical system, a certain amount of NaCO3 and NaOH needs to be added in the process of ammonia nitrogen degradation. The local municipal sludge was adopted for the cultivation and domestication of activated sludge in this biochemical unit. The inflow water was gradually increased to 1500  m3/d by four stages, sludge quantity in the aerobic pond was maintained at about 6000  mg/L. Figure  4  shows the data of 30  days consecutive testing after debugging stability. As can be seen from Figure 4, after

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a biochemical reaction, the COD of inflow in the biochemical pool was decreased from 950 mg/L to 300  mg/L, the obvious degradation of COD also explains that the magnetization catalytic oxidation unit can effectively improve the wastewater biodegradability. In order to improve denitrification ability quickly, a moderate amount of antinitrifying bacteria was added. The testing data of 30 days indicate that ammonia nitrogen decreased from 300–350 mg/L to 20 mg/L, the denitrification effect is remarkable in this system. 4.3

MBR membrane cell processing unit

The microorganisms were shut off within the bioreactor completely, which is not only helpful for interception growth of the proliferated slowly microorganisms, but also for the hydraulic retention time of the refractory organic matter, and thus improved the nitrification efficiency and degradation efficiency. Moreover, high quality and stability of effluent water was obtained, of which the suspended solids and turbidity were close to zero. The microorganisms were shut off within the bioreactor completely, which is not only helpful for interception growth of the proliferated slowly microorganisms, but also for the hydraulic retention time of the refractory organic matter, and thus improved the nitrification efficiency and degradation efficiency. Moreover, high quality and stability of effluent water was obtained, of which the suspended solids and turbidity were close to zero. 5

of poor biodegradability, high ammonia nitrogen content and high SS. The use of magnetization catalytic oxidation-A2O-MBR technology for deep treatment improved the biodegradability and denitrification effect and intercepted the suspended solid effectively. For the effluent, the COD, ammonia nitrogen and SS were 250 mg/L, 15 mg/L and 5 mg/L, respectively, and the removal rate of COD, ammonia nitrogen and SS was 93%, 97% and 99.5%, respectively. The quality of effluent conforms to the national integrated wastewater discharge standard (GB8978-1996) and biological pharmaceutical industry secondary emission standards. This process design has a good prospect in industrial application. REFERENCES [1] Oktem Yalcin, Ince Orhan, Donnelly Tom. Determination of optimum operating conditions of an acidification reactor treating a chemical synthesis-based pharmaceutical wastewater [J]. Process Biochemistry, 2006, 41: 2258–2263. [2] Timothy M, Lapara,. Aerobic biological treatment of a pharmaceutical wastewater: effect of temperature on COD removal and bacterial community development [J]. Water Research, 2001, 35 (18): 4417–4425. [3] Wu Suqing, Yue Qingyan, Qi Yuanfeng. Preparation of ultra-lightweight sludge ceramics (ULSC) and application for pharmaceutical advanced wastewater treatment in a biological aerobic filter (BAF) [J]. Bioresource Technology, 2011, 102(3): 2296–2300.

CONCLUSION

After the secondary biochemical process, the pharmaceutical wastewater has characteristics

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study of broadband microstrip circulator using YIG single crystal Zhi-Qun Cheng, Ya Luan, Min-Shi Jia & Xin-Xiang Lian Key Laboratory of RF Circuit and System, Education Ministry, Hangzhou Dianzi University, Hangzhou, China

ABSTRACT: A broadband C-band microstrip junction circulator is designed in this paper, and the design method and process are given. By using double Y-junction technology, we improve the bandwidth of the circulator. The substrate is YIG single crystal ferrite materials. This circulator is simulated with HFSS software. After Optimization, we get performances of the circulator, the results show that insertion loss of circulator is less than 0.5 dB from 3.90–9.15 GHz, isolation is more than 15 dB. Meanwhile, considering the change of material properties under high frequency, we do a deviation analysis. Keywords: 1

ferrite; broadband; circulator

INTRODUCTION

2

Ferrite circulator, as one of nonreciprocal microwave components based on ferrites, plays an important role in modern communications [1]. The circulators have been used in wireless communications, GSM/WCADMA mobile communications systems, GPS, military radar, electronic warfare and other RF systems [2]. Most circulator use waveguide structure, it works as a large external module in circuit system rather than integrated into circuit PCB. It is an effective and reasonable way to reduce the size and weight of devices [3]. Development of broadband technology also brings a big challenge to the circulator, how to improve the bandwidth of the circulator has became an urgent problem for RF engineers. Double Y-junction circulator has a good performance on miniaturization and bandwidth, so it becomes a hot research direction. In this paper, a broadband C-band microstrip junction circulator is designed. The bandwidth of the circulator is improved by using “double Y-junction” technology. First, the appropriate ferrite materials-YIG single crystal is chosen at first. And then, the related parameters and modeling are set up. Finally, circuit is simulated by using software of HFSS (High Frequency Structure Simulator). It is analyzed that the circulator performances change with permittivity and the resonance linewidth of YIG material. The simulated results show that insertion loss of circulator is less than 0.5 dB from 3.90 to 9.15 GHz, isolation is more than 15 dB, and VSWR is less than 1.46. Meanwhile, deviation analysis is given out for considering the change of material properties under high frequency.

THEORY

Ferrite circulator is a nonreciprocal three-port device and it possesses a unidirectional transmission of high-frequency signal energy characteristic, because of using gyromagnetic ferrite material. To Design a circulator, first, we must determine the saturation magnetization of ferrite (4πMs). Saturation magnetization intensity is saturated magnetic moment of ferrite per unit volume. Under normal circumstances, the higher center frequency f0 of annular is, the bigger saturation magnetization 4πMs of the ferrite materials adopted is [4]. Figure  1 shows the saturation magnetization. It shows the relationship between saturation

Figure  1. working.

The choice of saturation magnetization

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magnetization and the center frequency, which indicates the basic linear relationship:

that the center equal model is RLC parallel circuit. The equivalent admittance of center is following as:

4πMs ≈ 0.3f0 (MHz)

Gj = Gr + jωC1 + 1/jωL1

(1)

The center frequency of C band is 6 GHz, so, ferrite materials whose saturated magnetization intensity is about 1850 G is chosen with frequency of 6 GHz. YIG gyromagnetic materials is adopted as the substrates of circulator, the saturation magnetization of materials is 1780 G, the relative dielectric constant εr equal to 15, the linewidth is 20 Oe, dielectric loss is 0.0037. The resonance linewidth of the material is very small, [5] which have a positive impact on developing circulator’s bandwidth and reducing the insertion loss. C band circulator generally works in low field mode. It means that constant deviation resonance magnetic field intensity is lower than the material itself. Due to the demagnetization effect itself, ferrite materials’ infield is close to 0. In the design, we set the diamond as 0 to simplify the problem. Another important parameter is the size of the center of the circular. The center junction radius R can be calculated by equation: R

a λ0

ξr μ⊥

(2)

where, “a” takes between 0.127∼ 0.293, λ0 is for the center frequency, εr is ferrite relative dielectric constant, effective permeability μ⊥ takes between 0.5 ∼ 0.8. Finally, the center junction radius R is calculated of 2.9 mm. Double Y-junction circulator is a good candidate to get performances of miniaturization and wide bandwidth. So, it becomes a hot research direction. [6] By adding a small Y-junction, the impedance of center can be adjusted. If small Y-junction has appropriate size, the imaginary part of center impedance is close to zero in wideband. It avoids that imaginary part of impedance changes with frequency and provides a precondition for broadband circulator. The equivalent model is given in Figure  2. Essentially, small Y-junction is opencircuit line, it multiples with the center. It shows

Figure 2. The equivalent circuit of Y-junction choice of saturation magnetization.

(3)

The small Y-junction equivalent model is LC parallel circuit, the equivalent admittance of center is Gy = jωC2 + 1/jωL2

(4)

Under the action of small Y-junction, equivalent admittance of center is: G1 = Gr + jωC1 + 1/jωL1 + jωC2+ 1/jωL2 = Gr + jω (C1 + C2 − 1/L1 − 1/L2

(5)

By adjust the size of small Y-junction opencircuit line, supposing: jω (C1 + C2 − 1/L1− 1/L2= 0

(6)

It means that the imaginary part of center impedance is nearly disappear. Pure resistance is easy to match to 50Ω in wideband. 3

DESIGN

According to selection of material and calculation of the design, double Y-junction microstrip circulator is designed as shown in Figure 3. Using 3D electromagnetic field simulation software of Ansoft HFSS to establish and simulate circulator model. YIG and substrate of printed copper microstrip line are adopted in the design and microstrip line thickness is 0.002 mm. Figure 4 shows the circulator’s flat structure and part parameters. The destination of the design of the three different microstrip lines is to match the

Figure 3.

The model of circulator.

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Figure 4.

The parameters of circulator.

Figure 5.

Simulated S parameters of circulator.

(isolation) is more than 15 dB, S11 (return loss) is less than 15 dB at the frequency range from 3.90 to 9.15 GHz. Microwave circuit and device is becoming smaller. Decrease the thickness of YIG substrate a good way to make circulator smaller. The relationship between the thickness and insertion loss is simulated in this design. At center frequency 6 GHz, we observe the insertion loss while changing the thickness of substrate from 0.7 to 2.2 mm. The relation between them is showed in the following Figure 6. The relative permittivity and the resonance linewidth of YIG material will fluctuate in high frequency [7]. Figure  7 shows curves of S11, S21 versus the resonance linewidth respectively at frequency of 4, 6, 8 GHz. The bigger resonance linewidth of the ferrite materials is, the bigger insertion loss (S21) is. The RMSE (Root-MeanSquare Error) of S11 are 0.03656, 0.02503, 0.01966 respectively at frequency of 4, 6, 8 GHz, this shows a good stability. Port 1 reflection coefficient (S11) is almost constant.

Figure 6.

center junction impedance to port characteristic impedance 50 Ω, in order to improve the device performance, two-stage microstrip matching is adopted and small Y-junction is added. So it can effectively expand the bandwidth. The circulator is simulated with impedance of three ports of 50 Ω and frequency range from 3 to 10 GHz. The physical parameters R, W1, L1, W2 and L2, W3, L3 are optimized to obtain ideal transmission characteristics. After optimization, simulated results show in Figure 5 at the frequency range from 3 to 10 GHz. Circulator is symmetry, the performance of port 2 and port 3 is same to port 1. The circulator works in the annular direction as 1-2-3-1 according to the S parameter simulating result. S21 (insertion loss) is less than 0.5 dB, S31

Insertion loss at different thickness.

Figure 7. Simulated S parameters at different linewidth.

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(the black curve): the relative dielectric constant εr is 15 and the linewidth is 20 Oe. S21 (insertion loss) down a little and S11 have a deviation toward high frequency on deteriorative situation. 4

Figure 8. Simulated S parameters at different permittivity.

CONCLUSIONS

By using double Y-junction technology, the performances of bandwidth of the circulator are improved. An ultra broadband circulator with YIG single is successfully designed. It shows that insertion loss of circulator is less than 0.5 dB from 3.90–9.15 GHz, isolation is more than 15 dB. Circulator is still stable while the permittivity and the resonance linewidth of YIG material have a fluctuation. ACKNOWLEDGMENT This work was supported by Natural Science Foundation of Zhejiang Province (No. Z1110937). REFERENCES

Figure 9.

Ideal and deteriorative S parameters.

Figure  8 shows the relationship between S11, S21 and relative dielectric constant respectively at 4, 6, 8 GHz. Insertion loss (S21) increases as relative permittivity decreases, and port1 reflection coefficient (S11) increases as relative permittivity decreases at the low frequency such as 4GHz. The RMSE of S21 is 0.06014. The RMSE of S11 is 2.4272. S21 and S11 change little at the high frequency such as 6, 8 GHz. Figure  9 shows that S parameters change with frequency in an extreme deteriorative situation (the red curve): the relative permittivity (εr) of 13.5 and the linewidth of 40 Oe and in the ideal situation

[1] J. Helszajn, The Stripline Circulators: Theory and Practice, Wiley, 2008. [2] L.Y. Tio, et al, “Novel general finite elementsolver for gyroelectric structures,” in IEEE MTTSInt. Dig., vol. 1, Jun. 2003, pp. 421–424. [3] H. Newman, D.C. Webb, and C.M. Krown “Design and realization of millimeter wave microstrip circulators,” Proc. SPIE, Vol. 2842, pp. 181–191, 1996. [4] W.K. Gwarek and A. Moryc, “An alternative approach to FD-TD analysis of magnetized ferrites,” IEEE Microwave and Wireless Component Letters, Vol. 14, No. 7, July 2004. [5] Lu and F.A. Fernandez, “An efficient finite ement solution of inhomogeneous anisotropicand lossy dielectric waveguides,” IEEE Trans. Microwave Theory Tech., vol. 41, pp. 1215–1223, June/July 1993. [6] O. Zahwe, B. Abdel Samad, B. Sauviac, J.P. Chatelon, M.F. BlancMignon and J.J. Rousseau, “YIG thin film used to miniaturize acoplanar junction circulator,” J. of Electromagn. Waves and appl., Vol. 24, pp. 25–32, 2010.3. [7] Helszajn, J.; Apollo Microwaves Ltd., Dorval, QC, Canada; Caplin, M.; Frenna, J.; Characteristic Planes and Scattering Matrices of E and H-Plane Waveguide Tee Junctions Microwave and Wireless Components Letters, pp. 209–211 IEEE.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Decoupling mechanism for suspension laryngoscopy using a curvedframe trans-oral robotic system X.Y. Fu & X.Y. Yu Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China

J.T. Seo Hanyang University ERICA Campus, Ansan-si, Gyeonggido, Korea

ABSTRACT: The decoupling mechanism for suspension laryngoscopy, which uses a curved-frame trans-oral robotic system, is presented to promise enough position precision. The mechanism provides some friction force to maintain the stiffness of the end-effector so that the clamped motion of claw does not have influence on the shifting motion of end-effector. What is more, the structure of this decoupling mechanism ensures that it can be adopted widely in rope-traction end-effector. From simulations and from experiment, it is seen that the decoupling mechanism promises enough friction force to solve the coupled motion problem. Keywords: 1

decoupling mechanism; trans-oral robotic surgery; surgical robot; suspension laryngoscopy

INTRODUCTION

By using suspension laryngoscopy, especially in [1], various treatments [2–5] have been employed for laryngeal disease without radical open surgery of the hypopharynx, which results in loss of linguistic function. What is more, “a new surgery procedure which employs a curved frame as a means to guide a flexible arm for laryngeal surgery” in [6] has been applied to protect the patient from secondary complication, and promises people who have congenital problem to be conducted by suspension laryngoscopy. However, end-effector in [6] involves a coupled motion with deviation of 10º when the rope-traction claw closes, shown in Figure  1. It can be obviously seen that the clamping motion involves an extra force, leading the position of the end-effector to deviate from the accurate point. Then, the position precision of the end-effector cannot be guaranteed, which may cause irredeemable accident in surgery. It is apparent that this phenomenon occurs when active motion part in [6] does not have enough stiffness, so that clamping motion of claw changes the position of the end-effector, which means that the clamping motion of the claw couples with the shifting motion of the end-effector. To solve this problem, promising enough stiffness

of the active motion part can be an appropriate way. The paper is organized as follows. In Section II, the paper shows a possible scheme to decouple the clamping motion and shifting motion. Detailed demands for the decoupling mechanism are presented in Section III. In Section IV, a decoupling structure is shown. Then, simulation and trail model is presented to show that it provides sufficient friction force. Conclusions follow.

Figure 1.

Coupled motion.

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2

3

SECTION II

To begin, changing the material for active motion part into the one which has enough stiffness can be the possible way to solve the coupled motion problem. However, after changing a comparative high-stiffness active motion part, the coupled motion still exists, which has been verified in experiments on an adult phantom in [6]. What is more, it is not permitted to improve the stiffness infinitely, since the active motion part should maintain enough flexibility to bend when controller in the slave system pulls the wire to control the position of the end-effector. Then, control of the tightness of the wire can be an appropriate way to decouple the clamping motion of the claw and shifting motion of the end-effector. For instance, as shown in Figure  2, when the controller pulls the up-direction wire to move the claw upwards, if there is enough tightness in the down-direction wire, then the end-effector will have adequate stiffness which will weaken the influence, caused by the pulling force of the claw, on the position of the end-effector. Hence, it is a possible way to design an additional mechanism to keep sufficient stiffness for wires, which will solve the coupled motion problem.

The use of a friction force to maintain the tightness of the wire is a proper method to improve the stiffness of the end-effector. However, there are several demands for designing this mechanism. Firstly, an appropriate position for the decoupling mechanism should not affect the process of surgery. Considering that the end-effector can be inserted into the throat, and adaptor and driving modules are designed concentrated so that there is no space to install big mechanism as well, we make an adequate position between guide pipeline part and connecting part shown in Figure 3. It is not only convenient to insert an element, but also various lengths of the guide pipeline part do not have much influence on the effects of the system. Furthermore, the long tube is an ubiquitous characteristic for a surgical end-effector so that the decoupling mechanism can be applied widely. Then, the mechanism should respond to the changing of the controller automatically, so that the system can adopt a pure mechanical decoupling structure by utilizing the wires for shifting motion, which promise a smart structure and a wide application in a rope-traction end-effector. 4

Figure 2.

Decoupling method.

Figure 3.

Position of decoupling mechanism.

SECTION III

SECTION IV

By adopting lever and cam mechanism, a decoupling mechanism is shown in Figure 4. Up-control wire is attached to the up-direction wire show in Figure  4 (b), and the down-control wire, right-control, and left-control wire are attached to the corresponding wire as above. When the controller pulls the claw upwards, the up-control wire rotates the cam counter-clockwise, releases the lever, and no compression force acts on the updirection wire. Meanwhile, on the opposite side, the down wire releases the cam so that the spring rotates the cam clockwise, compresses the lever, and the lever compresses the down-direction wire, providing some friction force to improve the stiffness of the end-effector. Other directions have a similar phenomenon.

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where α is the preloading angle of the torsion spring, and k is spring constant. The simulation in Figure 4 (c) shows that it has sufficient strength. What is more, the trial model generates at most 5.76∼5.86N (weight of 588∼598g) upwards which is enough for the end-effector to maintain sufficient stiffness. From the experiment, the deviation is limited to 2º. By changing the texture on the end point of the lever, it is easy to gain more friction force. 5

CONCLUSION

For a suspension laryngoscopy using a curvedframe trans-oral robotic system, a decoupling mechanism is presented. The mechanism, in which the cam be attached on a tube, is controlled only by wires of the rope-traction end-effector, and is smart enough, so that it can be applied widely to lock the wires which is released by the controller. Quantified analysis of the friction force needed to guarantee enough stiffness is still remained to be further studied. REFERENCES

Figure 4.

Decoupling mechanism.

Figure 5.

Mechanical principle diagram.

Furthermore, from the mechanical principle diagram shown in Figure 5, we are able to calculate the compression force N acting on the wire, which is, N=

αk a r b

=

30 × 0.1993 2

×

47 8

[1] Xidong C., Xia Z, Chenjie X., Wenhong Y., Huichang Y., Jiaqi J. (2012). “Management of difficult suspension laryngoscopy using a GlideScope® Video Laryngoscope”. Archotolaryngol 132:1318–1323. [2] Park Y.M., Kim W.S., De Virgilio A., Lee S.Y., Seol J.H., Kim S.H. (2012). Transoral robotic surgery for hypopharyngeal squamous cell carcinoma: 3-year oncologic and functional analysis. Oral Oncol J48:560–566. [3] Hockstein et al (2005) Robot-assisted pharyngeal and laryngeal microsurgery: results of robotic cadaver dissections. Laryngoscope J115:1003–1008. [4] Weinstein G.S., O’Malley B.W. Jr, Hockstein N.G. (2005). Transoral robotic surgery: supraglottic laryngectomy in a canine model. Laryngoscope J 115:1315–1319. [5] Mc Leod I.K., Mair E.A., Melder P.C. (2005) Potential applications of the Da Vinci minimally invasive surgical robotic system in otolaryngology. Ear Nose Throat J 84:483–487. [6] Young-Sik Kwon, Kyung Tae, Byung-Ju Yi, “Suspension laryngoscopy using a curved-frame trans-oral robotic system”, International Journal of Computer Assisted Radiology and Surgery, Volume 9, Issue 4, pp 535–540, July 2014.

≈ 17.56N

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Calculation and analysis on shunt coefficient of short-circuit current inside Fuzhou 1000 kV Ultra-High Voltage substation Bin Zhang Department of Electrical Engineering, Tsinghua University, Beijing, China

Bin Tang Fujian Provincial Electric Power Company Limited, State Grid Corporation of China, Fuzhou, China

Wei-Hua Ma Communication Construction Branch, State Grid Corporation of China, Beijing, China

Jia-Yong Zou Southwest Electric Power Design Institute, Chengdu, China

ABSTRACT: When short-circuit fault occurs inside the power substation, it is the current diffusing into ground that really causes safety trouble. The shunt coefficient characterizes the shunt ability of grounding grid or overhead ground wires to fault current, so it can be used for analyzing the distribution of shortcircuit current. This paper studies the shunt mechanism of short-circuit current inside the power substation, defines the shunt coefficient suitable for engineering practice and focuses on introducing the numerical computation method of shunt coefficient. The paper calculates the shunt coefficient and the current diffusing into ground inside Fuzhou 1000 kV substation and analyzes various factors affecting the shunt coefficient. Keywords: short-circuit fault inside substation; shunt coefficient; grounding resistance; short-circuit current; ground potential rise 1

INTRODUCTION

Power substation’s good grounding is the fundamental guarantee of safe operation of the power system. The power system’s short-circuit fault is generally divided into two types, i.e. substation inside and outside short-circuit fault. Compared with the substation outside short-circuit fault, the substation inside short-circuit fault has a greater threat to safe operation of the substation and more easily results in accidents. Therefore, the paper mainly studies the short-circuit fault inside the substation[1]. According to the regulations of the international standard GB/T50065-2011 “Code for Design of AC Electrical Installations Earthing”[2], before the substation grounding system is designed, not only does the short-circuit current magnitude needs to be checked, but also the short-circuit shunt situation of the power system needs to be analyzed, so that the current diffusing into ground is accurately calculated and a basis is provided for grounding grid design[3]. The shunt coefficient of the substation grounding system means a ratio of current diffusing into ground to short-circuit fault current. The reasonable selection of the shunt coefficient is taken as

the basis of the design of the substation grounding system[4]. The shunt coefficient can be obtained by two methods: an experience equation method and a numerical calculation method, wherein the experience equation method is simpler in model, but lower in calculation accuracy[5]; the numerical calculation method is more accurate in calculated results, but relatively complicated in model[6]. The paper focuses on introducing the numerical calculation method. 2 2.1

CALCULATION CONDITIONS Preparing the new file with the correct template

The substation studied in the paper is Fuzhou Ultra-High Voltage GIS Substation; there are two Table 1.

Substation.

Quantity of transformers

Capacity/MVA

Short-circuit voltage percentage/%

2

1000

18

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Table 2.

Ground lines. South Zhejiang No. 1 line

Lili No. 1 line

Ningde No. 1 line

Line name

South Zhejiang No. 2 line

Lili No. 2 line

Ningde No. 2 line

Models of ground wires DC resistance/(Ω/km) External diameters of ground wires/mm

JLB20A-240 0.1444 24.8

JLB40-150 0.2935 15.8

OPGW-150 0.3300 16.6

Table 3.

Conductors. South Zhejiang No. 1 line

Lili No. 1 line

Ningde No. 1 line

Line name

South Zhejiang No. 2 line

Lili No. 2 line

Ningde No. 2 line

Voltage level/kV Conductor model Split spacing/mm DC resistance/(Ω/km) Conductor external diameter/mm Span/m Line length/km

1000 8 × JL/G1A-500/45 400 0.0578 32.2

500 4 × JL/LB20A-720/50 400 0.0355 36.2

500 4 × JL/LB20A-720/50 400 0.0355 36.2

400 26.47

475 25.76

483 4.35

inlet lines which are single-loop lines in the same tower, i.e. a South Zhejiang No. 1 line and a South Zhejiang No. 2 respectively on 1000 kV side; and there are four inlet lines which are double-loop lines in the same tower, i.e. a Lili No. 1 line, a Lili No. 2 line, a Ningde No. 1 and a Ningde No. 2 line respectively arranged on 500 kV side. Two main transformers are three-phase autotransformers. 3

Figure 1.

CALCULATION METHOD OF SHUNT COEFFICIENT

3.1

Path of fault current inside substation.

Analysis on short-circuit current inside the substation

The overhead ground wires of the power transmission line and the substation grounding grid are connected, when short-circuit fault occurs inside the substation, the fault current is provided by the phase conductors of the line, i.e. the phase conductors are taken as current leads, as shown in Figure 1. When short-circuit fault occurs inside the substation, the current distribution is shown in Figure 2. The short-circuit fault current is I0. Current Ig can directly flow to the infinitely-remote end power supplies from the ground through the grounding system; and a part of current flows out of the grounding grid through the transformer neutral

Figure  2. Current distribution under the situation of short-circuit fault inside substation.

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points and the ground wires of the high and lowvoltage lines, i.e. IN, Iw1 and Iw2. When the current Iw1 and the current Iw2 flowing back to the power supplies, a part of them flows to ground through the tower grounding system, i.e. Iw12 and Iw22, and the rest current Iw11 and Iw21 continuously flow back to the power supplies though the ground wires. 3.2 Shunt coefficient of fault current inside substation The shunt coefficient can be divided into the shunt coefficient of the grounding grid and the shunt coefficient of the ground wires which respectively characterize the shunt ability of the overhead ground wires to the short-circuit fault current when short-circuit fault occurs inside the substation[7]. The shunt coefficient Ksl of the ground wires is generally given in the IEEE standard[5], which can be expressed as: K sl =

Iw I0 IN

1 K sl =

Ig I0

(2)

IN

In the practical engineering, it is the current Ig diffusing into ground that really causes the harm and bring along the safety problem. Thus the paper mainly pays close attention to the current Ig diffusing into ground and its shunt coefficient Ksg. The short-circuit fault current I0 is often provided in the engineering design. The current IN flowing through the transformer neutral points is complicated to calculate and not provided generally. Therefore, in order to facilitate the use by designer, Ig/I0 is defined as the shunt coefficient of the grounding grid, different from that regulated in the standard[8]. 3.3

YV = b

(4)

The fault situation can be stimulated through modifying the above equation. The voltage of all nodes can be obtained through solving the above equation, and the current distribution can be obtained through substituting the equation (3). The node equation including the fault current is shown as follows: YNV = bN

I

(5)

In the equation, YN is admittance matrix of the fault-free component; bN is independent current source vector of the fault-free components and I is current vector at the joint of the system and the fault component. The above equation can be decomposed as: ⎡Y11 Y12 ⎤ ⎡V1 ⎤ ⎡ b1 ⎤ ⎡ I1 ⎤ ⎢Y Y ⎥ ⎢V ⎥ = ⎢ b ⎥ + ⎢0 ⎥ 22 ⎦ ⎣ 2 ⎦ ⎣ 2 ⎦ ⎣ ⎦ ⎣ 21

(1)

In the equation, Iw is the sum of all short-circuit current flowing through the ground wires. The shunt coefficient Ksg of the grounding grid can be correspondingly expressed as: K sg

matrix; and bk is independent current source vector. The node equation form in the equation (3) is:

(6)

In the equation, the subscripts 1 are expressed as components connected with the fault components; the subscripts 2 are expressed as other components of the system; and I1 is current vector at the system terminal connected with the fault components. The above equation can be switched as: YeV1 = be

I1

(7)

In the equation, Ye is equivalent admittance matrix of the whole system except for the fault component; and be is equivalent independent current source, i.e. Ye Y11 − Y12Y22−1Y21 be

b1 − Y12Y2−21b2

(8) (9)

The ground potential rise of the grounding system and the current diffusing into ground from the substation grounding grid can be obtained, and further, the shunt coefficient can be calculated.

Calculation principle of shunt coefficient

All components in the power system can be stimulated by the generalized admittance matrix[10], it can be expressed as: I k YkVk

bk

(k

, ,

, n)

(3)

In the equation, Ik is component terminal’s current vector; Vk is component terminal’s voltage vector; Yk is component’s generalized admittance

4

4.1

CALCULATION ON SHUNT COEFFICIENT IN FUZHOU SUBSTATION AND CURRENT DIFFUSING INTO GROUND Calculation on shunt coefficient inside Fuzhou substation

As the tower grounding resistance may change, it respectively takes 10Ω, 15Ω, 25Ω and 35Ω which

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Table 5.

are used for calculating short-circuit current distributions and shunt coefficients on different shortcircuit sides under different substation grounding resistances. Ground Wire Shunt Coefficient under the Situation that the Tower Grounding Resistance is 10Ω. Seen from Figure 3, the ground wire shunt coefficient on 500 kV side and 1000 kV side linearly increases basically along with the increase of the substation grounding resistance, and the shunt coefficient on 1000 kV side is slightly larger than that on 500 kV side. Ground Wire Shunt Coefficient under the Situation that the Tower Grounding Resistance is 0.5Ω. The calculation results show that along with the increase of the substation grounding resistance, the ground wire shunt coefficient gradually increases; and along with the increase of the tower grounding resistance, the ground wire shunt coefficient gradually and slightly decreases. A tiny difference exists between the ground wire shunt coefficients when the short-circuit fault occurs on 500 kV side and 1000 kV side.

Table 4.

Tower grounding resistance is 15Ω. Ground wire shunt coefficient

Grounding resistance/Ω

500 kV side

1000 kV side

0.5 1 1.5 2 3 4

0.548 0.582 0.614 0.643 0.694 0.735

0.545 0.579 0.611 0.640 0.692 0.733

Table 6.

Tower grounding resistance is 25Ω. Ground wire shunt coefficient

Grounding resistance/Ω

500 kV side

1000 kV side

0.5 1 1.5 2 3 4

0.546 0.579 0.611 0.640 0.691 0.732

0.543 0.576 0.608 0.637 0.688 0.729

Tower grounding resistance is 10Ω. Table 7.

Ground wire shunt coefficient Grounding resistance/Ω

500 kV side

1000 kV side

0.5 1 1.5 2 3 4

0.550 0.584 0.616 0.645 0.697 0.738

0.547 0.581 0.614 0.642 0.695 0.736

Tower grounding resistance is 35Ω. Ground wire shunt coefficient

Figure 3. Ground wire shunt coefficient under the situation that the tower grounding resistance is 10Ω.

Grounding resistance/Ω

500 kV side

1000 kV side

0.5 1 1.5 2 3 4

0.543 0.576 0.608 0.637 0.687 0.728

0.540 0.573 0.605 0.634 0.685 0.726

Figure 4. Ground wire shunt coefficient under the situation that the tower grounding resistance is 0.5Ω.

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4.2

Calculation on short-circuit current diffusing into ground inside Fuzhou substation

The calculation results of short-circuit current show that under the most common single-phase short-circuit situation, the single-phase shortcircuit current on 1000 kV side and 500 kV side is 24.1 kA and 57.19 kA respectively. Due to a tidy difference between the ground wire shunt coefficients when the short-circuit fault occurs on 500 kV side and 1000 kV side, the actual maximum current diffusing into ground is analyzed by applying the single-phase short-circuit current, i.e. 57.19 kA on 500 kV side under the most dangerous situation, and the ground wire shunt coefficient is obtained when the tower grounding resistance is 10Ω. Seen from Figure 5, along with the increase of substation grounding resistance, the maximum current diffusing into ground gradually decreases and the ground potential rise gradually increases. Therefore, in the design of the substation grounding system, not only the shunt coefficient (maximum current diffusing into ground) needs to be concerned, but also the effect of grounding resistTable  8. Relationship between short-circuit current diffusing into ground in substation and grounding resistance. Grounding resistance/Ω

Maximum current diffusing into ground/kA

Ground potential rise/kV

0.5 1 1.5 2 3 4

25.74 23.79 21.96 20.30 17.33 14.98

12.87 23.79 32.94 40.60 51.99 59.94

ance on ground potential rise must be considered; and through carrying out reasonable resistance reduction on the substation, the ground potential rise is effectively reduced, so that the substation safety is improved. 5

CONCLUSIONS

The paper systematically studies the distribution of the short-circuit current and the calculation on shunt coefficient when fault occurs inside the substation. The results show that: 1. The substation shunt coefficient is decided by current distribution in the system. The factors affecting the shunt coefficient include the grounding resistance of the substation grounding grid, the grounding resistance of the tower grounding device, the ground wire self impedance, the phase conductor self impedance, the electrical parameters of the overhead ground wires and the like. 2. The ground wire shunt coefficient increases along with the increase of substation grounding resistance and decreases along with the increase of the tower grounding resistance. There is a tiny difference between the ground wire shunt coefficients when the short-circuit fault occurs on 500 kV and 1000 kV. 3. Along with the increase of substation grounding resistance, the maximum current diffusing into ground obviously decreases, and the ground potential rise gradually increases. Therefore, it is necessary to carry out reasonable resistance reduction design on the substation. REFERENCES

Figure  5. Relationship between short-circuit current diffusing into ground and grounding resistance.

[1] He Jinliang, Zeng Rong. Power system grounding techniques [M]. Beijing, China: Science Press, 2007. [2] China Electricity Council. GB 50065-2011 “Code for Design of AC Electrical Installations Earthing” [S]. Beijing: China Planning Press, 2011. [3] Cao Wei, Wang Yongsheng, Zhang Wenqing, et al. Analysis on DC component in short-circuit current of power grid and its influence on breaking ability of circuit breakers [J]. Power System Technology, 2012, 36(3): 283–288. [4] Li Qian, Jiang Yukuan, Xiao Leishi, et al. Measurement and analysis on short-circuit current shunt coefficient inside substation [J]. Power System Technology, 2013, 37(7): 2060–2065. [5] Substations Committee of the IEEE Power Engineering Society. IEEE Guide for Safety of AC Substation Groundings [S]. New York, USA: IEEE, 2000. [6] He Jinliang, Zhang Bo, Zeng Rong, et al. Grounding system design of 1000  kV ultra-high voltage substation [J]. Proceedings of the CSEE, 2009, 29(7): 7–12.

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[7] Zou Jun, Yuan Jiansheng, Zhou Yukun, et al. Uniform generalized double-sided elimination method and the calculation of the fault current distribution for hybrid overhead-underground power lines [J]. Proceedings of the CSEE, 2002, 22(10): 112–115. [8] Sun Xu, Lu Jiayu, Xu Daming, et al. Simulation test of shunt coefficient of short circuit current [J]. High Voltage Engineering, 2001, 21(8): 77–79.

[9] Huang Ruifeng, Li Lin. A novel phase-coordinate transformer model and its application to uniform generalized double-sided elimination method [J]. Proceedings of the CSEE, 2004, 24(7): 188–193. [10] Meliopoulos A.P., Webb R.P., Joy E.B., et al. Computation of maximum earth current in substation switchyards [J]. IEEE Transactions on Power Apparatus and Systems, 1983, 102(9): 3131–3139.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Mechanical analysis of reliability test device of Head-Up Display’s retraction and extension Y.G. Liu & Y.X. Wang School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

ABSTRACT: This essay designs a kind of unbiased crank rocker mechanism with a certain tilt angle as the reliability test device of Head-Up Display (HUD)’s retraction and extension. Divide the test motion into several stages, build the mathematic model of angle relationship between crank and rocker, and calculate the torque of the crank axle by superimposing the moment of inertia and gravity. The simulation result shows that the whole process of HUD’s retraction and extension is relatively stable. Keywords: 1 1.1

reliability of HUD’s motion; unbiased; certain tilt angle; crank rocker; crank axle torque

INTRODUCTION HUD reliability test

As a military product, Head-Up Display (HUD) is requested a long service life, even millions of times of stable folding. For HUD’s motion of retraction and extension with a certain angle, by using several driving methods at the present, such as hydraulic actuator or steel wire rope driven by motor, the actuating devices would be easy to get damaged under repeated cyclic loading, and could not normally complete the tests. What is more, it is of a great challenge to keep the stable control of the test drive. 1.2

Crank rocker mechanism

As is shown in Figure 1, crank rocker mechanism can transmit the reciprocating folding of HUD to the continual rotating of the crank. It can lower the degree of the driver device’s fatigue, and can easily control the swing angle. The quick-return

characteristic may cause the inconformity of the motion. For keeping the smoothness of the motion, the unbiased arrangement form to avoid the cyclic concussive caused by quick-return motion is used, and then the dynamic performance is improved. Methods of eliminating the quick-return characteristic and the motion property of unbiased bar joint have been correlationally researched. A crank mechanism is widely applied to drive telescoping, and the axle torque is generally derived from the bar’s directional compression. But the torque of HUD in the process of retraction and extension is effective by several factors. This article builds the mathematic model of angle relationship between crank and rocker, calculates the torque of the crank by superimposing the moment of inertia and gravity, and finally verifies the property of the mechanism by simulation analysis. That is of great reference value for structure design of reliability simulation experiments. 2

Figure  1. motion.

Crank rocker mechanism used for HUD

THE ANGLES RELATIONSHIP BETWEEN CRANK AND ROCKER

The movement process of crank rocker is shown in Figure 2. AB stands for crank, BC is bar, and CD is rocker. DC0 and DC4 are two extreme positions of the rocker. B1 and B2 are on line AD, stand for two cranks’ special positions. When crank AB revolves around for angle α with a constant speed, the angle between rocker DC and AD is β. In the motion, BC and AD may cross, so it is analyzed by grading different stages: the crank rotates counterclockwise from AB1 to AB2 (α ∈ [0,π)), called stage 1; the crank rotates

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Figure 2.

Motion of the crank rocker mechanism.

counterclockwise from AB2 back to AB1 (α ∈ [π, 2π)), called stage 2. Stage 1: as is shown in Figure  3. In triangles ABD and BCD, based on the law of cosines: cos β1 = cos β 2 = cos α =

h2

R2 − l 2 2⋅h⋅R

(1)

h2

d 2 − r2 2⋅h⋅d

(2)

d2

r 2 − h2 2⋅r⋅d

Figure 3.

Motion of crank rocker in stage 1.

Figure 4.

Motion of crank rocker in stage 2.

(3)

By β = β1 + β2, then we obtain that r2 + d 2

h

β = arccos

r ⋅ d ⋅ cos α

(4)

⎛ 2 ⎞ ⎛ d − r ⋅ cos α ⎞ + arccos ⎜ R − r ⋅ d ⋅ cos α ⎟ ⎝ ⎠ ⎝ ⎠ h h⋅R (5)

Stage 2: as is shown in Figure 4. Similarly, based on the law of cosines and β = β1 − β2: ⎛ 2 ⎞ ⎛ d − r ⋅ cos α ⎞ β = arccos ⎜ R − d ⋅ cos α ⎟ − arccos ⎝ ⎠ ⎝ ⎠ h h R

Table 1.

The chosen key parameters.

Group

Ψ (°)

r (mm)

Group

Ψ (°)

r (mm)

Gr 1 Gr 2 Gr 3 Gr 4

107 107 107 107

50 100 150 200

Gr 5 Gr 6 Gr 7 Gr 8

107 88 73 60

50 50 50 50

(6) The swing angle is constant value ψ, and the quick return angle is 0. Then the relationship between the length of crank (r) and rocker (R) is: r

R ⋅ sin(ψ / )

(7)

Here we choose several key parameters shown in Table  1. Build the simulation curves by Matlab software, and get the results in the graph (Figs. 5 and 6). We obtain the following conclusion: (a) the motion of crank rocker eliminating the quite return is approximate sinusoidal; (b) the total swing angle can control in any value, and would not effected by the length of crank, but it would be unstable in the case of overlong.

Figure 5.

Simulation curves of angles (1).

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Figure 6.

3

Figure 7.

Simulation curves of angles (2).

TORQUE OF CRANK AXLE

Obtain torque TF from the gravity of the load:

The dynamics is analyzed as below as shown in Figure 7: (a) Assuming the load is much heavier than the crank and the bar, the rocker mainly bears the load gravity moment; (b) in the swing process, it continually bears the inertial moment from the mass center G. So the torque amounting to axle A mainly comes from gravity and inertial moment of the load. Due to the change of direction of the gravity moment and inertial moment following the change of position and direction of the rocker, we consider the limit positions of the rocker as the boundaries, and divide one cycle averagely into four periods to analyze the bearing torque. 3.1

G rr r ⋅ cos((ψ 0

β0

β 0 = arccos

β )),

d 2 + R 2 − ( + )2 , 2⋅d R

(8)

(9)

where ψ0 is the initial angle of the crank, and (180° 180 ψ ) / 2,

(10)

γ is the angle between bar and rocker, and l 2 + R 2 − h2 γ = arccos . 2⋅l R

G rr r cos(ψ 0 + β 0 − β ) ⋅ ⋅ sin( β + α + γ )). R sin γ (12)

If we consider the facing perpendicular to the paper as the positive direction, so the direction of TF is positive; The other stages are shown in Figure  8. First we calculate the gravity of the load converting into force F on the bar, the directions are all from C to B; the methods of β0, ψ0, γ are similar to stage 1. Obtain the torque of the other stages: TF =

G rr r cos(ψ 0 + β 0 − β ) ⋅ ⋅ sin( β + α + γ ) (13) R sin γ

The directions are positive, negative and negative.

where β0 is the angle between C0D and AD, and

ψ0

TF =

Gravity moment of the load

The first period is shown in Figure  7. The swing angle is ψ, the gravity is in negative direction of Y axis, the limit C0D is the initial position of the crank, and the distance between central mass G and axle D is rr. F is load gravity equating to the force on the bar, and its direction is from C to B along the bar. According to the moment balance: F R ⋅ sin γ

Dynamics analysis diagram.

(11)

3.2

Inertial moment of the load

Similarly, divide into the same four periods. According to the law of inertial moment, we get inertial moment in the four stages: m ⋅ rr 2 ⋅ β

TI

(14)

Inertial moment directly transfers to the bar. Considering the law of moment balance, get the torque in axle A: TL =

m rr 2 ⋅ β ⋅ r ⋅ β +α +γ ) . R ⋅ sin γ

(15)

The directions are positive, positive, negative, and negative. Superimposing the moment of inertia and gravity, get the whole torque in axle A: T=

G rr ⋅ cos(ψ 0 + β 0 − β ) + ⋅ R ⋅sin γ

2

⋅ β

⋅ r ⋅sin( β + α + γ ) (16)

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Figure 8.

Four stages of the torque analysis.

Figure 10.

Angle simulation curves (2).

Table 2.

The chosen key parameters.

Group

r (mm)

l (mm)

Group

r (mm)

l (mm)

In practice, that can be avoided by adjusting the structure parameters.

Gr 1 Gr 2 Gr3 Gr 4

30 30 30 30

550 400 250 100

Gr 5 Gr 6 Gr7 Gr 8

30 60 90 120

400 400 400 400

4

Figure 9.

CONCLUSION

For the unbiased crank rocker mechanism with a certain tilt angle as the reliability test device of HUD, we build the motion model of crank and rocker’s angles by stages. According to the simulation result we find the angle curve varies nearly as a sinusoidal wave, and the whole swing angle can be controlled. Calculate the crank axle torque by superimposing the moment of inertia and gravity, and find that in the first half of the swing cycle, the torque is nearly a sine wave, but in the last half, it differs based on the case of size matching condition of crank and bar. In the case of “size mismatching”, a serious instability can occur in the whole crank axle torque. That is of great reference value for the structure design of reliabile simulation experiments.

Angle simulation curves (1).

Using the relationship of α and β, and choose parameters as shown in Table 2, and get the results in the graph (Figs. 9 and 10). We obtain the following conclusion: (1) in the first half of the cycle, torque of rocker is nearly a sine wave, and seldom affects the size of the mechanism; (2) in the last half of cycle, if bar is far longer than rocker (5∼7 times), the curves of rocker torque are similar to the first half, but it will first decay and then increase a little, called “size matching”. If the length of the bar and rocker is relatively close (3–4 times), the curves will lag and sharply increase with appearance of instability, called “size mismatching”. The mechanism is unbiased with a certain tilt angle, so the moment rocker swings through the limit position, the angle between rocker and bar is relatively small and rapidly changes, that is the reason of “size mismatching” causing instability.

REFERENCES Chongzhen, C. & Chunyu, Z. 2009. Unbiased Crank and Rocker Mechanism with 90°Tilt Angle and Its Application. Journal of Shandong University of Science and Technology. Vol. 28 No. 3, 2009, 45–48. Hongxian, W. & Caijun, X. 2010. On the Design and Application of a Fatigue Testing System for Landinggear Retraction and Extension. Journal of Experimental Mechanics. Vol. 25 No. 2, 2010, 173–180. Jia, W. & Daohua, L. 2011. Research of Simulation Integrated Testing System for Boat Davit with Wave Compensating Function. Ship Engineering. Vol. 33 No. 6, 2011, 37–39. Xiaoyan, Y. & Zhaohui, L. 2007. New Discussion on the Synthesis of Crank-Rocker Mechanism with Quick Return Characteristic. Machine Design and Research. Vol. 23 No. 6, 2007, 43–50. Zhixin, H. & Xuehong, L. 2007. Dynamic Characteristics of IC engines with Offset Crank-Slider Mechanism. Small Internal Combustion Engine and Motorcycle. Vol. 36 No. 1, 2007, 39–40.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study on electromagnetic loop rejection in Liaoning power grid Kai Gao State Grid Liaoning Electric Power Supply Co. Ltd., Shenyang, China

Zhen-Hao Wang Northeast Dianli University, Jilin, China

ABSTRACT: Electromagnetic loop network is a particular form of network configuration in the development of power network, which is of great importance to improving the transmission ability of a power grid. In recent years, with the power grid expanding, the closed-loop operation problem is becoming more and more prominent, such as the difficulty to control power flow, short-circuit current superscalar issue and the destruction of the system thermal stability. First, the basic concepts of electromagnetic loop network are introduced, then the basic principles and general steps of breaking up electromagnetic loop are summed up, and finally according to the characteristics of Liaoning power network, the scheme of electromagnetic loop rejection is designed through a multi-faceted calculation and analysis. The research results of this paper offer theoretical basis for reasonably determining the location to break up the electromagnetic loop of the Liaoning power grid. Keywords: 1

electromagnetic loop network; short-circuit current superscalar; security and stability

INTRODUCTION

In recent years, with the power grid expanding, the closed loop operation problem is becoming more and more prominent (Chunqing et al. 2005). The transmission power of urban power networks grows rapidly with the development of power systems. Therefore, higher voltage level transmission lines must be built in urban power networks accordingly. In this process, China’s power grids have two typical characteristics (Wei et  al. 2007): first, the problems of short-circuit current superscalar are serious; second, there are many electromagnetic loops, which lead to the limitations of power transmission capacity. As the improvements of grid structure, it is of great significance to break up the electromagnetic loop network and realize the power grid hierarchical division operation to enhance the economy and safety operation of power systems. Due to the complexity and diversity of power grid development, how to divide the power grid is a hot topic deserved to explore and research (Zhao et  al. 2004). In the early stage of high-voltage power grid development, the power flow of high-low loop network is little. In this case, the electromagnetic loops can improve the reliability and flexibility of power grids. But with the development of high-level voltage grid, the transmission load is increasing, and

the electromagnetic loops become serious potential hazards (Xiang et al. 2004). The main hidden danger of the electromagnetic loop network mode is that large amounts of power flow pour into the lower voltage grid when the higher voltage grid is cut off, it can easily cause serious problems threatening the safe and stable operation of power systems (Shuyong et  al. 2008). This operation mode also leads many potential problems on security and stability for power grids, such as overload, the difficulty to control power flow, short-circuit current superscalar issue, complexity of protection setting, and even the destruction of the system thermal stability. A mainstream approach of eliminating the loop running is electromagnetic loop rejection to achieve the partitioning operation of power networks (Canizares et al. 1993). Because of the complexity of power system operation, multi-faceted calculation and analysis must be carried out before breaking up the electromagnetic loop, comprising power flow calculation, stability calculation, short-circuit current calculation, and network loss calculation. Moreover, other issues affecting the system operation should also be comprehensively considered to make reasonable and feasible decisions (Dong et  al. 2009). Therefore, it has an important theoretical significance and application value to study the scheme of breaking up electromagnetic loop networks.

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2

due to the presence of multiple subsystems operating independently. Each subsystem makes construction of power grids in accordance with their own ideas and habits, which will inevitably lead to the parallel operation situation of different voltage levels grids.

METHODS OF ELECTROMAGNETIC LOOP REJECTION

2.1

Concept and causes of electromagnetic loop

Electromagnetic loop refers to the lines with different voltage levels connected in parallel loop through the electromagnetic circuit of the connected transformers (Yihan et al. 2009), as shown in Figure 1. In Figure 1, the operating voltage U1 of line L1 is higher than the operating voltage U2 of line L2, T1 and T2 are transformers, lines L1 and L2 constitute an electromagnetic loop through the magnetic circuits of T1 and T2. Electromagnetic ring networks are the products of the process of power grid construction. When the high-level grid is not strong, there is rationality of its existence. But when high-level grid development is more mature, it should be considered to break up the electromagnetic ring network to ensure the hierarchical partitioning operation of power systems (Haihui et al. 2005). The main reasons why electromagnetic rings appear are described as follows: 1. Some planning managers of power grids think that the use of electromagnetic loop network operation mode can improve the reliability of the local power supply, so that the power supply network can be enhanced, and construction investment in power networks can also be saved. They also think that the shortcomings of this electromagnetic ring are relatively minor. 2. In the planning process, planners fail to conduct a reasonable forecasting and analysis for supply networks, which leads to some lines not meeting the requirements for subsequent transmission capacity. To solve this problem, new higher levels of transmission lines are often added, and eventually leading to the formation of electromagnetic ring network. 3. In the operation and management of power grids, management is not concentrated enough

2.2

The basic principles of breaking up electromagnetic loop networks are described as follows (Yangyu et al. 2011). 1. The loop rejection should result in more reasonable flow distribution. Power flows should have more flexible controllability, and be able to adapt to the changes in various operating modes. It should also ensure that no element overloads. 2. The loop rejection should be able to ensure the security and stability of power systems. The stability indexes should be compared before and after breaking up the loops. If power flow transferring caused by disconnecting the high voltage lines seriously affected the stability of power systems, it is an urgent need to breaking up the electromagnetic loops. 3. After breaking up the loops, the power networks should have a larger anti-jamming capability to meet the N-1 requirements. 4. After breaking up the loops, power supplies should be reasonably allocated among supply districts to provide sufficient reactive power compensation for each partition. 5. After breaking up the loops, each partition should have sufficient connected aisles with reasonable distributions to timely transfer loads and avoid blackouts when an accident occurs. 6. The short-circuit current usually decreases after breaking up the loops, and then it needs to determine that what kind of line operation mode is most effective in reducing the shortcircuit current. 7. The network loss after breaking up the loops must be calculated, and be compared with the network loss before breaking up the loops. 2.3

Figure 1.

Schematic of an electromagnetic loop.

Basic principles of breaking up electromagnetic loop networks

Steps of breaking up electromagnetic loop networks

Because of the purpose of electromagnetic loop rejection is to solve the potential problems in power grids, the status of power grids after should be fully taken into account, and must not bring new problems to the open-loop operation of electromagnetic loop networks (Qiantu 2005).

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The decision analysis steps of electromagnetic loop rejection can be summarized as follows (Xiang et al. 2004). 1. The present situation of electromagnetic loops under typical operating modes should be analyzed, according to the actual situations of the power grid including supply network structure, network construction and future planning, and load forecasting. 2. The weak links that may cause larger-scale power flow transferring and poor stability of power networks when an accident occurs are identified through multi-faceted calculation and analysis under typical operating modes of electromagnetic loops. 3. According to the principles of electromagnetic loop rejection, proper lines are selected to breaking up the loops. And then, several schemes of electromagnetic loop rejection are proposed through the calculation and argumentation of the indicators. 4. The optimal scheme for electromagnetic loop rejection is determined through a comprehensive assessment and economic calculation of the candidate schemes. 3 3.1

NUMERICAL EXAMPLES

3.3

Method of calculating short-circuit current

Short-circuit current levels for the whole network is scanned by using the transient stability calculation procedure PSD-SWNT based on the principle of superposition. According to the equivalent network and the normal voltage under normal operating conditions, the normal current of each branch can be obtained. Then, by using the equivalent network with identified fault components, each node voltage and each branch current caused by a fault can be also obtained. And finally, by means of adding them, the actual voltage of each node and each branch current after a fault can be obtained.

Introduction of examples

The power system considered is Liaoning power grid, covering an area of 148,000 square kilometers is a highly interconnected grid with an approximate installed capacity of 39657.2 MW. The system lies in the south of Northeast power grid, and it is the load center of the Northeast power grid. According to the power load layout and grid structure, the grid is divided into three parts: western power system, north-central power system and southern power system. By 2015, the north-central system of Liaoning grid will be from 500 kV loop network structures and intensive 220 kV power grid, which leads to serious short-circuit current superscalar issues. In this study, the simulation software is PSDBPA developed by China Electric Power Research Institute, mainly comprising the power flow calculation procedure PSD-PFNT, the transient stability calculation procedure PSD-SWNT, the drawing procedure of geography wiring diagram PSD-CLIQUE, and the multi-curve comparison program PSD-MYCHART. 3.2

frequency stability. Note that the system is stable only when all the three stabilities remain stable. The specific criterions are listed as follows: Transient stability: when a power system fails, rocking curve of relative angles in any two machines is synchronous damped oscillations. Voltage stability: for a disturbed power system, the load bus voltage can be restored to 0.80 pu and 0.90 pu above in the transient process and longterm process, respectively. Frequency stability: the system frequency can be quickly restored to near the nominal frequency, and the frequency crashes does not occur.

Stability criterion

Power system stability can be divided into three types: angle stability, voltage stability, and

3.4

Scheme of electromagnetic loop rejection

Anshan regional power grid is the focus of this study, because it is currently the most serious region of short-circuit current superscalar issue in Liaoning power gird. A scheme of electromagnetic loop rejection is designed through multi-faceted calculation and analysis. The proposed scheme is the following lines should be disconnected, comprising QianGangCaoHeKou, NiuZhuang-DongChang, and Cheng Ang-HongQiBao, as shown in Figure 2. 3.5

Scheme checkout

1. Short-circuit current level According to the partition scheme, the shortcircuit current levels in the area are shown in Table 1. From Table 1, it can be seen that the threephase short-circuit current of 220 kV bus in the ChengAng substation (the most serious substation of short-circuit current superscalar issue in Liaoning power grid) has been reduced from 49.38 kV to 39.05 kV, and it s single-phase short-circuit current has been reduced from 56.39 kV to 45.18 kV. Both of them are controlled under 50 kA, which meets the requirements

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determined through multi-faceted calculation and analysis, comprising power flow calculation, stability calculation, short-circuit current calculation, and network loss calculation. The simulation results show that the proposed scheme can effectively solve the problem of shortcircuit current superscalar for Liaoning power grid. Furthermore, the methodology of breaking up the electromagnetic loop network may be applied to any similar problems in engineering field. REFERENCES

Figure 2.

Schematic of the proposed scheme.

Table 1. Short-circuit current levels in different operation modes. Operation mode

Three-phase SCC (kA)

Single-phase SCC (kA)

Closed-loop operation Open-loop operation

49.38 39.05

56.39 45.18

of rated breaking current for beakers and have a certain margin of safety. Therefore, the proposed scheme can effectively reduce the shortcircuit current level. 2. Other checkouts In order to properly evaluate the performance of the presented scheme, multi-faceted calculation and analysis are carried out in this operating mode, including power flow calculation, stability calculation, and network loss calculation. The simulation results demonstrate that the proposed scheme meets the requirements for power system safe operation, which has no thermal stability and N-1 problem. Moreover, the network loss after breaking up the loops has reduced compared with that of under the mode of electromagnetic loop. 4

CONCLUSIONS

The basic concepts of the electromagnetic loop are introduced in this paper. The electromagnetic loop rejection scheme for Liaoning power grid is

Canizares C.A, Alvarado F.L 1993. Point of collapse and continuation methods for large AC/DC systems. IEEE Trans on Power Systems 8(1): 1–8. Chunqing Hou, Huiping Zheng 2005. Research on parallel operation of 500 kV and 220 kV power networks in south and central part of Shanxi power grid in 2005. Power System Technology 27(10): 80–84 (in Chinese). Dong Yang, Yutian Liu 2009. Influence of electromagnetic loop in early ultra-high voltage grid. Electric Power Automation Equipment 29(6): 77–84 (in Chinese). Haihui Cheng 2005. Analysis of breaking the 500/220 kV electromagnetic circuits. Jiangsu Electrical Engineering 24(5): 38–40 (in Chinese). Qiantu Ruan (2005). Present situation of short circuit current control in Shanghai power grid and countermeasures. Power System Technology, 29(2): 78–83 (in Chinese). Shuyong Liu, Qiang Gu, Lijuan Zhang, et a1 2008. Research on power supply scheme based on partitioning of 500/220 kV Tianjin power grid during the 1lth Five-Year plan. Power System Techno1ogy 32(9): 51–55 (in Chinese). Wei Pan, Wenying Liu, Yihan Yang, et a1 2007. Research on operating modes of 750 kV Northwest China power grid electro-magnetically coupled with 330 kV power grid. Power System Technology 31(15): 33–38 (in Chinese). Xiang Xia, Jun Xiong, Liexiang Hu 2004. Analysis and control of loop power flow in regional power network. Power System Technology 28(22): 76–80 (in Chinese). Yangyu Hu, Hongjun Fu, Yiming Zhang, et  al (2011). Analysis on electromagnetically coupled loop operation of tie line connecting Hubei power grid and Henan power grid after building up of Nanyang UHVAC substation. Power System Technology, 35(6): 22–26 (in Chinese). Yihan Yang, Lin Ai, Tong Jiang, et al 2009. Risk assessment and control of electromagnetically coupled power loop based on benefit risk function. Power System Technology 33(7): 65–70 (in Chinese). Zhao He 2004. A discussion on scheme of improving structure of East China power grid to reduce short circuit capability. Power System Technology 28(21): 28–31 (in Chinese).

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study on effect of evaluating corrosion inhibitor for circulating cooling water in dynamic simulation method Yan-Ping Li Stage Grid of China Technology College, Jinan, Shandong, China

Yong Li Stage Grid Shandong Electric Power Company, Jinan, Shandong, China

Fang Liu Stage Grid of China Technology College, Jinan, Shandong, China

ABSTRACT: Based on the quality of recharged circulating cooling water in power generation, the experiment helps to choose the corrosion inhibitor and the exact relative quantity through preliminary enrichment ratio test. Evaluating the effect on the basis of surface condition of the tube during dynamic simulation test, fouling resistance, quality of circulating cooling water and so on, we determine the quantity of corrosion during actual operation, concentration ratio and other operating parameters. The water quality in actual operation verifies the feasibility and reliability of the evaluation method. Keywords: resistance 1

circulating cooling water treatment; corrosion inhibitor; dynamic simulation method; fouling

INTRODUCTION

In a coal-fired power plant designed with capacity expansion for the 2 × 1000 MW, circulating cooling water is recharged by local sewage treatment plant effluent. Due to the uncertainty quality of the recharged water, when used as supplement water, in addition to the regular production of scaling, corrosion, bacterium and so on, we need to pay special attention to ammonia nitrogen and phosphorus which may cause problems. In a circulatory cooling water system when water reaches a certain concentration ratio, scaling and corrosion of metal materials occur, influencing the safe and stable operation. Adding corrosion inhibitor is a major means of controlling water fouling. Corrosion varies with different quality of water and treatment methods. Therefore, evaluating the practiced effect of corrosion inhibitor is particularly important. The purpose of this experiment is to examine the effect of two kinds of corrosion inhibitors chosen based on the early concentrate rate test when used to current water and simulation unit operation so that the best corrosion inhibitor and other conditions such as additive concentration can be determined, guiding the design for the circulation cooling water system, the determination of the technological process, equipment selection and controlling operation after the production.

Reclaimed water has been conducted advancedly during pre-processing. The process is: sewage plant effluent—lime softening—BAF—water quality adjustment (adding sulfuric acid aiming at pH 8.3) -supplement for circulating cooling water. 2

DYNAMIC SIMULATION EXPERIMENT

2.1 Apparatus for experiment The WDM-D dynamic device for simulation of circulating water is as shown in Figure 1, the system process is shown in Figure 2.

Figure 1. The WDM-D dynamic device for simulation of circulating water.

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Figure 2.

dynamic simulation. The section is equipped with the simulated condenser, a condenser pipe section of Φ 25 × 0.5, and length of 650. The section is also equipped in the exchanger, the material of which should be the same as the generator, in another way, the pipe is made with 317  L stainless steel. Fouling tendency in the water side during experiment is monitored. The inspection on corrosion condition is needed after the experiment.

The system process.

When run for the first time, steps are as follows, adding enough supplemental water with corrosion inhibitor in the circulating water tank 1, opening water circulating pump 2, thus making cooling water flowing through the simulated heat exchanger 5, transferring heat to the steam from the generator, steam cooling into condensation and flowing back, temperature of cooling water raised, when flowing through the cooling tower, temperature dropped due to the spray 7, automatic operation 8 charging supplemental water into the tank to ensure the water balance. Flow sensor 3 controls flow velocity. Temperature sensors 4 and 6 monitor the temperature of water inlet and outlet in simulated heat exchanger. Simulated heat exchanger is equipped with a condenser tube, by which the tubing corrosion, fouling and fouling resistance can be measured during the operation. Collected from the circulating water tank 1, circulating cooling water is analyzed for pH, conductivity, turbidity, residual chlorine, phenolphthalein, alkalinity, total alkalinity, hardness, Ca. hardness, and so on. 2.2 Method Referred to “Method of Cooling Water Dynamic Simulation Experiment” (HG/T 2160-2008), the experiment is conducted. Mainly to complete the experiment on three aspects: 2.2.1 Experiment on water quality Considering the uncertainty of water quantity in practice, experiments are separately conducted when using reclaimed water, mixing water (1 + 1) as supplementary, completely simulating the operation of cooling water system for power generator. Evaluating the effect by analyzing the quality of cooling water and the changing trend of fouling resistance, inspecting, and evaluating the whole process and the rationality of controlling circulating cooling water and determining the final dosage, stability of water quality, and concentration ratio during operation. 2.2.2

Experiment on corrosion and fouling in condenser pipe Experiment on corrosion of pipe with circulating cooling water is conducted at the same time with

2.2.3 Experiment on corrosion of metal material in circulating cooling water system Preparation for all kinds of metal specimens is related to the cooling water system in the power plant before experiment. Oil removal and cleaning the specimens are done. Then we dry them to constant weight, weigh and number them before hanging them into the pool. After the experiment, we weigh to examine the corrosion or pitting to the specimens caused by circulating cooling water. 3 3.1

PARAMETERS FOR EXPERIMENT AND WATER QUALITY Parameters for experiment

The material of the power plant condenser pipe in practice is 317 L stainless steel, Φ 25 mm × 0.5 mm, in length of 650  mm (effective heat conduction length is 570 mm); water volume of system, 240 L; flow rate of circulating cooling water, 600 l/h; velocity, 0.4 m/s; operating duration, 15 days; corrosion inhibitor, SD2316; dosage in supplement water, 8 mg/L; metal specimens for corrosion, 317  L stainless steel, 316 L stainless steel, 1Cr18 Ni9Ti, C.I., steel 20 # copper B30. 3.2

Quality of reclaimed water

Claimed water quality in experiment is presented in Table 1. 4 4.1

RESULTS Curves during experiment

This experiment is aimed at the maximum stable concentration ratio of circulating cooling water when added with small quantity of corrosion inhibitor after the lime treatment—pH adjustment filter—disinfection and other advanced treatment. Curves during experiment are as shown in Figure 3, and fouling resistance curve is shown in Figure 4.

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Table 1. Item

Analyses on quality of advanced treated reclaimed water. Residual Conductivity Turbidity chlorine

pH

Value 8.21 Item CODCr

958 μS/cm BOD5

Value 33.6 mg/L 2 mg/L

Figure 3.

Phenolphthalein Total alkalinity alkalinity

1.2 NTU 0.25 mg/L 0 mmol/L Total P Activity Oil SiO2 0 mg/L 10.8 mg/L 0.18 mg/L

Full hardness

Calcium stiffness

1.0 mmol/L 3.7 mmol/L 2.1 mmol/L Chloride NH3-N SO42− 154.1 mg/L

263.0 mg/L

3.3

Curves during experiment. Figure 4.

Fouling resistance curve.

Table 2. (When SD2316 8 mg/L is added to reclaimed water) The condition of attachment in condenser and the stable quality of circulating cooling water after experiment. Weight before experiment (g)

Weight after experiment (g)

Amount of sediment g/m2

194.59

195.31

16.76

Appearance inspection condition Inner surface exit with small amount of hoary attachment which could be easily erased. The surface of the metal is bright like a new one, clean and smooth. 0.58

(After the operation of the experiment)yearly fouling thermal resistance × 10–4 m2 C/W (Test concentration ratio of stable operation) water quality of circulating cooling water Basicity mmol/L

Hardness mmol/L

Calcium stiffness mmol/L

Chloride mg/L

pH

DD μs/cm

3.95–4.2

17–18.8

10.5–11.5

749–792

8.55–8.70

4550–4600

4.2

Discussion

1. When cooling water is concentrated to 5.5 times, the monitoring analyses on water quality and fouling in simulated condenser indicated that it turns to fouling; at that moment, by adjusting the sewage to reduce cooling water concentration ratio to 5.1 times lower, the chlorine concentration ratio and difference between concentration ratio of hardness are kept less than 0.2, and two aspects of fouling resistance value in the condenser without rising meet the standard. Quality of circulating cooling water

is stable, concluding that during the operation in five times of concentration rate, there is no fouling attached to the surface of transfer. 2. During the later period of experiment, concentration ratio is controlled at approximately 4.8–5.1. Quality of circulating cooling water and condenser tube is represented in Table 2. Draft of supplementing water and circulating cooling water treatment is as follows. The concentration ratio can only maintain five times at maximum when operating, and there will be a risk of fouling when concentration ratio increases again.

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5

REFERENCES

OPERATION IN PRACTICE

Now, the generator has been put into operation for almost 2 years. Cooling water comes mainly from reclaimed water. The system is regularly cleaned by rubber ball. SD2316 is used as the corrosion inhibitor, with a dosage of 8 mg/L, concentration ratio of 3.5–3.5, Δ B < 0.2. In the routine check, surface of stainless steel condenser is cleaning, without fouling and corrosion. The effect of condenser operation indicates that the inhibitors based on the experiment are in accordance with water quality, that parameters such as dosage, concentration rate firs the situation in practice and that this evaluation method is reliable and feasible.

[1] L.G. Xu, Selection of Technical Scheme on using city’s recycled water to Circulating Water of power plant Shandong Electric Technology, [J] 2004 (4): 10–14. [2] B.Y. Shen, Study on the Effect of corrosion resistance of Recycled Water Treated by Lime and used in recirculation cooling water system. [J] Journal of Neimenggu University, 2011(3): 334–336. [3] H.M. Li, Evaluation and Application on water from power plant to handle pharmaceutical lab. [J] Industrial Water Treatment, 2003 (7): 73–75. [4] L.G. Xu, Water Treatment Technologies in Heatengine Plant [M] Beijing: China Electric Power Press, 2007.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on the impulse current dispersal characteristics of tower grounding devices Chang-Cheng Zhu & Tao Wang State Grid Hubei Electric Power Research Institute, Wuhan, Hubei Province, China

Xue-Fang Tong China Electric Power Research Institute, Wuhan, Hubei Province, China

Peng-Xiang Xing, Zhi-Qiang Feng, Hai-Liang Lu & Lan Lan School of Electrical Engineering, Wuhan University, Wuhan, Hubei Province, China

ABSTRACT: The impulse simulation tests of a single horizontal grounding device were carried out in the laboratory and the impulse current dispersal characteristics of grounding devices with different lengths are analyzed in this paper. The test results are also verified by the modeling and simulation calculation. Both the impulse tests and simulation calculation indicate that the impulse current dispersal characteristics of a single horizontal grounding electrode present a U-shaped distribution along with the grounding electrodes. The end effect, length of the electrodes, and soil resistivity are the key parameters that affect the impulse current dispersal characteristics of grounding devices. Keywords: 1

transmission line; tower grounding devices; impulse current dispersal

TEST PROCEDURE

Small-scale experiments were carried out in the grounding laboratory in the UHV base of China Electric Power Research Institute in Wuhan. This laboratory is equipped with an impulse current generator of 60 kV/10 kA. The test field is a hemispherical grounding pool whose diameter is 8  m. The excited current and voltage response waveform is measured by a Pearson CT and a resistive voltage divider. The impulse dispersal experimental arrangement is shown in Figure 1. The test procedure in detail is shown as follows Step 1: The single horizontal grounding electrode is located in the center of the grounding pool. The

electrode is embedded in the current measurement CT and the location of CT is shown in Figure 1. Step 2: Connect the impulse current generator and measurement system, and ensure the measurement system function properly before the electrode is buried. Step 3: Inject the impulse current with different current amplitude and record the experimental data. The impulse test is repeated three times under the same injected current. Step 4: Test the single horizontal electrode with different length and repeat the test following Step 1–Step 3. Step 5: Analyze the experimental data and conclude the impulse dispersal regulation of the grounding electrode. The dissipating current of each conductor segment is equal to the difference value between the adjacent points. 2

Figure 1. The schematic of impulse dispersal experiment.

TEST RESULTS AND ANALYSIS

The impulse current distribution of a single horizontal grounding electrode with length of 0.5  m and 1.0  m tests was carried out according to the test principle and method. Experimental data of the two simulation models are shown in Tables  1 and 2, respectively. It should be noted that the

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Table 1. Impulse current distribution of 0.5 m grounding electrode. Amplitude of impulse current/kA Axial current 1 2 3

0.056

0.163

0.407

0.039 0.023 0.014

0.096 0.065 0.048

0.196 0.141 0.098

Dissipating current (S1)/kA Dissipating current (S2)/kA Dissipating current (S3)/kA Dissipating current (S4)/kA

0.017 0.016 0.009 0.014

0.067 0.031 0.017 0.048

0.211 0.055 0.043 0.098

Current ratio (S1)/% Current ratio (S2)/% Current ratio (S3)/% Current ratio (S4)/%

30.36 28.57 16.07 25.00

41.10 19.02 10.43 29.45

51.84 13.51 10.57 24.08

Si: conductor segment i. Table 2. Impulse current distribution of 1.0 m grounding electrode. Amplitude of impulse current/kA Axial current 1 2 3

0.132

0.322

0.642

0.095 0.068 0.035

0.199 0.152 0.083

0.355 0.281 0.155

Dissipating current (S1)/kA Dissipating current (S2)/kA Dissipating current (S3)/kA Dissipating current (S4)/kA

0.037 0.027 0.033 0.035

0.123 0.047 0.069 0.083

0.287 0.074 0.126 0.155

Current ratio (S1)/% Current ratio (S2)/% Current ratio (S3)/% Current ratio (S4)/%

28.03 20.45 25.00 26.52

38.20 14.60 21.43 25.78

Figure 2. Impulse current dispersal of single horizontal grounding electrode.

44.70 11.53 19.63 24.14

Si: conductor segment i.

dissipating current in Tables 1 and 2 is the amplitude of the impulse current. The fitting curves of impulse current dispersal characteristics of the two grounding electrodes with length of 0.5 and 1.0 m are shown in Figure 2. According to the test data and fitted curves, impulse current dispersal characteristics can be concluded as follows: 1. The current distribution characteristics of 0.5 m and 1.0 m grounding electrodes along with the grounding electrodes present a U-shape distribution. The impulse current flows through the end parts of electrodes are much higher than its middle parts. 2. Dispersal current ratio at the current injection point increased more quickly than the end point

as the current amplitude increases, because spark discharge at the current injected point is more intensive than the middle parts, which also leads to more current dissipating into the earth at the injected point. 3. It is generally believed that as the length of electrodes increases, the dissipating current ratio of the first conductor segment is larger than the rest segments due to inductive effect. However, test results indicate that the dissipating current ratio of the first segment of 1 m grounding electrode is less than 0.5 m electrode. 3

THE SIMULATION STUDY OF THE IMPULSE CURRENT DISPERSAL CHARACTERISTICS

The impulse current dispersal characteristics of a single horizontal grounding electrode are calculated by CDEGS. The grounding device to be simulated is a single horizontal electrode whose length

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is 20 m and radius is 0.01 m. Conductor material is steel. Its embedded depth is 0.8  m. The electrode is divided into 10  segments that are shown in Figure 3. The amplitude of injected current is 20 kA and its front time tf/tail time tt is 2.6/50 μs which is shown in Figure 4. Calculation results of impulse current distribution of each segment of grounding electrode in different soil resistivity are shown in Table 3. The fitting curves of impulse current distribution of the grounding electrode in different soil resistivity are shown in Figure 5. The fitting curves indicate that the impulse current distribution of a single horizontal grounding electrode present a U-shape distribution along with the grounding electrodes. The impulse

Figure 3. Conductor segment number and impulse current injection point.

Figure 5. Current distribution of the single horizontal grounding electrode in different soil resistivity.

current flows through the end parts of electrodes are much higher than its middle parts, which are demonstrated by the experiments. However, as the roil resistivity is larger than 1000 Ω ⋅ m, the dissipating current flows through both end segments are approximately equal to each other. High-resistivity soil reduces the impulse current flowing through the injected point and increases the current flowing through the rest parts. 4

Figure 4.

Injected impulse waveform.

Table  3. Current distribution of the single horizontal grounding electrode. Amplitude of dissipating current/A Segment number

100 Ω⋅m

200 Ω⋅m

500 Ω⋅m

1000 Ω⋅m

2000 Ω⋅m

1 2 3 4 5 6 7 8 9 10

3829.5 2766.6 2310.2 2015.9 1872.7 1820.0 1811.8 1839.7 1923.8 2227.5

2962.4 2278.2 2007.1 1909.1 1863.2 1845.4 1850.4 1885.1 1976.0 2290.0

2418.8 2056.3 1933.5 1879.0 1856.2 1853.6 1870.4 1912.9 2008.9 2330.0

2372.1 2034.3 1927.8 1876.6 1853.5 1854.0 1873.2 1917.8 2015.8 2339.0

2349.6 2023.9 1922.2 1874.3 1852.6 1852.7 1872.1 1916.9 2015.1 2338.4

CONCLUSION

1. The impulse tests and simulation results indicate that impulse current dispersal characteristics of a single horizontal grounding electrode present a U-shaped distribution along with the grounding electrodes. The impulse current flows through the end parts of electrodes are much higher than its middle parts. Besides, as the grounding electrode is excited by the highamplitude impulse current, spark discharge at the current injected point is more intensive than the middle parts, which also leads more current dissipating into the earth at injected point. 2. End effect is the dominant factor affecting impulse current distribution of a short grounding electrode. However, the impulse current distribution becomes uniform as the length of grounding electrodes increases, which explains the resistance reducing principle of extended conductors. 3. Soil resistivity also affects impulse current dispersal characteristics of the grounding electrode. As the soil resistivity is larger than 1000 Ω ⋅ m, the dissipating current flows through the two end segments are approximately equal to each other. High-resistivity soil reduces the impulse current flowing through the injected point

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and increases the current flowing through the remaining parts. REFERENCES He, J.L. & Zeng, R. 2007. Grounding technologies for power system. Beijing, China: Science Press. Huang, W.W. et  al. 2005. Simulation experiment of the current distribution along axial direction of the grounding network. High Voltage Engineering, 3(6): 76–77. Oettle E.E. 1988. A new general estimation curve for predicting the impulse impedance of concentrated earth electrodes. IEEE Transactions on Power Delivery, 3(4): 2020–2029. Sima, W.X. et  al. 2008. Analysis on shielding effect of the grounding electrodes under impulse-current. International Conference on High Voltage Engineering and Application, Chongqing, China: 309–312.

Sima, W.X. et  al. 2011. Experimental study on grounding resistance reduction based on improved grounding electric field distribution induced by the diffuser of Impulse current. High Voltage Engineering, 32(9): 2294–2301. Xia, C.Z. & Chen, C.X. 2001. Impulse experiment for real extended grounding electrode in unit length. High Voltage Engineering, 27(3): 34–35. Xu, H. et al. 2006. Calculation of tower impulse grounding resistance. High Voltage Engineering, 32(3): 93–95. Yuan, T. et  al. 2008. Experiment study on impulse current distribution rules of different structure grounding devices. Academic Conference of Chongqing Society for Electrical Engineering. Chongqing, China: Chongqing Society for Electrical Engineering, 416–422. Zhang, B.P. et  al. 2008. Experimental study on impulse characteristics of frozen soil. Proceedings of the CSEE, 28(16): 143–147 (in Chinese).

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Innovative design of centrifugal oil press C.Q. Zhong & Y.L. Zhang Wuhan Polytechnic University, Wuhan, China

ABSTRACT: In order to reduce the high energy consumption of traditional screw oil press, improve the efficiency and press a variety of oils in the same oil machine, a new type of centrifugal oil press is designed combining the principle of centrifuge and screw oil press. The two-dimensional picture of a new type of centrifugal oil press has been drawn in AutoCAD. The influence of swivel plate’s rotation rate, swivel plate’s radius, and swivel plate’s conical bottom angle swivel plate’s friction factor on the delivery pressure is simulated in MATLAB. After that, the relevant experiments are done. Whose results show that the new oil press avoids the problem of friction heat dissipation in screw oil press, the new oil press is more productive than the screw oil press, and it suits a variety of oils in the same oil machine. Keywords: 1

centrifugal oil press; low energy; innovative design; MATLAB

INTRODUCTION

Nowadays, single screw cold press and double screw cold press are the traditional way of cold press. Yield efficiency of single screw press is low, and its energy consumption is high. Yield efficiency of a double screw press has been greatly improved, but its energy consumption is also high. Transmission and squeeze of oil plants in both the single screw press and double screw press are done by friction. As is well known, friction generates heat, this is bound to cause large energy waste. More than 60% energy in the working process of the press machine is wasted because of friction according to the related experimental data. New centrifugal oil press is designed based on high efficiency and low energy consumption. 2

plate and a large centrifugal force when it rotates at a high speed, oil plants are squeezed to the edge of swivel plate. After that, oil plants are into material flow which is solid–liquid coexistence. Then, material is pushed into the solid–liquid separation screw under the action of pressure. Finally, the oil is pushed out from oil filter under the mutual extrusion between solid–liquid separation screw

PROFILES OF CENTRIFUGAL OIL PRESS

The process of centrifugal oil can be divided into three sections: solid plants conveying section, disk fusion section, and ingress of oil section. Solid plants are conveyed by centrifugal force rather than friction, this is the biggest difference between centrifugal oil press and screw oil press. The structure diagram of a centrifugal oil press is shown in Figure 1. Oil plants are added into centrifugal oil press through feed inlet, then they flow to the swivel plate along the inner cavity of the oil press. Because of the large centrifugal force produced by the swivel

Figure 1. Structure diagram of centrifugal oil. 1 represents belt pulley. 2, oil plants. 3, bearing. 4, frame. 5, swivel plate. 6, material flow. 7, solid–liquid separation screw. 8, electromotor. 9, engine base. 10, oil filter. 11, feed inlet. 12, pie mouth.

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and oil filter. Material residue is pushed out from the pie mouth. Centrifugal oil press almost avoids the friction between oil plants and swivel plate. Energy consumption is greatly reduced and the production efficiency of oil is greatly improved in the new centrifugal oil press.

4

3

⎧ rτ ⎡ R 1−τ ⎞ ⎤ 2 τ⎛ 1 r0 2 −τ − r0 ⎟ ⎥ ⎪P = τ ⋅ ⎢ P0 ρω r0 ⎜ ⎝ ⎠⎦ 2 − τ 1 − τ r ⎣ 0 ⎪ ⎪ R 1−τ ⎞ ⎛ 1 2 τ ⎪ × ρω ω 2 rτ ⎜ r − r ⎟ ⎝ 2 −τ ⎠ ⎪ 1−τ ⎨ ⎪r R − r ⎪ ⎪r0 R − r0 ⎪ f f2 ⎪τ = 1 tan α ⎩

4.1

where P is the pressure of a distance of r, Pa; ω is angular velocity of cylinder rotation, r/s; R is swivel plate radius, m; r is the radius of the material in any position, m; P0 is initial pressure of entrance, Pa; r0 is feed inlet radius, m; f1 is the friction factor between oil plants and upper surface of swivel plate; f2 is the friction factor between oil plants and lower surface of swivel plate; α is angle on the conical bottom of swivel plate; ρ is the density of the material, kg/m 3 ; 4.2

Disk fusion section.

Influence of swivel plate’s radius on delivery pressure

Change the value of R, rotation rate n  =  6000r/ min, f1  =  f2  =  0.1, α  =  60 . The influence of swivel plate radius on delivery pressure is simulated in MATLAB. Simulation result is shown in Figure 3. According to Figure  3, delivery pressure is increased when radius of swivel plate is increased. 4.3

Figure 2.

Mathematical model of centrifugal oil press

The mathematical model of the centrifugal oil press is obtained based on the comparison of several mathematical models of centrifugal press, as follows:

PRESSURIZATION MECHANISM OF DISK FUSION SECTION IN CENTRIFUGAL OIL PRESS

The theoretical basis of pressurization mechanism is that the object’s inertia force will produce centrifugal force when it does curve movement. Oil plants are added into the centrifugal oil press through feed inlet, then they flow to the swivel plate along the inner cavity of the oil press. Because of the large centrifugal force produced by the swivel plate when it rotates at a high speed, oil plants are squeezed to the edge of swivel plate as shown in Figure 2. Acceleration of solid plants in centrifugal force field is a = ω 2r Where ω is revolving speed, rad /s; R is the rotation radius of the material. The centrifugal acceleration is proportional to the rotation radius and is proportional to the square of the revolving speed according to the formula just mentioned. A part of friction power energy made by screw cold press is transformed into the rise of material temperature, the other part of friction power energy disappears through the surface. But this part of the energy is not wasted in the centrifugal oil press. The new centrifugal oil press is more efficient and consumes lower energy than screw cold press based on the above analysis. So, energy will be saved when the new centrifugal oil press is used.

STRESS ANALYSIS OF NEW OIL CENTRIFUGAL PRESS

Influence of swivel plate’s rotation rate on delivery pressure

When rotation rate is changed from 1000 r/min to 10,000 r/min uniformly, f1  =  f2  =  0.1, α  =  60°, R = 0.15 m, influence of swivel plate’s rotation rate on delivery pressure is obtained in MATLAB, as shown in Figure 4. According to Figure  4, delivery pressure is increased when rotation rate of the swivel plate is increased.

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Table 1. Influence of conical bottom angle on delivery pressure. R (m) P (MPa)

0.10

0.12

0.14

0.16

0.18

0.20

α 30° 60° 90°

0.45 0.52 0.59

0.69 0.88 1.01

1.19 1.55 1.78

1.21 2.13 2.50

1.99 2.23 3.00

1.98 2.45 3.62

Table  2. Influence of friction factor on delivery pressure. Figure 3. pressure.

Influence of swivel plate’s radius on delivery

f1

0.1

0.15

0.3

P (MPa)

1.24

0.81

0.66

Influence of angle on the conical bottom and friction factor on delivery pressure is important. Right angle of the conical bottom and friction factor are also key factors to a good centrifugal oil press. 5

Figure  4. Influence of swivel plate’s rotation rate on delivery pressure.

4.4

Influence of conical bottom angle and friction factor on delivery pressure

When α is changed from 30° to 60° uniformly and swivel plate’s radius is changed from 0.1m to 0.2 m uniformly, f1  =  f2  =  0.1, R  =  0.15 m, influence of conical bottom angle on delivery pressure is obtained in MATLAB. The delivery pressure is shown in Table 1. According to Table  1, delivery pressure is increased when conical bottom angle of the swivel plate is increased. When friction factor is changed from 0.1 to 0.3, R  =  0.15 m, α  =  60°, influence of friction factor on delivery pressure is obtained in MATLAB. The delivery pressure is shown in Table 2. According to Table  1, delivery pressure is increased when friction factor of the swivel plate is decreased.

CONCLUSION

1. A new type of centrifugal oil press is designed combining the principle of centrifuge and screw oil press. The new oil press is more productive and has lower energy consumption than the screw oil press, and it suits a variety of oils in the same oil machine. 2. Delivery pressure will be increased when swivel plate’s radius is increased. But the overall structure of the centrifugal oil press will be larger when swivel plate’s radius is increased, which will result in a decline of manufacturing precision and an increase of manufacturing cost. So swivel plate’s radius needs to be chosen reasonably. 3. Delivery pressure and productivity can be effectively increased when swivel plate’s rotation rate is increased. But higher precision is needed in the centrifugal oil press when swivel plate’s rotation rate is increased. So swivel plate’s rotation rate needs to be chosen reasonably. 4. Delivery pressure will be increased when friction factor is reduced. But the decrease of friction factor will result in an increase of manufacturing cost. Delivery pressure will be increased when angle of the conical bottom is increased. But productivity will be decreased when angle of the conical bottom is increased. So angle of the conical bottom and friction factor need to be chosen reasonably.

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REFERENCES [1] Liu Congcong. Centrifugal extruder solid conveying and pressurization mechanism research [D]. Master thesis, Beijing university of chemical industry, 2007:24–38. [2] Zhao Jing, Wu Daming, Chen Weihong. Mechanism of centrifugal extruder solid conveying experimental study[J]. plastics, 2008, 37(3)98–100. [3] Wang Moran. MATALB and scientific computing [M]. Beijing: Electronic Industry Press, 2003, 9. [4] Li Wenlin, Huang Fenghong. As the development and application of double screw cold pressing machine [J]. Chinese Society of Agricultural Engineering, 2006, 6. [5] B.H. Sokolov. Centrifugal separation theory and equipment [M]. Beijing: China Machine Press, 1986. [6] Ye Kejiang. Wu Chaohui. The energy consumption of the virtualization of the cloud computing platform management [J]. Chinese Journal of Computers, 2012, 6.

[7] Wang Xiaojun, Chen Xiaoshu. A semi-central lowenergy adaptive Wireless Sensor Network (WSN) into a cluster algorithm [J]. Modern Electronics Technique, 2007, 24. [8] Pan Yunhe, Sun Shouqian, Bao Enwei. Computer aided industrial design technology development status and trends [J]. Journal of computer aided design and graphics, 1999, 03. [9] Tan Runhua. Product innovation design problems is reviewed [J]. Chinese Journal of Mechanical Engineering, 2003, 09. [10] Sun Shouqian, Bao Enwei, Pan Yunhe. On the basis of the principle of combination of the concept of innovative [J]. Journal of computer aided design and graphics, 1999, 03.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis of the centrifugal oil press vibration C.Q. Zhong & Y.L. Zhang Wuhan Polytechnic University, Wuhan, China

ABSTRACT: Mechanical vibration will greatly shorten life of the new centrifugal oil press because the press belonged to high-speed rotating machinery. In order to reduce vibration, research and optimization of centrifugal oil press about vibration have been done. Three-dimensional solid modeling of the new centrifugal oil press is conducted in Pro/e. After that, modal analysis and transient dynamics analysis have been done in ANSYS. Whose results show that rotation rate has the greatest influence on vibration of centrifugal oil press. Reasonable rotation rate is very important to reduce the vibration of the centrifugal oil press. Keywords: 1

centrifugal oil press; vibration analysis; ANSYS; optimal design

INTRODUCTION

A great centrifugal force is produced when a new centrifugal oil press rotates at a high speed. Then, a part of the centrifugal force is converted to pressing force which can make oil plants into oil. Efficiency and centrifugal force will be higher when rotation rate is increased. But stronger vibration will be produced when rotation rate is increased. Therefore, analysis of centrifugal oil press vibration is very important.

2

PROFILES OF CENTRIFUGAL OIL PRESS

The process of centrifugal oil can be divided into three sections: solid plants conveying section, disk fusion section, and ingress of oil section. Solid plants are conveyed by centrifugal force rather than friction, this is the biggest difference between centrifugal oil press and screw oil press. The structure diagram of centrifugal oil press is shown in Figure 1.

3

Figure 1. Structure diagram of centrifugal oil. 1 represents belt pulley. 2, oil plants. 3, bearing. 4, frame. 5, swivel plate. 6, material flow. 7, solid–liquid separation screw. 8, electromotor. 9, engine base. 10, oil filter. 11, feed inlet. 12, pie mouth.

MODELING OF NEW CENTRIFUGAL OIL PRESS

The size of swivel plate in new centrifugal oil press is shown in Figure 2. Three-dimensional model of swivel plate drawn in Pro/e is shown in Figure 3. A cutaway view of swivel plate is shown in Figure 4.

Figure 2.

Size of swivel plate.

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Figure 3.

Figure 4.

Figure 5.

4

One order vibration mode.

Figure 6.2.

Two order vibration mode.

Figure 6.3.

Three order vibration mode.

Figure 6.4.

Four order vibration mode.

Figure 6.5.

Five order vibration mode.

A cutaway view of swivel plate.

Meshing results.

MODAL ANALYSIS OF NEW CENTRIFUGAL OIL PRESS

Import the three-dimensional model of swivel plate shown in Figure 3 into ANSYS Workbench. Then, select the “Model ANSYS” module for analysis. After that, the model is meshed as shown in Figure 5. Last, modal analysis about swivel plate is done in ANSYS Workbench. Six modals are shown in Figure 6. 5

Figure 6.1. Three-dimensional model of swivel plate.

TRANSIENT DYNAMICS ANALYSIS OF NEW CENTRIFUGAL OIL PRESS

In order to further investigate the vibration of new centrifugal oil press when it rotates at high speed, transient dynamic analysis of new centrifugal oil press is done in ANSYS Workbench.

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Figure 6.6.

Figure 7.

Six order vibration mode.

Figure 9.1.

Directional deformation.

Figure 9.2.

Directional velocity.

Figure 9.3.

Directional acceleration.

transient structural (MBD). Results are shown in Figure 9. Compare Figure 8 and Figure 9. It is apparent that when the rotation rate is increased, deformation and vibration of swivel plate are significantly enhanced.

Apply rotation rate.

6 Figure 8.1.

Directional deformation.

Figure 8.2.

Directional velocity.

Figure 8.3.

Directional acceleration.

CONCLUSION

1. Natural frequency of swivel plate has been obtained based on the modal analysis made in ANSYS workbench. According to the modal analysis, rotation rate should be designed as far as possible away from its natural frequency. 2. Specific vibration of swivel plate when centrifugal oil press rotates at a high speed has been obtained from the transient dynamics analysis made in ANSYS workbench. Vibration is enhanced when rotation rate is increased. So swivel plate’s rotation rate needs to be chosen reasonably. REFERENCES

Three-dimensional model of swivel plate is imported into transient structural (MBD) which is a module of ANSYS workbench. Then, we apply a speed of 1000  rad/s on swivel plate after constraints and meshing have been completed, as shown in Figure 7. Directional deformation, directional velocity, and directional acceleration are selected as analysis result. Then click solve, the structure analysis is shown in Figure 8. Rotation rate of swivel plate is changed from 1000 rad/s to 6000 rad/s, same analysis is done in

[1] Liu Yang, Xiong Wang’e. A space optical pointing mirror system random vibration analysis based on ANSYS [J]. Machine Design and Manufacture, 2010, 4. [2] Bai Lidong, Qin Zhongbao. vibration breaks analysis of the beam slab structure based on ANSYS [J]. Henan Building Materials, 2011, 6. [3] Zhu Huangang, Zhang Bao. Drill string longitudinal vibration analysis based on ANSYS [J]. Oil Field Equipment, 2008, 10. [4] Pu Guangyi. ANSYS Workbench 12 Basic tutorials and example explanation [M]. Beijing: China WaterPower Publishing house, 2012.

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[5] Liu Congcong. Centrifugal extruder solid conveying and pressurization mechanism research [D]. Master thesis, Beijing university of chemical industry, 2007:24–38. [6] Zhao Jing, Wu Daming, Chen Weihong. Mechanism of centrifugal extruder solid conveying experimental study [J]. Plastics, 2008, 37 (3) 98–100. [7] Wang Moran. MATALB and scientific computing [M]. Beijing: Electronic Industry Press, 2003, 9. [8] Li Wenlin, Huang Fenghong. As the development and application of double screw cold pressing machine [J]. Chinese Society of Agricultural Engineering, 2006, 6.

[9] B.H. Sokolov. Centrifugal separation theory and equipment [M]. Beijing: China Machine Press, 1986. [10] Ye Kejiang. Wu Chaohui. The energy consumption of the virtualization of the cloud computing platform management [J]. Chinese Journal of Computers, 2012, 6.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

HPLC method for determination of concentration of Nifekalant in human plasma X. Xie Henan Polytechnic College, Zhengzhou, Henan, China

ABSTRACT: To establish a HPLC method for determining the concentrations of Nifekalant in human plasma and to evaluate its pharmacokinetic characteristics. An ODS3 (250  mm  ×  4.6  mm, 5 μm) column was used to separate Nifekalant in plasma with a mobile phase of a mixture of ammonium acetate (0.1 mol/L)–methanol–methyl cyanide (440: 180: 180, V/V) at a flow rate of 1 mL/min. Nifekalant was detected at 270 nm. The linear range of the standard curve of Nifekalant was 5 ∼ 1000 ng/mL, and the determination limit was 5 ng/mL. The extraction recoveries were more than 80%, intra-day and inter-day relative standard deviation were less than 6.23%. The Nifekalant plasma concentrations were determined after intravenous dosing and its pharmacokinetic parameters were calculated. The method is sensitive, fast, and accurate. It is suitable for therapeutic Nifekalant monitoring and its pharmacokinetic studies. Keywords: 1 1.1

Nifekalant; HPLC; pharmacokinetics

MATERIALS AND METHODS

1.3 Method of pretreatment

Instruments and reagents

The HPLC SIL-20  A system with automatic sampler (LC-10  AT) was used to separate and detect Nifekalant in human plasma, METTLER TOLEDO AX-205 electronic balance, XW-80 A eddy mixer, PROINO high speed centrifuge, PK514BP ultrasonic cleaner were supplied by Japan SHIMADZU Company, Mettler-Toledo Instrument (Shanghai) Co. Ltd, Shanghai Jingke Company, American Kendro Laboratory Products and Germany Bandel Company, respectively. Nifekalant hydrochloride injection and reference substance were offered by Sichuan Baili Pharmaceutical Factory. Chinese Drug and Biological Products Quality Control institute provided the Internal Standards (IS) of Ornidazole. Methanol, methyl cyanide and ethyl acetate were all chromatographic pure grade. 1.2

Conditions for chromatogram

The separation was carried out with a mobile phase of a mixture of ammonium acetate (0.1  mol/L)– methanol–methyl cyanide (440: 180: 180, V/V) at a flow rate of 1 mL/min and a stable phase of A INERTSIL ODS3 column (250  mm  ×  4.6  mm, 5  μm), 20 μL of purified sample was injected. Nifekalant was detected at 270 nm.

The stock solutions of Nifekalant and Ornidazole at the concentration of 1 mg/mL were all dissolved under methanol and kept at 4ºC, respectively. A liquor of 0.5 mL plasma of sample plus 40 μL of IS (Ornidazole 10 μg/mL) was acidified by adding 50  μL of 0.1  mol/mL hydrochloric acid and 4  mL of ethyl acetate. Then it was vortex-mixed 2  min, centrifuged at 5000 r/min for 5  min. The water phase was discarded and 4  mL of organic phase was moved to a clean glass tube and dried under nitrogen in a 40 ºC water bath. The residue was reconstituted with 0.1  mL of mobile phase, centrifuged for 3 min, and 20 μL of it was injected for analysis. 1.4 Corroboration of methods Under the above conditions, the retention times of IS and Nifekalant were 4.689 and 9.764  min, respectively. The blank plasma, blank plasma spiked with IS plus Nifekalant, volunteer samples spiked with IS are shown in Figure 1. Then 0.5  mL blank plasma was added in each glass tube which contained Nifekalant at concentrations of 5, 10, 20, 50, 100, 200, 500 and 1000 ng/mL. After dried with nitrogen, purified, injected, and analyzed, the regressive equation was as follows: y  = –0.01378 + 0.00473 x, r = 0.9999, the limit of quantity is 5  ng/mL. The linear relationship of Nifekalant from 5 ng/mL to 1000 ng/mL is good.

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Sixteen healthy volunteers were randomly divided into two groups, male and female. Under the condition of continuous electrocardiogram monitoring and the use of constant speed pump, hydrochloric acid Nifekalant for injection of 0.3 mg/kg or 0.4 mg/kg were infused into intravenous within 5 min, respectively. And 4 mL venous blood samples were obtained before and 5, 10, 15, 30, 45 min and 1, 2, 3, 4, 5, 6, 7h after the administration of hydrochloric acid Nifekalant injection preparations. The blood samples were centrifuged and plasma was collected and stored at –80 C for analysis. After the dosing, volunteers were accepted for electrocardiogram monitoring for 2 h. Eight healthy volunteers (male–female), under the condition of continuous electrocardiogram monitoring, the use of constant speed pump, hydrochloric acid Nifekalant for injection of 0.4 mg/kg were infused into intravenous within 5  min, then intravenous drip was administered at 0.4 mg/kg/h velocity for 6 h. Venous blood samples of 4  mL were obtained before and at the end of the infusion intravenous injection, and 5, 15, 30, 45, 60 min, 2, 4, 6 h after the start of intravenous dripping and 5, 10, 15, 30, 45 min, 1, 2, 3, 4, 5, 6 h after the end of administration of hydrochloric acid Nifekalant injection. The blood samples were centrifuged and plasma was collected and stored at –80°C for analysis. After the dosing, the volunteers were accepted for electrocardiogram monitoring for 2 h.

Figure 1. The chromatograms of Nifekalant plus IS(A). blank plasma(B). blank plasma spiked with Nifekalant and IS(C). volunteer plasma spiked with IS(D).

Table 1. Recovery rate, intra-day and inter-day RSD of Nifekalant (n = 5). Concentration (ng/mL)

Recovery rate (%)

Intra-day RSD (%)

Inter-day RSD (%)

15 200 750

89.465 94.086 80.375

2.071 2.873 1.326

6.228 5.116 4.384

2

RESULTS

2.1 Plasma concentrations of Nifekalant in each group The average plasma concentrations of Nifekalant after intravenous infusion injection of

Then 15, 200, and 750  ng/mL of Nifekalant were spiked in blank plasma and analyzed at the above conditions. The recovery rate, intra-day, and inter-day Relative Standard Deviation (RSD) were calculated (Table 1). 1.5 Subject and design Sixteen healthy volunteers participated in this study after physical examination and laboratory screening. They were asked to avoid all prescription for at least 10  days before the study. Those who had a history of drug or alcohol abuse or allergy to the components of Nifekalant and those who had concomitant drug therapy were excluded. All subjects gave their written informed consent at the beginning of the study and were explained the nature of the drug and purpose of this study.

Table  2. The Nifekalant time–plasma concentrations after intravenous infusion injection of 0.3  mg/kg and 0.4 mg/kg (x ± SD, n = 16). Concentration (ng/mL) Time (h)

0.3 (mg/kg)

0.4 (mg/kg)

0.08 0.17 0.25 0.5 0.75 1 2 3 4 5

230.95 ± 54.02 158.02 ± 39.85 112.05 ± 25.41 90.71 ± 26.08 69.87 ± 24.70 49.54 ± 17.10 26.04 ± 7.89 16.94 ± 6.98 11.24 ± 4.11 6.94 ± 2.60

358.62 ± 73.98 213.16 ± 38.35 175.79 ± 29.95 133.83 ± 26.18 106.81 ± 21.69 78.12 ± 18.20 38.28 ± 13.47 22.74 ± 4.86 14.19 ± 1.54 8.83 ± 1.41

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Table  3. The Nifekalant time–plasma concentrations after intravenous dripping of 0.4  mg/kg 6 h (x ± SD, n = 8). Time (h)

Concentration (ng/mL)

During the period of intravenous dripping

After the end of intravenous dripping

0.08 0.17 0.5 0.75 1 2 4 6 6.08 6.17 6.25 6.5 6.75 7 8 9 10 11 12

470.48 ± 104.25 353.64 ± 125.08 334.97 ± 135.90 323.24 ± 115.51 309.42 ± 115.55 369.80 ± 95.80 423.80 ± 86.40 344.69 ± 96.17 254.28 ± 64.44 237.60 ± 58.31 221.29 ± 68.16 180.89 ± 53.71 126.64 ± 27.73 98.45 ± 31.73 59.15 ± 18.55 42.23 ± 17.75 24.47 ± 4.67 15.26 ± 3.78 9.60 ± 2.91

0.3  mg/kg or 0.4  mg/kg within 5  min are shown in Table 2. The average plasma concentrations of Nifekalant after intravenous infusion injection of 0.4  mg/kg within 5  min, then intravenous dripping of 0.4 mg/kg/h for 6 h are shown in Table 3. The time–concentrations curves are shown in Figures 2 and 3. 2.2

Pharmacokinetic parameters of Nifekalant in each group

The mean pharmacokinetic parameters of Nifekalant in each group are shown in Table 4. 3

DISCUSSION

Nifekalant is a nonselective potassium channel blocker, which can effectively control the rapid ventricular arrhythmia caused by turn-back and has good curative effect on ischemic cardiac arrhythmia. Compared with other anti-arrhythmic drugs, Nifekalant is safer and more effective and is the first selection medicament for prevention and treatment of ventricular arrhythmia for patients with organic heart disease.

Figure  2. The Nifekalant time–plasma concentration curves after intravenous infusion of 0.3 mg/kg and 0.4 mg/kg (x ± SD, n = 16).

Figure  3. The Nifekalant time–plasma concentration curve after 6 h intravenous dripping of 0.4  mg/kg (x ± SD, n = 8).

Table 4. The Nifekalant pharmacokinetic parameters after 5 min intravenous infusion of 0.3 mg/kg and 0.4 mg/kg and 6 h intravenous dripping of 0.4 mg/kg (x ± SD, n = 16). A single dose of intravenous infusion

Intravenous dripping

Parameters (unit)

0.3 mg/kg

0.4 mg/kg

0.4 mg/kg

t1/2 (h) Tmax (h) Cmax (ng/mL) AUC0-5 (ng/mL/h) AUC0-∞ (ng/mL/h) MRT (h) CL (L/Kg/h) V (L/Kg)

1.54 ± 0.38 0.08 ± 0 230.95 ± 54.02 193.53 ± 45.19 209.90 ± 48.12 1.18 ± 0.15 1.50 ± 0.35 3.40 ± 1.44

1.34 ± 0.19 0.08 ± 0 358.62 ± 73.98 285.61 ± 46.57 302.44 ± 50.19 1.13 ± 0.08 1.35 ± 0.22 2.60 ± 0.35

1.35 ± 0.23 3.75 ± 1.28 444.30 ± 88.12 2609.02 ± 498.20 2627.33 ± 499.89 – 0.32 ± 0.06 0.61 ± 0.15

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A HPLC method for determining the blood concentration of Nifekalant with Ornidazole as internal standard was developed and reported. It has sensitivity, specialty, and precision, and suitable for Nifekalant therapy drug monitoring and pharmacokinetic studies. The whole experiment process was smooth, no adverse reaction occurred. REFERENCES

Kamiya, J. Ishii, M. Yoshihara, K. et al. MS-551: Pharmacokinetical profile of a novel class III antiarrhythmic agent. Drug Development Research. 30 1993.37–44. Donald, K. et al. Inhibition of ATP-sensitive potassium channels in cardiac myocytes by the novel class III antiarrhythmic agent MS−551. Pharmacology & Toxicology. 77 1995. 65–70. Kamiya, J. Ishii, M. Katakami, T. et al. Antiarrhythmic effects of MS-551, a new class III antiarrhythmic agent, on canine models of ventricular arrhythmia. Jpn J Pharmacol. 58(2) 1992. 107–115.

Kondoh, K. Haohimoto, H. Nishiyama, H. et al. 1994. Effects of MS-551, a new class III antiarrhythmic drug, on programmed stimulation-induced ventricular arrhythmias, electrophysiology, and hemodynamics in a canine myocardial infarction model. J Cardiovasc Pharmacol. 23(4) 1994. 675–679.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research of over-voltage in air-core reactor using Waveform Relaxation based on Lanczos L.M. Bo, B. Bao & Y. Xu North China Electric Power University, Baoding, China

ABSTRACT: This paper provides a method to using Waveform Relaxation (WR) to solve the circuit equation based on the model. This model and algorithm could be used in analysis of internal over-voltage of air-core reactor winding under lighting. However, as the coupling between the turns of winding becomes stronger, the WR algorithm either fails to converge or the number of iterations required for convergence increases. In this paper, the Lanczos algorithm is used through analysis of the coupling between the turns of winding, as the result, the number of iterations reduced significantly. At last an example is presented which demonstrates the accuracy and efficiency of the proposed model and method. Keywords: 1

Waveform Relaxation; Lanczos algorithm; winding equipment; reactor

INTRODUCTION

Air-core reactors are commonly used in current limiting applications, harmonic filters, reactive power compensators, and so on. Due to its installation and maintenance simple, linear, and low operating costs, air-core reactors have been developed rapidly and used widely in electric power systems [1]. So the research on the reactor is gradually coming into view. While during 2010–2011, many faults, such as insulation aging and fire occurred on the 35 kV shunt reactors in the Yunnan Power Grid. Figure  1 shows the burned severity of a 35 kV reactor. So the safe operation of the reactor becomes an important issue and it is helpful for studying the faults of reactors to analyze the

Figure 1.

Burned severity of a 35 kV reactor.

voltage distribution in the reactor under lightning. Taking the number of turns in reactor, the calculation is difficult because of high order matrix. So, we use the Waveform Relaxation (WR) method to solve the equation. WR is a decoupling technique of large systems, which was first applied in circuit simulation [2]. The WR method consists of a “divide-and-conquer” approach, i.e., in differential equation levels WR straightaway decouples a large system into small transient subsystems. This approach allows each decoupled subsystem to be independently solved with its own time-step length and therefore latency and multi-rate behaviors of stiff systems can be effectively exploited [3]. The WR has been first used in the time domain circuit simulation by the timing simulator MOTIS [4]. This approach has later been modified and implemented in other mixed-mode sim7J.latws such as SPLICE [5], DIANA [6] and SAMSON [7]. The particular association of relaxation with the conventional numerical methods used by these simulators has given rise to a new area of time domain simulation called the timing simulation. However, the convergence rate of WR algorithms depends on the nature of coupling between the partitioned lines. Therefore, as the coupling between the lines increases, the number of iterations required for WR convergence also increases. To address this problem, a new WR with Lanczos algorithm for tightly coupled interconnects is presented in this paper. This paper is organized as follows. Section  2 describes the WR method and Section 3 describes the Lanczos algorithm and takes the Lanczos

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algorithm into the WR method during its iteration. In Section 4, a detailed case is introduced to prove the WR method and when combined with the Lanczos algorithm it is more better than the traditional WR. 2

WAVEFORM RELAXATION

The WR method is an iterative method for analyzing nonlinear dynamical systems in the time domain. The method, at each iteration, decomposes the system into several dynamical subsystems each of which is analyzed for the entire given time interval [8]. We consider dynamical systems which can be described by a system of mixed implicit algebraicdifferential equations of the form ⎧ F ( y , y, u ) = 0 ⎨ ⎩E ( y(0 ) y0 ) = 0

(1)

where y(t ) ∈R p is the vector of unknown variables at time t, y (t ) ∈R p is the time derivative of y at time t, u(t ) ∈Rr is the vector of input variables at time t, y0 ∈R p is the given initial value of y, F : R p R p Rr → R p is a continuous function, and E ∈ R n × p , n ≤ p is a matrix of rank n such that Ey(t ) is the state of the system at time t. The relaxation process is an iterative process. For simplicity two most commonly used types of relaxation namely the Gauss-Seidel WR and Gauss-Jacobi WR. The relaxation process starts with an initial guess of the waveform solution of the original dynamical Equations (1) in order to initialize the approximate waveforms of the decoupling vectors. For the GS relaxation, the waveform solution obtained by solving one decomposed subsystem is immediately used to update the approximate waveforms of the decoupling vectors of the other subsystems. In GJ WR, all waveforms of the decoupling vectors are updated at the beginning of the next iteration. The relaxation is carried out repeatedly until satisfactory convergence is achieved. This process could be expressed by the following Equations [9–11]. F (u, v, t ) = f v1, v2 , ..., vi

1

ui , vi

1

Since Lanczos’s seminal paper [12] in 1950, despite some early setbacks about the applicability of the method in computers in finite precision arithmetic, the method found its way into many aspects of science and engineering. This method could transform symmetric matrix into triple diagonal matrix by the matrix consisting of orthogonal vectors. So, the storage occupied by the Lanczos method is tiny and the computing speed is fast. It is very useful in solving the large sparse symmetric matrix equation. The following will introduce this method through solving an equation. Assume the equation we wish to solve is Ax = b

(3)

where A is symmetric, positive definite and of order n, x is variables, and b is constant coefficient. QnT AQ Qn Tn , where Qn is an orthogonal matrix. Therefore, we can evaluate the inverse of A as A

1

−1

QnTn QnT

(4)

and find the exact solution x

A−1b QnTn 1QnT b

(5)

Based on the Lanczos method, the circuit equation of the reactor could be expressed as follows. Figure  2 shows the structure of a reactor and Figure  2 shows the circuit model of one turn of the reactor. From Figure  3 the controlled voltage source um represents the coupling between the inductances of turns, the controlled current source

vn , t )

(Gauss-Jacobi WR) R F (u, v, t ) = f u1, u2 , ..., ui

1

ui , vi

1

vn , t )

(Gauss-Seidel WR) 3

(2)

LANCZOS WAVEFORM RELAXATION

The method of Lanczos is probably one of the most influential methods of computational mathematics in the second half of the 20th century.

Figure 2.

Structure of reactor.

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⎧ ⎡ P11 0 ⎤ ⎡ 0 P12 ⎤ ⎪P=⎢ ⎥ ⎥−⎢ T ⎣ 0 P22 ⎦ ⎣ P12 0 ⎦ ⎪ ⎨ ⎡Q11 0 ⎤ ⎡ 0 Q12 ⎤ ⎪ ⎥ ⎪Q = ⎢ 0 Q ⎥ − ⎢QT 0 ⎦ 22 ⎦ ⎣ 12 ⎣ ⎩ Putting them into (8), we can get

Figure 3.

im represents the coupling between the capacitances of turns. Through the circuit model, the KCL and KVL equations of the reactor are ⎧ duu n ⎪⎪ C dt + Ai b = is ⎨ ⎪− L dii b + AT u = 0 n ⎪⎩ dt

(6)

(9)

If we take the first subsystem into account, then we can put X1n +11 1 VmY1n +11,r 1 into Equation (9) VmT

n +11 r 1 11VmY1

VmT ( P12 X 2n +1 r

Q11X1n Q1122 X 2n )

If Tm VmT P11Vm , then

where C is node capacitance matrix, L is branch inductance matrix, A is incidence matrix, un, ib are node voltage and branch current, and is is node injection current. Define ⎧ ⎡C ⎪M = ⎢ ⎣0 ⎪ ⎨ ⎪N = ⎡ 0 ⎢ T ⎪ ⎣A ⎩

n +1 n +1 ⎡ P11 0 ⎤ ⎡ X1 ⎤ ⎡ 0 P12 ⎤ ⎡ X1 ⎤ ⎥ ⎢ n +1 ⎥ ⎢ 0 P ⎥ ⎢ n +1 ⎥ − ⎢ T 22 ⎦ ⎢ ⎣ ⎣ X 2 ⎥⎦ ⎣ P12 0 ⎦ ⎢⎣ X 2 ⎥⎦ n n ⎡Q11 0 ⎤ ⎡ X1 ⎤ ⎡ 0 Q12 ⎤ ⎡ X1 ⎤ =⎢ ⎢ n⎥ − ⎢ T ⎥⎢ n⎥ ⎥ ⎣ 0 Q22 ⎦ ⎢⎣ X 2 ⎥⎦ ⎣Q12 0 ⎦ ⎢⎣ X 2 ⎥⎦

Circuit model.

TmY1n +11,rr

= VmT ( P12 X 2n +1 r +

11

n 1

−

12

n 2 ),

where Tm is tri-diagonal matrix. Another subsystem is just like the first subsystem. 4

0 ⎤ L ⎥⎦

1

DISCUSSION AND ANALYSIS OF CASE

Figure 4 shows a 35-turn winding and its equivalent circuit model is shown in Figure  9. Taking

A⎤ ⎥ 0⎦

then Equation (6) will become M

dX X + NX = 0 . dt

(7)

Equation (7) could be solved by the BDF method ⎛ M N ⎞ n +1 ⎛ M N ⎞ n + ⎟X = − ⎟X ⎜⎝ ⎝ Δt Δt 2 ⎠ Δt 2 ⎠

(8)

If ⎧ ⎛M N⎞ ⎪⎪P = ⎝ Δt + 2 ⎠ ⎨ ⎪Q = ⎛ M − N ⎞ ⎝ Δt 2 ⎠ ⎪⎩ According to the WR method, the system is partitioned into two subsystems, then P and Q would become

Figure 4.

35-turn winding model.

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into account the clarity of the circuit, only mutual inductances and capacitances between adjacent turns are marked in Figure  5, but it should be noted that the mutual inductances and capacitances between each turn and node are existed. Inject the standard 1.2/50 μs lightning which is shown in Figure  6 into the head of the winding. Then the voltage waveforms of first to fifth turns are shown in Figure 6. Figure 6 shows the comparison of three methods and Figure 7 shows the iterations of WR and Lanczos WR methods. From the figures, the iteration of the Lanczos WR method is less than the traditional WR method, as the precision is high. That is to say, the

Figure 7.

Overvoltage waveform.

Figure 8.

Iterations of two methods.

convergence property of the Lanczos WR method is better than WR without Lanczos. 5

Figure 5.

Equivalent circuit.

CONCLUSION

This paper presents a new WR with Lanczos algorithm for tightly coupled interconnects. Based on the Lanczos WR method, we deduce the equation of the reactor circuit model and prove the advantage of the Lanczos WR method through a case. Based on the result of this paper, some further studies will be implemented, such as over-voltage calculation and insulation diagnosis method of the reactor based on the Lanczos WR method. REFERENCES

Figure 6.

Lightning.

[1] Qing Li, Bernard, J.A, Design and evaluation of an observer for nuclear reactor fault detection, Nuclear Science, IEEE Transactions on, vol.49, no.3, Jun 2002: 1304–1308. [2] Lelarasmee, E., Ruehli, Albert E., SangiovanniVincentelli, AL., The Waveform Relaxation Method for Time-Domain Analysis of Large Scale Integrated Circuits, Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, vol.1, no.3, July 1982:131–145.

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[3] Yao-Lin Jiang, A general approach to waveform relaxation solutions of nonlinear differential-algebraic equations: the continuous-time and discrete-time cases, Circuits and Systems I: Regular Papers, IEEE Transactions on, vol.51, no.9, Sept. 2004: 1770–1780. [4] B.R Chawla, H.K. Gummel and P. Kozak, MOTIS—An MOS Timing Simulator, IEEE Transactions on Circuits and Systems, Vol. CAS-22, December1975: 901–910. [5] A.R Newton, The Simulation of Large Scale Integrated Circuits, IEEE Trans. on Circuits and Systems, Vol. CAS-26, September 1979: 741–749. [6] G. knout and H. De Man. The use of threshold functions and Boolean-controlled network elements for macromodeling of LSI circuits, IEEE Solid-State Circuits Society, Vol. SC-13, June 1978: 326–332. [7] K. Sakallah and S.W. Director, An Activity-Directed Circuit Simulation Algorithm, IEEE Proceedings Int. Conference on Circuits and Computer, New York, October 1980: 1032–1035.

[8] Lelarasmee, E., Ruehli, Albert E., SangiovanniVincentelli, AL., The Waveform Relaxation Method for Time-Domain Analysis of Large Scale Integrated Circuits, Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, vol.1, no.3, July 1982: 131–145. [9] U, Miekkala, O. Nevanlinna. Convergence of dynamic iteration methods for initial value problem [J] SIAM Journal on Scientific and Statistical Computing, 1987, (8). [10] Olavi Nevanlinna. Remarks on Picard-Lindelöf iteration [J]. BIT, 1989, (2). [11] Miekkala U. Dynamic iteration methods applied to linear DAE systems [J] Journal of Computational and Applied Mathematics, 1989, (2). [12] Lanczos C. An interation method for the solution of the eigenvalue problem of linear differential and integral operators. Journal of the National Bureau of Standards, 1950, 45: 255–282.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The design of automatic sorting and staking system based on PLC S7-200 L.Y. Fang & X.Y. Zhou Guangdong AIB Polytechnic College, Guangzhou, China

F.G. Wu & X. Zhang Guangdong University of Technology, Guangzhou, China

ABSTRACT: In order to reduce the energy consumption as well as improve production efficiency in automated production lines, this paper presents an automatic sorting and staking system, with feeding, transportation, sorting and staking of Three Degrees Manipulator (3DM), based on the technology of PLC, servo, variable-frequency drive. The experimental implementation has proved that the conveyor belt driven by PLC could not only improve the working effeciency and reduce the energy consumption, but also ensures safe and reliable transfer of the products. In sorting–stacking process, the servo motor used to control 3DM can achieve much more accurate position control. Meanwhile, this kind of system provides the scalability and flexibility to support different stacking requirements of products, it can be applied to the logistics center and sorting system of modern production line. Keywords: 1

PLC; automatic sorting and staking system; three degrees manipulator

INTRODUCTION

According to certain principles to achieve various products to move orderly is the traditional sorting. Sorting operation of traditional sorting is labor-intensive, time-consuming and the highest costs process, it accounts for about 40%–60% of distribution center’s workload[1]. With the rapid development of modern science and technology, automated and technical processing mode gradually replaces manual processing mode, and becomes the main way of modern sorting. It has become one of the necessary facilities and a key factor to improve logistics efficiency in an advanced distribution center of developed countries[2]. For an enterprise, whether it is large or small, how to face fierce market competition and to improve the efficiency of production that is the important task, so it strongly demands highly automated production line. PLC control technology, as a kind of highly automated process control technology, gets more and more widely used in the production line. However, the PLC industry in our country still belongs to the developing stage, many aspects are not perfect enough, cannot be very good providing technical support for the industrial development of our country. At present, most of the domestic automatic identification sorting system is controlled by relay, but it is poor in precision control and reliability, so it brings on high failure and

inconvenience for maintenance[3]. The present paper aims at these questions to discuss a new products’ sorting—stacking system based on PLC S7-200, it contains the process of feeding, transportation, sorting and stacking. Through many experiments and debugging, it is confirmed that the system is economic, reliable and highly flexibe for different sorting requirements of the production line. Simply modified detection sensor can achieve sorting different products, the system can be applied to logistics center and sorting system of modern production line. 2

SCHEMATIC DESIGN[4–10]

Through the comprehensive analysis of various automatic sorting system, in this paper, the automatic sorting and staking system is divided into four parts, it contains the process of feeding, transportation, sorting, and stacking of 3DM. Figure 1 illustrates the design scheme. The description of working process is as follows: after feeding system sends out the products, the conveyor belt starts to run to send them to the end of belt. During the transportation, Sensors on the conveyor belt distinguish the products of black metal and white plastic; if they are white plastic products, the pushing rod will push them away.

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there is product in the hoppers, the feeding cylinder is controlled by throttle valve. Figure 1 illustrates the physical map of the fending system. 2.2

Transportation system[14,15]

The main functions for the transportation system is to send the products to the end of the belt. During the transportation, sensors on the conveyor belt distinguish the products of black metal and white plastic; if they are white plastic products,

Figure  1. The design scheme of automatic sorting– staking system.

Figure 4.

Figure 2.

Feeding system.

Figure 3.

Functional block diagram of conveyor belt.

The physical map of 3DM.

Figure 5. Physical map of diagram stacking shelves.

Meanwhile, the black metal products will be transported to the end of belt and 3DM comes and grabs the products to stacking shelves, the next cycle will begin after the completion of this working process. Each functional block is introduced as follows. 2.1

Fending system[11–13]

The main functions of the feeding system is to automatically push the products in hoppers to the conveyor belt, Then the belt transports them to other units. Magnetic induction sensor is used to determine whether the feeding rod to the back stops unit, fiber optic sensor is used to detect whether

Figure 6.

Structure of 3D stacking shelves.

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the pushing rod will push them away. According to the different sorting requirements of production line, the I1.1, I1.3, I1.4 shown in Figure 1 can be replaced by other sensors. The conveyor belt is driven by a three-phase asynchronous motor (3PAM) whose speed is adjusted by the Variable-Frequency Drive (VFD). The PLC connects the input terminal of VFD’S multi-velocity to realize a variety of speed control for a conveyor belt. Figure 3 shows the functional block diagram. 2.3

Sorting and staking system of the 3DM[16–18]

The main functions for the 3DM are to grab the products and move to stacking shelves. It has the three axis, X, Y, Z axes. The position of X axis is controlled by the servo controller whose pulses are generated by the EM253[19]. Pulse quantity determines the movement distance of X axis. The position of Y axis is controlled by the servo controller whose pulses are generated by the Q0.0 of PLC S7-200. The position of Z axis is controlled by the stepper motor driver whose pulses are generated by the Q0.1 of PLC S7-200. Figure 4 shows the physical map. 2.4 Staking system[20] The main functions of the stacking shelves are used for stacking black metal products, there are 3 * 3 stacking position, it is a combined type and the height of each shelf is different. It can be adjusted in three directions, if we adjust the pallet, the products can be put in different ways.

Table 1. Input

3 3.1

PLC PROGRAM DESIGN Selection of PLC and I/O allocation[5]

PLC is the core component of control system, it plays an important role for technical and economic indicators of the whole control system to select the correct PLC. In the selection, we should consider on cost-effective performance and multimission adaptability. Siemens S7-200[4,9] has strong function, advanced program structure, easy-to-use programming software, rich man–machine interface, etc. It is suitable for all walks of life, all kinds of detection, monitoring and control automation. It has a very high price, because it can replace the simple or more complex relay control, including machine tools, mechanical power facili ties, civilian facilities, and environmental protection equipment. This paper selects S7-200, CPU226CN, there are 24 digital inputs and 16 digital outputs, it can meet I/O point requirements[8] of the whole system. Table 1 shows the allocation of I/O points. 3.2 Program design of PLC 3.2.1 The program design of the fending system[11] In writing feeding programs, you should pay attention to three issues: (1) time delay problem of feeding (2) the locking problems of feeding cylinder. (3) the adjustment of the optical fiber sensor. If the feeding cylinder pushes over and over again in a short time. You will have the three ways to deal with it. (1) adjust the delay time of feeding (2) observe optical fiber sensor (3) whether the fending valve is

The allocation of I/O points of automatic sorting and staking system. F description

I0.0 Origin of Y axis I0.1 front stop unit of Y axis I0.2 back stop unit of Y axis I0.3 Origin of Z axis I0.4 up stop unit of Z axis I0.5 Down stop unit of Z axis I0.6 Examine product I0.7 back stop unit of fending rod I1.0 back stop unit of pushing rod I1.1 The end of belt I1.2 identify posture I1.3 Identify material I1.4 Identify color I1.5 fault signal of VFD I2.0 Sudden button I2.1 start button I2.2 stop button

Output

Functional description

Q0.0 pulse input of Y axis Q0.1 pulse input of Z axis Q0.2 direction of Y axis Q0.3 direction of Z axis Q0.4 clamping and loosening of Finger cylinder Q0.5 scaling of fending cylinder Q0.6 scaling of pushing cylinder Q1.0 the red indicator light Q1.1 the green indicator light Q1.2 alarm indicator lamp Q1.3 forward of VFD Q1.4 backward of VFD Q1.5 High speed of VFD Q1.6 Low speed of VFD

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pushing too fast. Figure 7 illustrates programs of the feeding system. 3.2.2 The program design of 3DM[16,17] The 3DM can move in three different axes, it is the X, Y and Z axes, respectively. By controlling three axes of 3DM, the products can be stacked in any position of 3D stacking shelves. In this paper we take the Y axis programming for example. The position of Y axis is controlled by the servo controller whose pulses are generated by the Q0.0

Figure 9.

Figure 7.

The program design of feeding system.

The flow charts of the whole PLC program.

of PLC S7-200 CPU226CN. Siemens CPU226CN configures two built-in pulse generators, which are PTO-pulse train output and PWM pulse-width modulation output. The maximum pulse frequency for both is 100 kHz, they output by Q0.0 and Q0.1, respectively. In this paper, we use PTO configuration wizard in S7-200 step 7 programming software to generate some subprogram, by calling the subprogram directly to write the Y axis motion program. Setting the direction and position in the envelope can control the movement of the Y axis. Figure 6 shows the products to be stacked to nine different positions, so we need to set three envelopes for Y axis, the products of 147, 258, and 369 should define three different envelopes, respectively. Figure  8  shows the program. 3.3

The flow charts design of the whole PLC program

In order to present the logic control and sequence of steps clearly, we write a complete program flow chart of delivery and staking process of a product. Figure  9  shows the program flow chart; the method for other products is identical, just change the number of envelopes of the XYZ axes to realize the different position. 4

Figure 8. The program design of Y axis.

CONCLUSION

The research of this paper is tested on SUKEY-II, which are assessment devices of the PLC designer. The result shows that it can achieve a fully

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automated sorting–stacking process. It also proves that the conveyor belt is driven by Siemens S7-200 PLC, with VFD in the transportation module, can save electricity and energy, is comfortable, has long life, safe and reliable, mute, etc. Installing photoelectric encoder in the output of 3PAM may achieve precise control to remove inferiors. In the sorting—stacking process, the servo motor used to control 3DM can achieve much more accurate position control. The result further shows that the system has a high flexibility. In the actual production line, other sensors can replace the color and metal sensor to implement the different sorting tasks. The models of 3DM can be upgraded to robot models to realize spatial localization of any position. REFERENCES [1] Yan. Y.Q. 2012. Automatic sorting system based on PLC. Journal of Mechanical& electrical engineering. 9.1286–1289. [2] Jia. Z.X, Liu. K. 2004. The planning and design of Logistics distribution center. Beijing: China Machine Press. 56–150. [3] Zhai. S.C. 2013. Shallow theory on the development trend of PLC of automation control devices. Journal of shandong industrial technology. 6.90–103. [4] Automation and drives division of Siemens. 2008. Siemens PLC S7-200 operating instruction manual. 8: 5–80. [5] Liao. C.C. 2007. programming and applications of PLC S7-200. China Machine Press. beijing. 23–256. [6] Wu. Z.M. 2009. Integrated Application Course of Siemens PLC, VFD and HMI. Beijing: China electric power press. 22–324. [7] Dong. L.F. 2009. Pneumatic components and electrical drawing and reading of system. Beijing: Chemical Industry Press. 15–168.

[8] Haval. K. 2000. Schemes to predigest the number of PLC input or output. Mechanical and Electrical Engineering. 26(4): 19–21. [9] Simosi. A. 2002. The method of improving the efficiency of PLC. Mechanism Manufacture. and Automatization, 46(2): 27–29. [10] MLmlzkua. S. 2009. Application of PLC In Logic control System Control Engineering. 40(5): 65–67. [11] Gao. L.Y. 2011. The research of Automatic Production line of Pneumatie technology based on PLC. Master’s thesis of southwest petroleum university. 1–60. [12] Yu. H. 2011. Application of PLC in automatic sorting system of materials. Journal of Coal Technology. 30(12): 31–32. [13] Wang. H. 2011. A Design of PLC-based Hydrauli System for Workpiece Turnover. Procedia Engineering, 15: 122–126. [14] Liu. Z.H & Pan. M.F. control of conveyor belt based on the technology of PLC and VFD. Journal of Coal Mine Machinery. 9: 192–194. [15] Automation and drives division of Siemens. 2007. general VFD application manual of Siemens. 6.6–375. [16] Li. L. 2009. Fetching manipulator on the target object. Master’s thesis of Xihua university. 1–72. [17] Zhou. Z.R & Wang. L. 2009. A system design to improve production line base on the Origin regression scheme of injection molding manipulator. Journal of engineering and technology. 10:5–7. [18] Luo. J.H. 2013. Siemens S7-200 in application of the manipulator reconstruction. Journal of Coal Mine Machinery. 34(3): 61–65. [19] Automation and drives division of Siemens. 2005. Getting started of position module EM253. 1–47. [20] Lu. Z.Z. 2008. The control system of automatic bagging andpa11etizing Line based on PLC. Master’s thesis of Jiangnan University. 1–53.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study of dynamic optimized allocation policy for AGC regulation power based on fuzzy-Q learning algorithm H. Qian Shanghai University of Electric Power, Shanghai, China Shanghai University, Shanghai, China

L. Zhou, W.X. Jin & J.B. Luo Shanghai University of Electric Power, Shanghai, China

ABSTRACT: The unit characteristics cannot be fully described in the multi-objective optimized allocation of Automatic Generation Control (AGC) regulation power; therefore, this paper proposes the optimal allocation approach based on the reinforcement Q-learning. With the AGC system, the “uncertain stochastic system”, combined with the ACE adjusting dead zone and CPS evaluation standard, the Markov Decision Process (MDP) model is established for optimized allocation of AGC regulation power; the Q-learning approach is introduced to the study of MDP’s optimal value function. The simulation results show that the proposed allocation strategy can adapt to the changing requirement of grid environment. Keywords: 1

AGC; Q-learning algorithm; dynamic optimized allocation of regulation power

INTRODUCTION

Automatic Generation Control (AGC) is an important technology to balance the power system generation and load and to ensure the frequency quality of the power system, security, stability, and economics of the power grid operation. In recent years, the CPS standards is used for the assessment of AGC control performance in many provinces’ interconnected power grid in China. This implementation means that the needs for stability and rapidity are put forward to AGC on the premise of stability of the power grid. Growing expansion of power grid scale and more renewable intermittent wind power and large-scale photovoltaic power connected to the electricity grid increase the uncertainty of power grid and the random volatility of load. Therefore, the AGC system can be seen as an “uncertain stochastic system”. At present, a lot of research has studied periodicity AGC as a discretetime Markov Decision Process (MDP). Due to the control targets and the random sequential decision characteristics presented in the process of AGC, MDP theory is put forward to study the dynamic optimization allocation problem for AGC regulation capacity. Reinforcement issue is an important branch of machine learning, and is also an important method of MDP. At present, the reinforcement learning has a certain application in AGC,

voltage reactive power control, power market and power information network, and other fields [1]. In [2], concerning complex nonlinear constraints such as generator unit rate of AGC system model in the process of reinforcement learning algorithm, fully embodies the Q-learning algorithm does not depend on the characteristic of system model. Q-learning algorithm is applied to optimal allocation of AGC regulation capacity, and regulating margin joins into the reward function in [3]. Furthermore, the requirements for multiobjective control of allocation of AGC regulation capacity is considered in [1] and [4], and regulating cost is introduced into reward function, but the size of the weight cannot reflect environmental changing. In this paper, the MDP model is established for optimized allocation of AGC regulation power, and multi-objective reward function based on rapidity and economics of regulation performance is represented. The function is divided into dead zone and regulating zone in principle, and combines CPS standard to gain weight value of multiobjective reward function by using fuzzy function. Then optimized allocation policy is found by the Q-learning algorithm. Finally, dynamic optimized allocation policy for AGC regulation capacity based on reinforcement Q-learning and fuzzy multiple objectives contributes to robustness and economics of power systems.

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2

When am = (am1, am2, …, ami ) (a ∈ A ), regulating capacity allocated to ith unit can be calculated by Equation (3):

MDP MODEL FOR OPTIMIZED ALLOCATION OF AGC REGULATION POWER

System composition of AGC is shown in Figure 1. As is shown in Figure  1, optimized allocation problem of AGC regulation power can be described as follows. Since power grid dispatching center can access the different frequency Δf, transfer capability of tie line ΔPtie in the each period of AGC control, ACE is concluded and sent to the AGC controller, and then the total regulating capacity ΔPr is calculated. Combining the actual running status of each units and grid condition, dispatching center obtains distribution factor of each unit by using optimization algorithm, such as target regulating power ΔPri provided by each unit is obtained. AGC is a periodic control system, therefore, it is easy to time discrete for allocation algorithm of AGC regulation power. Assume observing system at time t1 t2 , …tn , … , optimized allocation problem of AGC regulation power studied as a finite quadruple MDP (called MDP) model. MDP 2.1

(S , A, R,T )

(1)

Possible states S

Due to regulating capacity of AGC, units are based on total regulating capacity ΔPr gained by AGC, and the regulating capacity of AGC unit is limited, so division scope of ΔPr is limited. Thus the discretization of ΔPr into limit state values can be got and limit state space. 2.2

ΔPri (t )

2.3

Figure 1.

R

k

2 n ⎧ ⎡ ⎛ ⎤ ⎛n k −1 k− k⎞ k⎞ ⎪ ⎢ ⎜ΔP Pr P ci ΔW Wi ⎟⎥ ∑ ∑ Gi⎟ ⎜ ⎪ ⎢ i =1 ⎟ + ω 2 ⎜ i =1 k ⎟⎥ , ACE ≥ δ = ⎨1 − ⎢ω1 ⎜ k ⎝ ⎠ ⎝ Cb ⎠⎥⎦ P b ⎪ ⎣ ⎪1, ACE < δ ⎩

(4)

N

System composition of AGC.

Reward function

The purpose of AGC is to maintain Area Control Error (ACE) at zero. At the modern interconnected power grid environment, to meet the requirements of the CPS standard, reward function is ordered to reflect the tracing performance of generator units with performance and consider economy of the regulation in conformity with the CPS standard as far as possible. To improve the frequency quality in optimized allocation, dead zone of ACE is necessary to consider when a load disturbance occurs during a dispatching plan. It is prescribed that maximum reward is obtained when ACE in the scope of dead zone which conforms to the control requirements, whereas reward value is given by multi-objective reward function when ACE in the scope of regulating zone. Then reward function is designed as multi-objective piecewise function given as Equation (4):

Action sets A consists of distribution factor of each unit α: (an1, …, ani ) | (a ⎫ 0 ≤ ani ≤ 1, ∑ ani = 1⎬ n =1 ⎭

(3)

Due to the limited number of AGC units involved in regulating, so here the available action set A is limited to discrete values.

Available action sets A

A = {(a11, …,, a1i )

ami ΔPr

(2)

where ω1 is the rapidity weight of unit tracing; ω2 is the economics weight of unit cost; ΔPr is total regulating power; ΔPGi is actual regulating power of ith unit; ci is unit regulating cost of each unit, yuan/MW ⋅ h−1; ΔWi is generator power of ith unit in a control period, MW ⋅ h−1; Pb is base power of current system, MW; Cb is generator cost of base power, yuan; δ is thresholds of ACE’s dead zone. Through building membership function of weight and fuzzification, weight in the multiobjective reward function is determined. Combined with CPS standard, the built membership function of weight in multi-objective reward function is described as follows.

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When CPS < 100%, CPS1 assessment is unqualified, so quick response of unit is needed to restore CPS index to normal range. So smaller the CPS1 is, the better the membership degree is. So falling kind membership function can be used as fuzzy constraints set of rapidity weight ω1, as Equation(5) shows: ⎧1, ⎪ 200 − CPS1 ω1 = ⎨ , ⎪ 200 − 100 0 , ⎩

3

C S1 < 100 100 < CPS1 < 200

(5)

C S1 > 200

whereas when the CPS1 > 200%, the ACE is positive for frequency quality of interconnected power grid. The magnitude of the ACE need not be considered, but only the economics is taken into consideration which means reducing relating cost as far as possible. As a result, the bigger the CPS1 is, the greater the membership degree becomes. Then the rising kind membership function can be used as fuzzy constraints set of economics weight ω2, demonstrated as Equation(6), ⎧0, ⎪ CPS1 − 100 ω2 = ⎨ , ⎪ 200 − 100 ⎩1,

Q-learning is proposed to train optimal value function to avoid calculating transition probability directly.

C S1 < 100 100 < CPS1 < 200

Q-LEARNING USED IN DYNAMIC OPTIMIZED ALLOCATION ALGORITHM FOR AGC REGULATION CAPACITY

In this paper, Q-learning is studied for dynamic optimized allocation policy for AGC regulation capacity, and AGC regulating capacity optimally allocated based on measurable information of the AGC system. ΔPr is regarded as state input and the state is divided according to actual AGC regulation capacity. The action sets consist of distribution factor α and depend on the number of units specifically involved in regulation, as shown in simulation example. Reward function is defined in Equation (4). In Q-learning, the Agent depends on environment state s to choose an action a at the moment t, and it observes instantaneous reward R and new state s’, then updates Q according to (8) (α is learning factor to maintain convergences)

(6)

CPS1 > 200

Qt (ss a )

(1

)Qt ( s, a ) + α [Rt

Qt s a

] (8)

2.4

The state transition probability function

The next state s of AGC system’s dynamic frequency response is due to current state sk and action ak, and has nothing to do with last state. Although both the actions and states are limited, the dynamic system is described by a series of probability models as Equation (7). a Pxy

P ob ⎡⎣ xk = y xk = x,aak = a ⎤⎦ x, y X , a A (7)

a where Pxy is probability of state x transfer to state by taking action α. Since there is not enough knowledge to gain state transition probability,

When an action can be chosen by the one with the maximum Q value for the current state in the Q-learning algorithm, this method is called greedy policy π#:

π # ( ) argg max Q k ( , a )

whereas there are risks of using this policy continuously, the Agent may be bound in the action with maximum Q value in the early training, and ignores researching for other existing action with more Q value. Therefore, the approach of probability distributions is commonly used to choosing action in Q-learning. This paper adopts pursuit algorithm to construct the selection strategy. This method first gives the same probability to any available action under every state, namely: ps0 (a ) =

Figure 2.

Membership functions of weights.

(9)

a A

1 , ∀a ∈ A ∀s S A

(10)

In the initial phase of learning, the algorithm chooses the action randomly. During the process of learning, according to the change of the Q function form, probability distribution of each action updates as Equation (11).

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⎧ psk +1(ag ) = psk (ag ) + β ⎡1 − psk (ag ) ⎤ ⎣ ⎦ ⎪ ⎪ k +1 k ⎨ ps (a ) = ps (ag )(1 − β ), ∀a ∈ A, a ≠ ag ⎪ k +1 ∀a ∈ A, ∀s ∈ S , s ≠ s (11) ⎪ ps (a ) = psk (ag ), ⎩ psk (a ) represents the probability of taking action a in the kth iteration, that is Pr ob( k ) = psk (a ). β(0 < β < 1) is a constant. “explored” and “used” for enough iterations, Qk will converge the probability of the optimal function Q# to 1. Finally a matrix Q# presenting the optimal control policy is concluded. Exploration policy is available for the Agent in the early stages of learning policy, and then gradually it transitions to the use type policy. The algorithm aims to minimize the cumulative variance between actual output of units and regulating capacity and to minimize the regulating cost in a control period, yet has nothing to do with backward state. So we take discount factor γ is 0.0001, β is 0.01, and α is 0.1. To sum up, Q-learning algorithm based on dynamic optimized allocation for AGC regulation capacity is described as in Figure 3. Q-learning algorithm has the characteristics of independence on the model, and thus the limit that included the rate of regulating and capacity of

unit don’t have to demonstrated in the algorithm, only if add the constraint set when the simulation is built. 4

EXPERIMENTAL SIMULATION AND ANALYSIS

4.1

The establishment of double areas’ simulation model for AGC control system

This paper adopts load frequency’s control model in the double area interconnected system as the research object and related parameters shown in Table  1. Gross rated capacity of the system is 18,000 MW, load under normal operation is 12,000 MW, and AGC adjustable capacity is 6000 MW. Instead of only one equivalent unit simulating the generation process in area 1 in the former model, three units with different inertia (inertia constant is Ts), power constrains, climbing constrains, and regulating cost are used for this simulation model, and related parameters are shown in Table 2. Since there is no load disturbance in area 2, one equivalent unit model remains. The simulation is based on MATLAB/simulink. 4.2

Q-learning algorithm module

For simulating the Q-learning algorithm, MATLAB/S-function module is used. Based on AGC regulation capacity, the state space S is divided into 12 discrete states as (−∞, −3000], (−3000, −2000], (−2000, −1000], (−1000, −500], (−500, −250], (−250, 0], [0, 250), [250, 500), [500, 1000), [1000, 2000), [2000, 3000), [3000, +∞). Actions matrix A  =  {(0, 0, 1.0), (0, 0.1, 0.9), (0, 0.2, 0.8), …, (0, 0.9, 0.1), (0, 1.0, 0), (0.1, 0.9, 0), (1.0,0,0)}, and there are 66 discrete action values. Therefore, initial Q matrix is a zero matrix containing 12*66 elements, and it is typed from Y0. Since Q-learning algorithm remain studying in choice at the initial stage of learning, and brings about instability to system. The search of environment to gain the optimal action policy, there exists strongly random of action. So before practical operation, Q-learning algorithm must be pre-learned for a period of time, it ensures that the Q matrix is close to the optimal matrix Q#. In pre-learn-phase, bigger disturbance added is conTable  1. system.

Figure 3.

Q-learning algorithm flow diagram.

Parameters of double area interconnected

Tg/s

Tt/s

TP/s

R/(Hz/MW)

Kp/(Hz/MW)

T12/s

0.08

0.3

20

1/7200

1/360

0.545

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Table 2.

Related parameters of three units.

Unit type

TS/s

Tg/s

I II III

180 120 60

0.1 0.08 0.05

min

max

+

−

Tt/s

R Hz/MW

PG /MW

PG /MW

Prate /MW ⋅ min−1

Prate /MW ⋅ min−1

cost Yuan /MW ⋅ h−1

0.3 0.3 0.2

1800 1800 3600

750 750 1500

−750 −750 −1500

120 180 450

−120 −180 −450

100 150 200

Figure 4.

Actual active power output.

tributed to Q-learning algorithm access to actions with all states. Based on the above, a continuous step load disturbance whose period is 1000s and amplitude is plus or minus 2000 MW is added into area 1. After pre-learning, we go through enough iterations (Q matrix is close to the optimal matrix Q#), Q-learning algorithm could be used in practical environment. The AGC period is taken as learning step size, 4 s is taken in this simulation. 4.3

Simulation of multi-objective piecewise optimized function based on Q-learning

In this simulation example, besides the simulation study for multi-objective piecewise optimized function based on Q-learning discussed above, the average allocation strategy generally adopt by actual AGC allocation policy is introduced to compare with, and also multi-objective controllable AGC regulation power optimization allocation described in [6] is introduced. Then taking advantage of rapidity weight, allocation factor sought out is (0.16, 0.24, 0.6) by genetic algorithm. The simulation is experimented to allocate the AGC regulation capacity under the same output of AGC controllers. The curves of actual active power output under three allocation policies are given in Figure  4. The simulation demonstrates that all of the three allocation policies trace the load disturbance well under small load disturbance, yet when bigger disturbance occurs, the Q-learning algorithm rises unit output to 2000 MW in 220  s, while genetic algorithm uses 280 s and average allocation strategy uses about 350 s. Obviously, Q-learning algorithm allocation embodies outstanding advantage in rapid tracing of load disturbance. The curves of total regulating power of AGC under three allocation policies are given in Figure 6. Combined with changing condition of the frequency deviation, it is demonstrated that after adding +1000 MW load disturbance, system frequency returns to allowed value due to the understanding of tracing performance of the Q-learning algorithm. So the changing amplitude of total regulation power with Q-learning algorithm is smaller than genetic algorithm and average allocation

Figure 5. Δf of area 1.

Figure 6.

Total regulation power of AGC.

strategy. The favorable tracing performance helps recover the frequency after larger disturbance. The largest error of total regulation capacity and unit output is only 35% of average allocation strategy and 20% of genetic algorithm. It is concluded that

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more stable total regulation power could be gained by optimized allocation policy based on Q-learning, rather than the traditional allocation policy. The curve shows changing condition of system performance evaluation index CPS1 by using the three allocation policies under the above disturbances shown in Figure 8. It is analysis that policies can remain value of CPS1 within qualified scope when smaller disturbance occurs. When it comes a larger load disturbance at the time 3000s, ACE is quickly expanded. For recovery ACE to normal value, optimized policy based on Q-learning is able to gain the instantaneous reward according to changes of CPS’ value to choose proper action and controls CPS1 within qualified assessment indicators. Both average allocation strategy and

Figure 7.

ACE of area 1.

genetic algorithm are static optimization algorithms so that distribution factors cannot adjust according to the variation of CPS1. So the value of CPS by using optimized policy based on Q-learning is superior than using traditional allocation policies. When load distribution decreases to zero suddenly, value of CPS1 by Q-learning algorithm is smaller than genetic algorithm. It is explained that because of great output for large disturbance, ACE and frequency deviation are not small, so value of CPS1 which was calculated by this two is small. From the curves of CPS1 in area 2, it is concluded that rapid recovery of frequency in area 1 contributes to reduce the effect to area 2. After 1000 MW load disturbance added during the time of 1000s–2000s, the cost sequence arranged from high to low is genetic algorithm with 44,000 Yuan, average allocation strategy with 38 and Q-learning algorithm with 34. Since Q-learning response quickly to big disturbance by increasing the distribution factor of high-regulating speed-units so that recover the frequency rapidly. Although it increases cost for the unit with high regulating speed, it makes units with better economics involved in regulating soon when the frequency returns to allowed values. It is evident that the multi-objective piecewise optimized function based on Q-learning is able to dynamically adjust distribution factors in an ever-changing operation environment of power gird. 4.4

Figure 8.

CPS1 curve of area 1.

Figure 9.

CPS1 curve of area 2.

Simulation on changing dispatching plan

As shown in Figure  8, under current dispatching plan, when disturbance which is 2000 MW is occurred in the system, value of CPS1  in area 1 greatly reduces, and beyond 100%, the minimum value is allowed by assessment standard. So dispatching plan has to be changed when big disturbance emerges, and more units should be involved in regulation. In practical, variable types of units to complete different regulation, and undertake different regulation quantity is taken consideration into operation of AGC. Therefore, the simulation is carried out to study changing dispatching plan of the AGC system. Add square waves with period is 2000s and amplitude is 2000 MW to area 1. Under the first disturbance, Q-learning algorithm allocates power as the consequence trained before. In the time of 3000s, the second square wave comes, with the increase of ACE, AGC system changes its dispatching plan, increasing more AGC units good at rapidity to regulation. The regulation speed of these units increases to ±600 MW/min from ±450 MW/min. The curves of units output and regulation power are shown in Figures 10 and 11.

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5

Figure 10.

Actual output power.

Figure 11.

Total regulation power of AGC.

CONCLUSIONS

1. Dynamic optimized allocation policy for AGC regulation capacity based on Q-learning and fuzzy multiple objectives could carry on dynamic demand allocation quickly according to load disturbances. 2. The simulation results show that multi-objectives optimized policy based on Q-learning and fuzzy could designe fuzzy multi-objectives piecewise functions through reward function of Q-learning, it takes both rapidity of frequency recovery and economics of regulation into consideration to realize multi-objectives optimization. As a result, stability of power grid and economic effect are improved. 3. Q-learning algorithm is a kind of reinforcement algorithm with real-time online learning ability. It is able to adapt to the changes of operation environment, and provide real-time allocation action, thus improving the adaptability and robustness of optimal allocation of the AGC regulation power to the whole power system. REFERENCES

From the diagrams (Figs. 10 and 11), Q-learning algorithm could seek out the change of units’ characteristic which is increasing regulation speed of rapidity unit, and begins to update action policy automatically. Changing of units’ characteristic broke up the optimal Q matrix trained under previous characteristic, and Q-learning algorithm is carried on a new learning process so that large fluctuation appeared in both output power of units and total regulation power Pr. It is demonstrated that the algorithm converges after training for about three periods. Then the curve of output power tends to smooth and the power curves do not fluctuate as in the training. Obviously, the tracing performance to load is better than before dispatching plans changes. But average allocation strategy and genetic algorithm cannot change units’ characteristic, even provide optimized distribution factors to adapt to the environmental change.

[1] Yu, T. & Zhou, B & Zhen W.G. Application and development of reinforcement learning theory in power systems. Power system Protection and Control, 2009, 37(14). [2] Eftekharnejad S, FEliachi A. Stability Enhancement Through Reinforcement Learning: Load Frequency Control Case Study. in: 2007 IREP SymposiumBulk Power System Dynamics and Control. 2007.1–8. [3] Yu, T. & Wang, Y.M. & Liu, Q.J. Q-learning-based dynamic optimal allocation algorithm for CPS order of interconnected power grids [J]. Proceedings of the CSEE, 2010(7). [4] Yu, T. & Wang, Y.M. & Zhen, W.G. (eds). Multi-step backtrack Q-learning based dynamic optimal algorithm for auto generation control order dispatch. Control Theory & Applications, 2011, 28(1):58–64. [5] Bertsekas, D.P. & Tsitsikilis, J.N. Neuro Dynamic Programming. Belmout, MA: Athena Scientific, 1996. [6] Qian, H. & Yao, Y.M & Liu, G. Target-controllable optimal dispatch for AGC regulation capacity. Journal of Shanghai University of Electric Power, 2013, 29(4):321–324, 329.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Fault diagnosis of rolling bearing based on Fisher Discriminant Analysis Wei-Bing Zhang, Chu-Guang Liang & Gang-Cheng Li Department of Information Engineering, Hunan College of Information and Engineering, Changsha, China

ABSTRACT: This paper presents a fault diagnosis method of rolling bearing based on Kernel Fisher Discriminant Analysis (KFDA). The basic idea of KFDA is to map the original space into a high dimension feature space via nonlinear mapping and then extract the optimal Fisher feature vector and discriminant vector to achieve the state monitoring and fault diagnosis of rolling bearing. The experiment result shows that the KFDA method is able to recognize the fault patterns effectively. Keywords: (KFDA) 1

rolling bearing; fault diagnosis; pattern recognition; Kernel Fisher Discriminant Analysis

INTRODUCTION

The faultdiagnosis of machine aims at extracting its running characteristic parameters to identify or estimate which state the system is in. By establishing the relationship between characteristic information and status information to make an overall judgment, the fault monitoring or diagnostic procedure essentially is a fault pattern recognition and classification process[1]. In this, Fisher Discriminant Analysis (FDA) is employed as the identification and classification method of rolling bearing. However, FDA is essentially a linear classification method, but the signals of rolling bearings tend to be non-linear and non-stationary. In view of this, we propose a fault diagnosis method of rolling bearing based on Kernel Fisher Discriminant Analysis (KFDA). 2 2.1

KFDA METHOD

nuclear learning to FDA. Making great use of the idea of nuclear learning methods, first by a nonlinear mapping Φ , it maps the data of space R d into a high dimensional kernel space and then does the Fisher linear discriminant analysis in the highdimensional kernel space. Thus, the sample characteristics obtained in high-dimensional kernel space is non-linear with respect to the original space, which is more conducive to classification. 2.2

1.2.1 Kernel Fisher discriminant analysis Let be a nonlinear mapping from an input space to a certain feature space: Φ : X → F . By nonlinear mapping, the vector collection in the input space X1 X 2 , …, X N is mapped to Φ( 1 ), ), Φ((X X 2 ), …, Φ(X N ) in the feature space. So we can define the mean vector miΦ for the two types of training samples in the feature space as

Overview

The paper[2] describes the linear Fisher Discriminant Analysis (FDA) algorithm, which is a method designed to reduce the number of feature dimensions and supervise learning algorithm, in order to set up a subspace (composed by all the projection axes). All samples meet the minimum within-class scatter and the maximum inter-class scatter in this subspace. The projection coefficients of all samples in these projection axes are used as the feature vectors of samples then making use of these feature vectors to identify the classification for samples. KFDA is thought to be a new nonlinear characteristics extraction method produced by introducing

Kernel Fisher discriminant analysis and algorithms achievement for two types of problems

⎛ 1⎞ miΦ = ⎜ ⎟ ∑ Φ(X ) ⎝ Ni ⎠ X X i

(1)

Ni represents the number of samples in class i and N N1 + N2 , then the inter-class scatter matrix of samples SbΦ is SbΦ

(m m1Φ − m2Φ )( m1Φ − m2Φ )T

(2)

the total with-class scatter matrix is SwΦ

2

∑∑

X

miΦ )( (X ) − miΦ )T

(3)

i =1 X X i

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Fisher discriminant analysis is done in the feature space, the Fisher discriminant criterion is defined as: F (W ) =

max

W

(SwΦ )−1(m ( m1Φ

(4) M

m2Φ )

(5)

the projection of Φ( ) on W * is y W

∗T

H

(X i )

(X j )

(7)

Given that W can be represented by a linear Φ( 1 ), ), Φ((X X 2 ), …, Φ(X N ), that is to say (X i )

W T miΦ =

1 Ni

k(

( p,Xq

(p

, 2,

i

)

)

, N ; q 1, 2,

, Ni )

(14)

I is a unit matrix of Ni Ni , Li is a matrix of Ni and all its elements are 1 Ni . Essentially, in the equation (11), α is the feature vector of the matrix H 1M corresponding to the largest eigenvalue. It can be directly obtained by the following formula

Ni

α = H −1(M1 − M 2 )

(15)

To obtain W*, it’s essential to make H a positive definite matrix. To do this, simply adding a quantity μ of the matrix H, that is replacing H with H μ = H + μ I where I is a positive definite matrix. Finally, in the feature space, the projection of Φ( ) on W is converted into the projection of k ( , X ) on α . That is N

∑ α j k (X j , X )

(16)

j =1

This indicates that the conversion gets back to the data space. For kernel Fisher discriminant analysis method, demarcation threshold point y0 can be selected as

N Ni

∑ ∑ α j k X j , X k(ω ) ) i

j =1 k =1

= α Mi (i = 1, 2 ) T

⎛ 1 ⎞ Ni = ⎜ ⎟ ∑ k X j , X k( i ) ⎝ Ni ⎠ k =1 (i , 2; j 1, 2, , N )

(

y0 =

(9)

where Mi is a matrix of N × 1 and ω i stands for the sample of type i . And i)j

i ) p,q

(8)

combine formula (1) and (8), then we can get

(

(13)

Li )K iT

y W T ⋅ Φ(X )

i =1

yi

∑ Ki ( I

Among them, K i is a matrix of N Ni (i = , 2 ), representing the kernel matrix of type i and it meets

N

∑α i

(12)

(6)

The formulas (1) to (6) are performed in the feature space. Due to a high dimension of feature space, or even an infinite number of dimension, it is not possible to directly calculate the optimal projection direction. Reproducing kernel theory[3] of machine learning shows that the above operation can be carried out by the inner product kernel function defined by the original space, not relating to a specific nonlinear mapping. So it is necessary for formula (4) to be transformed so that it contains the inner product operation of mapped data. Inner product operation is represented by an appropriate kernel function.

W

(M1 − M 2 )(M1 − M 2 )T i =1,2

(

Φ(X )

(X i , X j )

(11)

where

W T SbΦW W T SwΦW

where W is the direction of the projection line. Then optimal projection direction can be obtained from the above equation, namely ∗

α T Mα α T Hα

max (α ) =

)

(10)

Combining all these kinds of formulas and through a series of matrix operations and transformation, formula (4) is equivalent to

N1m 1Φ + N2 m 2Φ N1 N2

(17)

where N1 and N2 represent the number of first and second type of training samples respectively, m iΦ (i , 2 ) is the average value of each type of the projected samples and it is a scalar, then m iΦ =

1 Ni

N

∑ ∑α j k X j ,X )

(18)

X ∈ω i j =1

At last, projecting the pending samples in the optimal projection direction to obtain the value

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of y. Do classification according to the following decision rule: if y  y0 , then it belongs to ω1, in contrast, if y ≺ y0 it belongs to ω 2 . 2.2.2

Achievement of two types of Fisher algorithms based on kernel: Å Figure out ( i ) j due to formula (9). Take a Gaussian radial basis function as kernel function: k (X j , X k(

i)

)

⎧⎪ X X k( exp ⎨− j 2σ 2 ⎩⎪

i)

2

⎫⎪ ⎬. ⎭⎪

4

Ç Figure out H and H μ = H + μ I due to formula (12) and (13). É Solve α = H μ−1(M1 − M 2 ). Ñ Find projection of various N types of training samples yi W *T ⋅ Φ(X Xj k (X i , X j ), (j = i i =1 1, 2, …, N). y. Ö Figure out average vector m iΦ 1/Ni yi∑ ∈ω i i Ü Find the threshold point y0 by the formula (17). á Seek the projection point y for pending samples x. à Do classification according to decision rule. 3

order p, which is estimated using auto-correlation algorithm[9] and taking P = [a1, a2, … ap] as the feature vector. Working out ailt (t = 1, 2, 3; i = 1, 2, 3, …, M; l  =  1, 2, 3, …, p) respectively in the same state of M samples, where t represents the running state of bearings (1 stands for normal situation, 2 stands for the inner ring fault and 3 stands for the outer ring fault), i denotes i-th sample of a certain state, l denotes the l-th parameters of AR model of the i-th sample.

STEPS OF FAULT DIAGNOSIS OF ROLLING BEARING USING KFDA

Applying KFDA method to the fault diagnosis of rolling bearings, firstly we establish AR parameter model using timing method for the vibration signals of rolling bearings in all kinds of states[4] in order to transform the vibration signals into feature parameters of time series model. Concentrating all the information of the original time series signals[5,6], the parameters of timing model can be done KFDA analysis as feature vectors when the bearing is running, so as to achieve the purpose of recognizing all various types of states and diagnosing faults of rolling bearings. Specific steps are as follows: Under normal bearing, the inner ring and outer ring fault state respectively, M samples are performed according to a sampling frequency fs and 3M vibration signals are obtained to be a sample. Establish AR model of order p for vibration signals:

EXPERIMENT RESULTS

The experiment is conducted utilizing the data of the literature[7]. Vibration signals are detected using piezoelectric acceleration sensor for the 6310 type of bearing in the three types of state, the normal, the outer ring and the inner ring faults. Take a total of 42 test bearings, which include 10 normal bearings, 16 outer ring fault bearings and 16 outer ring fault bearings. Each bearing samples 5 times, each time taking 1024 data. There are 6 normal bearings, 9 outer ring fault bearings and 8 inner fault bearings for training, and the remaining 19 bearings are used for validation sample after training. The parameter of Gaussian radial basis function σ takes 2 /22 and μ equals 0.0001 in the algorithm. As used here, using MATLAB simulation analysis method, the accuracy of fault diagnosis is more than 95%. Experiment results demonstrate the effectiveness of the methods used in this article. 5

CONCLUSION

KFDA is an extended form of linear FDA, maintaining the advantages of the FDA, but also having the ability to handle non-linear problems. It’s very sensitive to the non-linear characteristics of machine faults and suitable for nonlinear data classification process. The different rolling bearing faults can be well separated by this method that has a good classification performance and provides a new way for fault diagnosis of rolling bearings. REFERENCES

p

x ( n ) = − ∑ a p (i )x ( n i ) ε ( n )

(19)

i =1

where ε ( ) is the white noise signal whose mean is zero and variance is σ 2 , p is the order of the AR model. It is determined by the use of the Final Prediction Error (FPE) guidelines[9]. ai (i , 2, , p ) is the autoregressive parameter of AR model with

[1] Xiao J.H. The intelligence pattern recognition methods. South China university of technology press, 2012.8. [2] Bian Z.Q, Zhang X.G, Pattern recognition (second edition). Tsinghua university press, 2000. [3] Lennox B, Hiden H.G, Montague G. Application of multivariate statistical process control to batch operations. Computers and Chemical Engineering, 2010, 24: 291–296.

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[4] Product research and development center of FeiSi Technology. Signal processing technology and application using MATLAB 7. Electronic Industry Press, 2005. [5] Ding Y.L, Shi L.D. Mechanical fault diagnosis technology [M].Shanghai: Shanghai science and technology literature press, 1994: 225–230.

[6] Liu T.X, Zheng M.G, Chen Z.N. Application research of AR model and fractal geometry in equipment condition monitoring [J]. Journal of Mechanical Strength, 2010, 23(1):61–65. [7] Lu S. Intelligent diagnosis technology research of rolling bearing based on modern signal analysis and neural network. Ph.D. Thesis of Jilin university, 2008.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The voltage coordinated control strategy of the power grid which includes large scale wind power R. Shi, R. Jiao & Z.J. Chi Beijing Electric Power Research Institute, Beijing, China

X.N. Kang School of Electrical Engineering, Huazhong University of Science and Technology, Wuhan, China

ABSTRACT: The power grid, which includes large scale wind power, raised new challenges in reactive power control. This paper analyses the characteristics of renewable energy, which are—duster development, access to weak grids and long distance delivery. The paper also proposesthe strategy of the System of Wind Power field group net reactive power and voltage control coordination. This control strategy is based on the ideas of “multi-level coordination and progressive refinement”, and then the strategy is divided into four control strategies: wind machines strategy, wind farm strategy, wind group strategy and the network strategy. The highest level control layer is the network Each control station accepts the higher level control signal and takes it as a control target to perform, which the higher control signals sends to the lower stations. This control strategy strives to achieve the security and stability of the whole network voltage and to the maximum extent possible to the output of the wind power. Lastly, the result is combinedwith Gansu power grid, to discuss and test the validity and reliability of the strategy. Keywords: the power grid; large scale wind power; the voltage coordinated control strategy; multi-level coordination 1

INTRODUCTION

This template provides authors with most of the formatting specifications needed for preparing electronic versions of their papers. All standard paper components have been specified for three reasons: (1) ease of use when formatting individual papers, (2) automatic compliance to electronic requirements that facilitate the concurrent or later production of electronic products, and (3) common Introduction. The distributed generation power using renewable energy has been a hot area of research. In recent years China’s power grid structure has changed dramatically; the network structure is complicated and further requests have been advanced in reactive power and voltage. China’s new energy development generally has a cluster development, weak grid access and long distance delivery characteristics. New sources of volatility of strong, weak controllability, centralized access areas often lack the support of conventional power, reactive power and voltage problems are likely to cause system security incidents[1,2]. Reference 1 was considered from the view point of the time scale as well as the response time of the

source of reactive power compensation; the multilayer static and dynamic voltage coordinated control strategy of wind farms “program  + online  + urgent” was proposed. In reference 5, a group of wind farms are the central focus; the high side voltage of the step-up transformer of each wind farms is the constraint: a proposal for the coordination of the various wind farm reactive power regulating devices in operation was put forward. However, neither of them use a layered model to describe the System of Wind Power field group net reactive power and voltage control coordination. This work has played a positive role on reactive voltage control, but it is necessary to consider the complex factor of the large scale wind power in the overall grid voltage and the reactive power optimization control—from the perspective of the whole grid: it is important to solve the problem of reactive power from the local voltage to the power total system and to put forward an advanced reactive voltage control strategy. This paper, embarking from space granularity, proposed the strategy of System of Wind Power field group net reactive power and voltage control coordination under the application background of the Gansu power grid.

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2

THE GENERAL IDEA

The fundamental purpose of the strategy of System of Wind Power field group net reactive power and voltage control coordination is to solve the complex

Figure  1. A control strategy for the overall framework map.

Figure 2.

reactive voltage of the System of Wind Power field group net, to analyse multi-level problems from the perspective of space decoupling, to propose the control strategy of the System of wind turbine, wind farm, wind group and network, to achieve the objective of the safe and stable operation of the entire network voltage and the largest wind power output. To achieve the above-mentioned objectives, the paper designs the general framework of control strategy as shown in Figure 1. The control strategy mainly consists of four parts: the grid level control strategy, the group level control strategy, the field level control strategy and the wind turbine level control strategy. The basic idea for strategy of System of Wind Power field group net reactive power and voltage control coordination is that the grid level is the highest level of control layer; the grid level control station according to the voltage of the weak to choose reactive power optimization mode or voltage control mode; the group level is the senior level of control layer; based on the control command, it carries on the corresponding model calculation and control targets to stand at a lower level; the field level is both control layer and actuating layer; based on the proposed control objectives, it selects the local control

The strategy of System of Wind Power field group net reactive power and voltage control coordination.

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and lower wind turbines or photovoltaic modules; the wind turbine level is the execution layer: based on the superior orders it executes the reactive power regulation. The key to this strategy are the model calculations of the group level control and the field level control strategy. 3

THE CONTROL STRATEGY

This particular embodiment of the control strategy is shown in Figure 2. The goal of this policy is to improve the voltage control of power network, which includes large scale wind power from the global and local resources. On the whole, it makes the wind farm which has the relevant characteristics and similar location, as a common access point: it can then be a point centralized coordination and control. Through this strategy, it can effectively stabilize the single output of a station’s volatility and random and intermittent energy. Wind farm/ photovoltaic power plants on the scale and external control characteristics are similar to conventional energy supply, with flexible scheduling and the ability to control the response grid and also to improve new energy efficiency. On the local, each control station accepts the higher level control signal and takes it as a control target to perform, which the control signals send to the lower stations. Wind turbines, photovoltaic modules and reactive power compensation equipment are orderly and action efficient. 4 4.1

Table 1.

Characteristics of reactive power.

Reactive compensation source

Reactive Response power speed regulation

Static Var Fast Compensator (SVC) Reactor Faster Capacitors

Faster

Transformer joint adjustment Wind turbines

Slow

Slower

Control

Continuous Imple

Discrete

Relatively simple Discrete Relatively simple Continuous Complicated

Continuous More complex

CONTROL STRATEGY FUNCTION Basic principles of local control

Dynamic reactive power compensation devices generally include reactors, capacitors and a static var generator. According to the reactive power source control features on the table for analysis, this paper presents the basic principles of local control: 1. First, make SVC regulation. The SVC control station is called first SVC, until exhaustion date SVC; 2. Secondly, the regulation of the capacitive reactance. If the SVC is exhausted or there is no static compensator device, then it calls for capacitive reactance; when the voltage is too high, we must direct back the reactors in the sub-station; when all reactors quit, grouping cast capacitor until the voltage controller qualified or capacitors all put up. When the voltage ran in low pressure critical conditions, the strategy predicted the output of wind power. If the forecast of the wind power output is significantly reduced and

Figure 3.

Priority selection method.

the voltage on the rise, to avoid the frequent movement of the reactive power compensation device, it should stop regulation; If the forecast of the wind power output will not register significantly, it should reduce the packet back to sub-station reactors; and when all reactors quit, it need cast capacitor by group until the voltage qualified or capacitors all put up. 3. Transformer tap regulation. 4. Wind Turbine regulation. Select the followed wind turbine by priority regulated. 4.2

Priority selection method

The priority is shown in Figure 3.

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5

APPLICATION CASE

This research is correlated with the Jiuquan wind power base as an application platform and PSASP as a emulated platform. Xinjiang output active power 0 mw to Gansu. Dunhuang area’s active power from wind power accounted for 15% of its maximum activity. The Dunhuang area has a light load and high voltage at the moment. The Dunhuang 750 KV station voltage is 806.681  KV, in accordance with the strategy of System of Wind Power field group net reactive power and voltage control coordination into a voltage control mode. First determine the group level control station, the field level control station and the actuating station. And based on the priority selection method sort out the actions of the field level control station. The control strategy for the specific implementation plan of the Dunhuang area is shown in Figure  4. All controller layers of each level control stations are given in detail in Figure 4; a sequential action was carried out on the field level control stations of 10 areas of Dunhuang. Therefore, it controls the voltage hierarchically according to this strategy. In this paper, the control voltage is beyond the qualified; after the voltage is

stable, it can be found that it takes time to settle and the result is not perfect. The known 330 KV voltage compliance range of [352,360], in 0 minutes in the voltage of each site in the Dunhuang area is unqualified as shown in Figure 5. The simulation has been worked with PSASP software. Based on the control strategy of this paper, all voltage sites are qualified in five minutes, and the use of any control method does not make all voltage qualified after 8 minutes. 6

CONCLUSION

The power grid, which includes large scale wind power, has lots of complex problems about reactive power and voltage; the strategy, combined with a variety of reactive power sources and operating characteristic analysis of multi-level problems, analyzed this complex problem at different levels and proposed the reactive power and voltage control strategy of System of Wind turbine, wind farm, wind group and network. This policy is conducive to the full use of wind energy resources, with the domestic million kilowatts of wind power base: the paper control strategy has reference to this beautiful power based reactive power control. REFERENCES

Figure 4. The control strategy of the Dunhuang region.

[1] Tapia G., Tapia A., Ostolaza J.X. Proportional-integral regulator-based approach to wind farm reactive power management for secondary voltage control. IEEE Trans on, Energy Conversion, 2007, 22(2): 488–498. [2] Kayikci M., Milanovic J.V. Reactive power control strategies for DFIG-based plants. IEEE Trans on Energy Conversion, 2007, 22(2): 389–396. [3] Qiao Ying, Lu Zongxiang, Xu Fei. Coordinative strategy for automatic voltage control of wind farms with doubly-fed induction generators [J]. Automation of Electric Power Systems, 2009, 33(4): 87–91 (in Chinese). [4] Chen Ning, Zhu Lingzhi, Wang Wei. Strategy for reactive power control of wind farm for improving voltage stability in wind power integrated region [J]. Proceedings of the CSEE, 2009, 29(10): 102–108 (in Chinese).

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on predict Direct Capacitor Power Control of voltage source PWM rectifier, applied to electric vehicle charging and discharging field Cheng Gong, Long-Fei Ma, Zhong-Jun Chi, Wei Li, Bao-Qun Zhang & Yu-Tong Zhao State Grid Beijing Power Research Institute, Fengtai District, Beijing, China

ABSTRACT: To improve both the steady and dynamic performance, based on the mathematical model of the three-phase voltage source rectifier, this paper proposed a new control strategy with outer voltage square loop and inner capacitor active power and grid reactive power loop; for this 18 non-fixed sectors are divided and a scheme named predict direct capacitor power control is designed and realized by simulation. This new control strategy achieves the goal that the capacitor active power can be controlled directly by decoupling of the power of capacitor and load. Results of simulation prove the validity of this method. Keywords: PWM rectifier; predict; Direct Capacitor Power Control (DCPC); outer voltage square loop, non-fixed sectors 1

INTRODUCTION

The three-phase voltage type PWM rectifier has good prospects in fields of motor drive and new energy power generation. Applied in the electric vehicle, it can resolve the problem of harmonic and achieve the two-way flow of energy. According to different plants of the inner controller, the control strategies are mainly divided into two categories, which are, the current control and the Direct Power Control (DPC). Compared to the current control strategy, DPC has the advantages of the higher power factor, lower THD, higher efficiency and simpler structure, which has become the talking point in recent years. The Bidirectional DC-DC converter is the output unit of the electric vehicle charging equipment, which is also a kind of PWM converter. At present, the charging and discharging equipment of the electric vehicle is mainly composed by dual PWM converter above. Presently, both converters of the dual PWM converter work separately. When load disturbance occurs, the dynamic performance of the dc-link voltage is usually poor. A large capacitance is usually used to improve the performance, which causes other problems. In recent years, research has been conducted on the problems above. As to current control, current feed-forward control strategy [2] [3] and direct capacitor current control strategy [4] have been proposed—both can improve the dynamic performance. As for the power control, there has been little relevant research.

Based on the model of the dual converter, a new control strategy with two control loops is proposed in this paper—which is named predict direct capacitor power control (P-DPC). The outer loop controller adopts the voltage square form to realise the decoupling of grid-side active power, load power and capacitor power. In the inner loop, two power controllers are designed, based on index function, which minimise the error of grid side reactive power and capacitor power. By designing a kind of PWM converter power observer of the bidirectional DC-DC converter, the coordination control of dual PWM converter is realised. Simulation results show a good performance of the new control strategy. 2

THE POWER MATHEMATICAL MODEL OF PWM CONVERTER

Figure 1  shows the circuit topological structure of the grid-side PWM converter. The mathematical relationship between the phase current and voltages in the αβ-reference frame is shown as following: us

is + L

diis + ur dt

(1)

where us is the supply line voltage vector, is is the input current vector. L and R denote the inductance and resistance of the inductor; ur denotes the voltage vector generated by the converter.

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3

Figure 1.

Topological structure of PWM converter.

According to instantaneous power theory, the instantaneous active and reactive powers can be expressed in the stationary αβ-reference frame as: ⎧ p = usα isα usβisβ ⎨ ⎩ q usβisα usα isβ

As the formulas show, the DC side capacitor power flow will cause the DC voltage fluctuation. The outer voltage loop, the load power and the capacitance power decoupling can eliminate the disturbance of the capacitance power load power. As for the power ring, if we can control the pc equals to 0 or control the rate of change and there is no energy flow DC capacitor, then we can use smaller capacitor in DC side to maintain constant DC voltage. In view of the above ideas, this paper uses capacitor power as the control target to design the controller based on predict strategy, which is named the Predict Direct Capacitor Power Control Strategy (P-DCPC). Power prediction models are as follows: ⎧ dp − Rp − ω L Lq − (ur us + urβusβ ) + us2 ⎪ c = ⎪ dt L ⎨ − Rq − ω Lp L − ( urα usβ − urβusα ) d q ⎪ ⎪⎩ dt = L

(2)

Assuming the three-phase voltage symmetrical balance, there are ⎧ disa ⎪L dt = − Riisα − urα + usα ⎪ ⎪ di ⎪ L sβ = − Riisβ − urβ + usβ ⎪ dt ⎪ ⎪ dudc ⎨C = sα iα + sβiβ − iL dt ⎪ ⎪ du ⎪ sα = −ω usβ ⎪ dt ⎪ ⎪ dusβ ⎪⎩ dt = ω usα

pc

1 dVdc2 C 2 dx

dppc ⎧ ⎪⎪epci = dt | ur ui ⎨ ⎪ eq = dq | u u i ⎪⎩ i dt

(3)

us2 (4)

(5)

The formula (6) is rewritten as the form of PI, where vD = vdc2 pc

k⎞ ⎛ * (vD − vD ) kp + i ⎝ s⎠

−

dpl dt

(7)

Differentiating (7), we can get formula (8), from which we can see the available power changes caused by different voltage vector:

Based on (3), differentiating (2) results in instantaneous active and reactive power variations: ⎧ dp − Rp − ω L Lq − (urα usα + urβusβ ) + ⎪ = L ⎪ dt ⎨ Lp − (urα usβ − urβusα ) ⎪ dq − Rq − ω L = ⎪ L ⎩ dt

PREDICT DIRECT CAPACITOR POWER CONTROL

(6)

(8)

For each control cycle Ts, ui combination of different functions, can obtain Ts time changes in the amount of power: n ⎧ ⎪Δpc = pc ( k ) pc ( k ) ∑ eepci ti ⎪ i =0 ⎨ n ⎪ Δq = q( k ) q( k ) ∑ eqi ti ⎪ i =0 ⎩

(9)

By the direct capacitance power control thought that, with two of the ultimate goal of power control: 1) the power flow through a capacitor is approximately zero, so that the power is delivered to the load directly by the rectifier; 2) no power is approximately zero to absorb from the network side, so as to realise the network side unit power factor control. So the following design index functions are chosen as follows: W

2 Epc + Eq2

(10)

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Voltage vector selection determines the performance of the system. Based on paper [1–3], the mechanism of power voltage vector is studied further; 18 non-fixed sectors are divided. The voltage vector u1 as an example, assumes that the effect of u1 is none: ⎧ dp − Rp − ω L Lq − (u (urα usα urβusβ ) + us2 =0 ⎪ | u1 = ⎪ dt L ⎨ − Rq − ω L Lp − (ura usβ urβusα ) ⎪ dq =0 ⎪⎩ dt | u1 = L (11) Further processing, we can get the effect of increased or decreased boundary:

⎛ 3 3U m ⎞ ⎧ = ± arccos ⎜ ⎪ ωt | dp ⎟ |u1 = 0 ⎝ 2 2udc ⎠ ⎪ dt ⎪ ⎪ ⎛ 3 2ω Lp ⎞ ⎨ωt | dq = arcsin ⎜ ⎟ |u1 = 0 ⎝ 2 3U m udc ⎠ ⎪ dt ⎪ ⎛ 3 2ω Lp ⎞ ⎪ π + arcsin ⎜ ⎟ ⎪⎩ ⎝ 2 3U m udc ⎠

(12)

So the increased or decreased boundary of ui can be described as formula (13): ⎧ ⎛ 3 3U m ⎞ π (i − 1) arccos ⎜ ⎪ ωt | dpp ⎟ | u = 0 3 i ⎝ 2 2udc ⎠ ⎪ dt ⎨ ⎛ 3 2ω L Lp ⎞ π ⎪ ⎪ωt | dq |u = 0 3 (i − 1) arcsin ⎜ 2 3U u ⎟ i ⎝ m dc ⎠ dt ⎩

(13)

Table 1. Sector

1

2

3

4

5

6

7

8

9

Main vector Auxiliary vector Zero vector

u1 u2

u1 u2

u2 u1

u2 u3

u2 u3

u3 u2

u3 u4

u3 u4

u3 u4

u7

u7

u0

u0

u0

u7

u7

u7

u7

Sector

10

11

12

13

14

15

16

17

18

Main vector Auxiliary vector Zero vector

u4 u3

u4 u5

u4 u5

u5 u4

u5 u6

u5 u6

u6 u5

u6 u1

u1 u6

u0

u0

u0

u7

u7

u7

u0

u0

u7

lines are divided into 18 different dynamic sectors. The three voltage vectors selection method is adopted—themain vector, auxiliary vector and zero vector, vector selection, as shown in Table 1. According to the volt second balance principle, to further reduce power fluctuations, each vector time mean symmetric distribution, which is deduced as follows:

t1 =

( eq2 − eq0 )Δpp + ( ep e c0

eq0 epc 2 − eq1ep epc − eq2 eeppc 0 + eq1ep e c0 ( eq0 − eq1 )Δpp + ( eepc1 −

c0 )

Ts

2 eq0 ep eq e c1 eq2 ep e c1

q + ( eq1epc 0 − eq0 ep 1 )

Ts

t2 =

2 eq0 epc 2 − eq1epc 2 − eq2 ep 0 + eq1ep e c 0 − eq0 epc1 − eq2 epc1

t0

Ts / 2 − t1 t2

As for the bidirectional DC-DC converter, load power observer is needed to realizse P-DCPC, which can be deduced by formula 15. Pload 4

The division of switch vector function area.

eeppc 2 )Δq + ( eq0 epc 2 − eq2 epc 2 )

(14)

As shown in Figure 2, solid lines are for threephase static coordinate axis. Dashed and dotted

Figure 2.

Table of voltage vectors.

uo ⋅ io

(15)

SIMULATION RESULTS

To validate the theory, the simulation model is built in MATLAB. The sketch map of the system is shown in Figure 3. The buck-boost circuit is used in the bidirectional DC-DC converter and PI control strategy is adopted to control the switch. Simulation parameters are shown in Table 2. From Figure 4, we can see that the new strategy this paper put forward has a better dynamic performance compared toP-DPC. The fluctuation of dc-link voltage is smaller and the speed of dynamic response is faster when the load power changes suddenly at P-DCPC.

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Figure 3.

Sketch map of the control system.

Figure  6. The wave of current and voltage when discharging.

Table 2.

Parameters for the simulation system.

Figure 6 Shows the waves of the grid-side current and voltage when discharging. From Figure 6, we can get the conclusion that the power factor of the gird is nearly one when the motor is running in the state of energy consumption and the power factor of the gird is negative one when the motor is running in the state of discharging.

Input voltage us/V

Filter inductor L/mH

DC capacitor C/μF

DC voltage Udc/V

Switching frequency fs/Hz

220

4.4

660

500

5k

5

Figure 4.

CONCLUSION

Based on the predict theory as the foundation, this paper has proposed a control strategy named Predict Direct Capacitor Power Control (P-DCPC). In order to improve both the steady and dynamic performance, 18 non-fixed sectors were divided and a scheme named Predict Direct Capacitor Power Control was designed and realised by simulation. Simulation results show that the new control method has a better performance compared tthe traditional P-DPC method. The grid current has lower THD. The power factor of grid is close to unit, and the dc-link voltage has a better dynamic performance.

Voltage waveform of DC bus.

ACKNOWLEDGEMENT In this paper, the research was sponsored by the National High Technology Research and Development Program (“863”Program) of China (No. 2011AA05A109). Figure 5.

Pectrums of phase A current.

REFERENCES Figure 5 gives the spectrum analysis of the phase A current at traditional P-DPC and P-DCPC above, which includesthe dynamic process. As is shown, high order harmonics in both control strategies are centralised around switching frequency, which is easy to design suitable filters for the system.

[1] Larrinaga A, Vidal M, Yarbide E. Predictive control strategy for DC/AC converters based on direct power control [J]. IEEE Transaction on Power Electronics, 2007, 54(3): 1261–1271. [2] Yang Xingwu, Jiang Jianguo. Predictive Direct Power Control for Three-phase Voltage Source PWM Rectifiers [J]. 2011, 31(3): 1261–1271.

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[3] Shang Lei, Sun Dan, Hu Jiabing, et  al. Predictive Direct Power Control of Three-Phase Grid-Connected Voltage-Sourced Inverters [J]. 2011, 26(7): 216–222. [4] Wei Ke-xin, DU Ji-fei, Xiao Feng. Feed-forward Control Strategy of Three-phase PWM Rectifier With No Load Current Sensor [J]. East China Electric Power, 2010, 38(3): 356–357. [5] Li Guangye. Research on the Integrated Control of Dual PWM Converters [D]. Tianjin: Tianjin Univercity, 2011. [6] Li Mingshui, Wan Jianru, Li Guangye. Research on power feedforward control strategy of PWM rectifier [C]. 2011 4th International Conference on Power Electronics Systems and Applications, PESA 2011, Hong Kong, China, 2011. [7] Gong Cheng, Wan Jianru, Li Kunpeng. Direct Capacitor Power Control of Dual PWM Converter Based on Sliding Mode Variable Structure [C]. 2012 IEEE Innovative Smart Grid Technologies, ISGT Asia 2012, Tianjin, China, 2012.

[8] Ding Qi, Yan Dong-chao, Cao Qi-meng. Research on design method of control system for three-phase voltage source PWM rectifier [J]. Power System Protection and Control, 2009, 37(23):84–87. [9] Guo Wei-feng, Wu Jian, Xu Dian-guo, et al. Hybrid Shunt Active Power Filter Based on Novel Sliding Mode Control [J]. Proceedings of the CSEE, 2009, 29(27), pp. 29–35. [10] Wang Jiuhe, Yin Hongren, Zhang Jinlong, “Threephase voltage type PWM rectifiers with inner power loop and outer voltage square loop”, Journal of University of Science and Technology Beijing, Vol. 30, No. 1, 2008, pp. 90–95. [11] M. Malinowski, M.P. Kazmierkowski, A.M. Trzynadlowski, “A comparative study of control techniques for PWM rectifier in AC adjustable speed drives”, IEEE Trans. Power Electron., Vol. 18, No. 6, 2003, pp. 1390–1396.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Development of 2500V SMB-seagull SiC JBS diodes G. Chen Science and Technology on Monolithic Integrated Circuits and Modules Laboratory, Nanjing, China Nanjing Electronic Devices Institute, Nanjing, China

Q.M. Zhang Jinan Semiconductor Institute, Jinan, China

S. Bai Science and Technology on Monolithic Integrated Circuits and Modules Laboratory, Nanjing, China Nanjing Electronic Devices Institute, Nanjing, China

A. Liu Science and Technology on Monolithic Integrated Circuits and Modules Laboratory, Nanjing, China

L. Wang & R.H. Huang Nanjing Electronic Devices Institute, Nanjing, China

D.H. Li & Y.N. Li Jinan Semiconductor Institute, Jinan, China

ABSTRACT: High voltage 4H-SiC Ti schottky Junction Barrier Schottky (JBS) diode with breakdown voltage of 2500 V and forward current of 2 A has been fabricated. A low reverse leakage current below 1.13 × 10−4 A/cm2 at the bias voltage of −2.5 kV has been obtained. The forward on-state current was 2 A at VF = 1.8 V. The chip is 2.3 mm × 2.3 mm and the active-region is 1.5 mm × 1.5 mm. The turn-on voltage is about 0.9 V. The on-state resistance is 6.52 mΩ ⋅ cm2. The doping and thickness of the N-type drift layer and the device structure have been performed by numerical simulations. The SiC JBS devices have been fabricated and the processes were in detail. The die was packaged with SMB seagull package. The thickness of the N—epilayer is 24 μm, and the doping concentration is 4.0 × 1015 cm−3. A floating guard rings edge termination have been used to improve the effectiveness of the edge termination technique. By using Ti/Ni/Ag multilayer metal structure, the double side Ag process of 4H-SiC JBS diode is formed. We use the PECVD SizNy/SiO2 as the passivation dielectric and a non photosensitive polyamide with 390 °C cured temperature as the passivation in the end. Keywords: 1

4H-SiC; JBS; Schottky; floating guard rings

INTRODUCTION

SiC Schottky-type diodes have the potential to replace Si PiN diodes in applications such as traction or medium voltage drives. Planar SiC JBS diodes offer lower leakage-current levels and surge current capability when compared to similarly rated Schottky diodes.[1] In order to obtain the high reverse voltage with the low leakage current, the development of 4H-SiC SBDs is Junction Barrier Schottky (JBS) diodes. In general, electric field enhancement takes place at the edges of power devices, which leads to a breakdown voltage much lower than the ideal plane parallel

breakdown voltage. Edge termination guaranteeing high breakdown voltage and robustness in its fabrication are required in SiC power devices. The newly VLD (Variation of Lateral Doping) edge termination for 3.3 kV-rated SiC SBDs, which was formed by Al ion implantation using a resist mask having a varying thickness.[2] In the case of high voltage devices, guard ring with edge termination plays an extremely crucial role in determining the breakdown voltage. The guardring structure serves to reduce the amount of field crowding at the main junction by spreading the depletion layer past consecutively lower potential floating junctions. The JBS structure in

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Si is mainly used to lower the recovery transient losses. We have already reported a high-voltage (>2 kV) Ti/4H-SiC SBD fabricated on 12  μm thick 4H-SiC epilayer with B+ implantation edge termination and field plate technology.[3] In addition, 4.5 kV 4H-SiC JBS diodes have been fabricated on n+ type conductivity 4H-SiC substrates. At 7.5  A, the voltage drop is 4.0 V. Breakdown voltage exceeds 6.5 kV corresponding to a protection efficiency of 86%.[4] In this paper, we reported the fabrication and the measurement on electrical characteristics of SiC JBS devices. TCAD simulations have been performed to select the doping concentration and the thickness of the drift layer and the effectiveness of the guard ring termination technique. The epilayer properties of the N-type are 24 μm with a doping of 4.0 × 1015 cm−3. The diodes were fabricated with a 20 floating guard rings edge termination. The diodes can block a reverse voltage of at least 2.5 kV, and the on-state current was 2 A at VF = 1.52 V. The measured leakage current and breakdown voltage are close to the calculated value. 2

DESIGN AND FABRICATION

The SiC JBS devices have been fabricated at Science and Technology on Monolithic Integrated Circuits and Modules Laboratory, using 4H-SiC epitaxial wafers. In the device, the n− 4H-SiC epitaxial layer is grown on the n+ 4H-SiC substrate. The active layer doping level and thickness were the following: Nd = 4.0 × 1015 cm−3, and d = 24 μm, respectively. After cleaning the n type low resistance 4H-SiC wafer, the p+ regions and the p+ guard ring edge termination by using the multiple energy Al+ implantation at room temperature with maximum energy 550  Kev and dose 5.4  ×  1013 cm−2. High temperature ion implantation annealing of the SiC wafer coated with 8 μm thick photo resist and carbonized at 700 °C for 2 h to activate the implanted Al+ dopant was done in an Ar ambient in a graphite furnace at 1850 °C for 3  min. Nickel back-side ohmic contact was evaporated and annealed in N2 atmosphere at 950 °C for 5 min with the SiO2 layer mask on the SiC frontside. The anode Schottky contacts was formed by thermal evaporation of Ti with thickness of about 150  nm, with the subsequent annealing at 400 °C for 10 min. By using Ti/Ni/Ag multilayer metal structure, the double side Ag process of 4H-SiC JBS diode is formed. A non photosensitive polyamide is as the final passivation. The SiC JBS devices with SMB-seagull package are shown in Figure 1.

Figure  1. The schematic diagram of JBS diodes with SMB-seagull package.

3

RESULTS AND DISCUSSIONS

The forward characteristics were tested with a Tektronix 371 curve tracer and the reverse characteristics were tested with an Tektronix 370 curve tracer when the devices are packaged with SMB seagull mode. All tests were performed at room temperature. 4

REVERSE I–V CHARACTERISTICS

Figure  2  shows typical room temperature reverse current-voltage (I–V) characteristics of the 4H-SiC JBS diodes. It can be seen that the guard ring edge termination effectiveness is good. When the breakdown voltage of SiC JBS gets to 2.5 kV, the minimum leakage current is about 6 μA (leakage current density 1.13 × 10−4 A/cm2). The chip1 shows the reverse leakage current increases as the reverse voltage is 1100 V. However, chip2 is better and chip3 is the best. The breakdown voltage of chip2 and chip3 begin from 1550 V and 1800 V, but they all can reach 2600 V. The reason is that the SiC epilayer doping concentration is non-uniform. In the future work, we will choose the suitable distance between the guard rings based on JBS fabricated to improve the edge termination effectiveness and the forward current. 5

FORWARD I–V CHARACTERISTICS

The points in Figure  3  show the forward I–V characteristic of SiC JBS devices in the on-state.

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Figure  2. Reverse I–V characteristics of fabricated 4H-SiC JBS diodes. The reverse leakage current of the chip3 is 6 μA at 2500 V and 37 μA at 2600 V.

Figure 3.

Figure  4. Reverse recovery time of the fabricated 4H-SiC JBS diode is 29 ns.

Figure 5. Wafer map of blocking voltage and forward current yields at 2.5 kV and 2 A.

Forward I–V characteristics of three chips.

The on-state current is 2 A at VF =  1.8 V, the turn-on voltage is about 0.9 V. The chip size is 2.3  mm  ×  2.3  mm. The minimum on-state resistance is 6.52 mΩ ⋅ cm2. When the forward current is 2 A, the voltage of chip1 is 1.798 V, chip2 is 1.818 V and chip3 is 1.804 V. The voltage (forward current 2 A) of chip2 and chip3 is similar to chip1 because the SiC epilayer doping concentration uniformity is good. 2 A/600 V switching tests were carried out on three chips. The measured reverse recovery time of the three chips are similar 29 ns as shown in Figure  4. In silicon, the reverse recovery time of fast recovery schottky diodes is larger than 100 ns. Faster switching is positive, as it will impact on

the switching device in the circuit, reducing total switching losses. However, faster di/dt and dv/dt can increase current/voltage overshoot.[6] The wafer map of the blocking-voltage and forward current yields at 2.5 kV and 2 A measured on the 3-inch SiC wafer under a leakage current threshold of 100 μA. On-wafer measurements below 2.5 kV were conducted, for which the wafers were immersed in Fluorinert to prevent arcing. The blocking and current yield was in excess of 50%, as shown in Figure  5. The devices failed because of killer or process defects. The killer defect may have originated from the downfall during the epitaxial growth. Another device failed because of a surface defect.[5]

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conducting high-voltage and high-current switching test systems. Next step is to optimize the fabrication in order to get a lower reverse leakage and higher forward conduction. Further work will focus on more extensive studies of the reverse recovery waveforms and reverse capability of the fabricated JBS diodes at higher temperature.

ACKNOWLEDGMENT

Figure  6. The reverse leakage current of 4H-SiC JBS diodes when blocking voltage is 2.5 kV.

For the reverse I–V characteristics, a highvoltage measurement setup was used consisting of a probe station, a 3 kV high-voltage supply and a current amplifier. As shown in Figure 6, for almost all the chips the reverse leakage current observed were lower than 50 μA. 6

CONCLUSION

In summary, we have fabricated SiC JBS devices on n+ conductive 4H-SiC substrates. The double side Ag processes and the SMB-seagull package of the SiC JBS were developed and high performance of the SiC JBS device was reported. By employing a high energy ion implantation and high temperature annealing technique, excellent characteristics were obtained. The breakdown voltage improved to more than 2.5 kV depending on device guard ring termination structure. A low reverse leakage current below 1.13  ×  10−4 A/cm2 at the bias voltage of −2.5 kV has been obtained. The forward on-state current was 2  A at VF =  1.8 V at 25 °C and 38A/cm2. The fabricated diodes can be used in

We would like to thank all the members of wide band department and Science and Technology on Monolithic Integrated Circuits and Modules Laboratory. Help received from the silicon devices department is also acknowledged.

REFERENCES [1] H. Bartolf, V. Sundaramoorthy, A. Mihaila, et. al. Study of 4H-SiC Schottky Diode Designs for 3.3 kV Applications. Materials Science Forum Vols. 778–780 (2014) pp 795–799. [2] Kohei Ebihara1, Yasuki Yamamoto, Yoshiyuki Nakaki, et al. Designing and Fabrication of the VLD Edge Termination for 3.3 kV SiC SBD. Materials Science Forum Vols. 778–780 (2014) pp 791–794. [3] Chen Gang, Li Zhe-yang, Bai Song, Ren Chun-jiang. Ti/4H-SiC Schottky barrier diodes with field plate and B+ implantation edge termination technology. Chinese Journal of Semiconductors, Vo1. 28  No. 9, Sep., 2007, pp 1333–1336. [4] Runhua Huang, Gang Chen, Song Bai, et al. Simulation, Fabrication and Characterization of 4500 V 4H-SiC JBS diode. Materials Science Forum Vols. 778–780 (2014) pp 800–803. [5] Dai Okamoto1, a*, Yasunori Tanaka1, Tomonori Mizushima, et al. 13-kV, 20-A 4H-SiC PiN Diodes for Power System Applications. Materials Science Forum Vols. 778–780 (2014) pp 855–858. [6] P.M. Gammon1a, C.A. Fisher, V.A. Shah, et. al. The cryogenic testing and characterisation of SiC diodes. Materials Science Forum Vols. 778–780 (2014) pp 863–866.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Feature-based intelligent machining method based on NX X.H. Zhan & X.D. Li Zhuhai College of Jilin University, Guangdong, China

ABSTRACT: The aim of intelligent machining is identifying the model automatically under a nonhuman-computer interaction environment with a machining program software, and completing all or part of the machining process of the model. NX CAM, the mainstream numerical control programming software released by Siemens, provides the function of feature-based machining. The operation object of feature-based machining is not the model geometrical feature itself, but the machining feature recognised from the model. According to the machining feature, the corresponding operation could be created intelligently. The process of feature-based machining and the method of the machining feature recognized are described in this paper. At the end, one feature-based machining case is cited. Keywords: 1

feature; feature-based machining; machining knowledge editor; CAM

INTRODUCTION

CNC programming is one of the main contents in the NC machining stage, usually including confirming processing technology by model analysis; achieving tool location data after computing tool path; writing NC programs; production control medium; proofread program and try to cut of product. CNC programming is the entire process from drawing or 3D model to the NC program. CNC programming consists of a manual program and an automatic program. Each phase in manual programming is entirely operatedby human hand. This way is relatively simple, suitable for moderately complex procedures, such as point processing (drilling, reaming) or simple geometric shapes (plane face, square groove). The part with the freeform surface and complex cavity along with its tool path data is quite tedious, error-prone, and difficult to proofread. That partcan only be completed through the NC programming. The CNC automatic program simulated 3D model machining on a computer, and created the process operation. In the end, the program created the NC code which can be recognized by a NC machine through postprocess. Currently, the mainstream CNC machining software includes NX, Catia, FeatureCAM, MasterCAM, Cimatron and so on. With the rapid development of digital technology and auxiliary programming techniques, compared to the traditional manual programming, CNC’s automatic program not only improves the efficiency of production, reduceshe number of man-made mistakes, brings in the convenience of

the programmer, it also realises the automation to a certain extent. However, the needs of high precision shape and more complex parts for machining increasing, it highlights the case for the increase in intelligent automation programming software (Zhang et al. 2000; Hang et al. 2009). This paper discusses the intelligent process and implementation method with feature-based machining by Siemens NX software platform. This study covers the combination of the machining case and the application of feature-based machining in intelligent machining field with NX CAM. 2

THE CONCEPT OF INTELLIGENT MACHINING TECHNOLOGY

From feel to memory, from memory to thought, from thought to generating behavior and language, the processes are called intelligence. Intelligence is a goal that needs to be achieved through computer software. The more intelligence-aided machining software is, the greater help it provides to achieve more repetitive mental labour—even some given tasks which the human brain cannot complete. According to the automatic identification of the machining area, the intelligent machining base has definite ‘rules’ to specify how to automatically generate the correct tool path. These ‘rules’ comprise the right process knowledge accumulation of years. Based on the rule library, the automatic programming system identifies and assesses the model continually, filters the rule library and chooses the suitable process, and then completes the machining task. (Yun & Li 2011; Zhang 2008).

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3

4

THE PROCESS OF FEATURE-BASED MACHINING

Figure  1  shows the process of feature-based machining. (Siemens PLM Software 2013). First of all, import 3D model to the NX CAM application environment, which may be created by NX with features, also converted by other software. Different kinds of data have an impact on subsequent machining feature recognition. In the NX CAM application environment, the machining feature of the imported model needsto be recognized. The mentioned features doesn’tt mean the design features of creating model, but the machined feature. The design features, for example blind hole, slot, groove etc., can be recognized by the NX as the machining feature. In the inference engine phase, the recognized machining features were evaluated by the Machining Knowledge Editor (MKE) rules. The rules can automatically select the appropriate tool, reasonable operation mode and parameters, and attained relative ideal tool path. Machining Knowledge Library (MKL) consists of the rules that can decide which features can be recognized, whatt kind of solution should be taken to the machine, what type and size tool should be selected and the parameters to create the operation. MKE provide the tool to define and create processing technology base by MKL for users and enterprises. MKL is the core of FBM which determines the intelligent degree of the process. In the end, it goes the through machining operations, gets the appropriate tool path, and machines the part after postprocess. Designers can create feature—based processes for the following: Milling operation types: Face Milling, Planar Milling, Cavity Milling, Fixed-axis Surface Contouring, Variable-axis Surface Contouring, Z-Level Milling and Variable-axis Z-Level Milling.

Figure 1.

MACHINING FEATURE RECOGNITION

In NX Manufacturing, a machining feature is any shape that is recognized by the software as machinable. Machining features you can select include standard shapes, such as holes, slots, and pockets, and user-defined features created for irregularly shaped areas. The features can be identified, recognized, or tagged (Siemens PLM Software 2013). NX provides users with five kinds of feature recognition methods: feature identification, parametric recognition, legacy hole recognition, legacy face and pocket recognition and manual recognition. In the above methods, feature identification and parametric recognition are more commonly used, and NX recommends that users use the second way of parametric recognition. In the process of feature recognition, the way of parametric recognition doesn’t need to make a decision for specific design feature by users, but recognises automatically, according to the detail parameter of the model. The machining feature similar to the feature in the feature library would be found. 5

CREATE MACHINING FEATURE PROCESS

After feature recognition and performing the ‘Create Feature Process’, we can directly obtain the machining program that we wanted. The process is very simple and does not require looking at too many options. It reflects exactly the intelligenence of FBM technology. 6

ONE FBM CASE

This case shows how FBM realises intelligence in the machining process. Figure  2  shows the blank

Process of feature-based machining.

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Figure 2.

Blank and part.

Figure 3.

Process of find features.

Figure 4.

Create feature process and generate tool path.

and part. Three countersunk holes need to be machined. Step1 Open or import the model, and enter NX Manufacturing. Step 2 Before manufacturing, the parameters of the blank, part and MCS etc. need to defined first. Step 3 Recognized features In machining feature navigator, right click on model node, execute ‘Find Features’ command. In ‘Find Features’ dialog box,

set Type ‘parametric recognition’, “Features to Recognize” default. Click ‘Find Features’, machining feature were recognized automatically. Only three STEP 2 HOLE features remained—all other features were deleted. The process of finding features is shown in Figure 3. Step 4 Create feature process. Choose three countersunk hole machining features in machining feature navigator, execute ‘Create Feature Process’.

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Choose MillDrill node in ‘create feature process’ dialog box. Step 5 Generate tool path, as shown in Figure 4.

(No. 10110208) and the ‘Three Levels’ backbone teacher training program of Zhuhai College of Jilin University.

7

REFERENCES

CONCLUSIONS

Feature-based machining is an advanced application that automates the creation of operations such as spot drilling, drilling, countersinking, counter boring, reaming, tapping, deburring, and milling through the use of intelligent models containing manufacturing features (User Defined Features, User Defined Attributes, and NX Based Features) and embedded machining rules. Feature-based machining greatly simplifies the process of making holes, regardless of the type of application. ACKNOWLEDGEMENTS It is highly appreciated that Siemens PLM software provided the NX software. This work is supported by a grant from the Teaching Quality Project

Zhang Jie. Luo Xin. Du Runsheng & Yang Shuzi. 2000. The Conception of Feature and Its Generation, J. Huazhong Univ. of Sci. & Tech 28(1):95–97. Hang Rui-guo. Zhang Sheng-wen. Jia Wei & Yang Chang-qi. 2009. The Research of Intelligent NC Programming System Based On UG Platform, Group Technology & Production Modernization 26(2):9–12. Yun Zhi-dong & Li Hai-biao. 2011. The application of FBM technology in automatic NC programming, Modular Machine Tool & Automatic Manufacturing Technique 1(1):90–93. Zhang Yingjie. 2008. Modeling technique of machining feature for automatic numeric control part programming, Journal of Xi’An jiaotong university 42(3):281–285. Siemens PLM Software. 2013, Computer Assisted Self Teach for NX8.5. Siemens PLM Software. 2013, NX8.5 Help Library. Siemens PLM Software. 2013, Machining Knowledge Editor Training 9.0.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The application of Analyze Formability-One-Step in progressive die strip design X.D. Li & X.H. Zhan Zhuhai College of Jilin University, Guangdong, China

ABSTRACT: In the process of Multi-station progressive die design, firstly, one has to to design the strip, which is to get intermediate configuration of every processing station and blank, by unfolding product configuration reversely. Many sheet metal parts have free forrm surfaces, and the traditional method unfolding those needs complicated hand computing and drawing, and is also very time consuming. For these reasons, Analyze Formability-One-Step can be an excellent option for unfolding the free form surfaces at the stage of strip design itself. In this paper, the basic idea, hypothesis, and workflow of Analyze Formability-One-Step are stated and discussed. And in the analysis of cases, Analyze Formability-One-Step is used to complete ‘Entire Unform’ and ‘Immediate Unform’ in the process of strip design, with PDW. It is convenient to use Analyze Formability-One-Step to unfold free form surfaces in progressive die strip design. Keywords: 1

progressive die; NX PDW; Analyze Formability-One-Step; strip design; sheet metal 2

INTRODUCTION

Sheet metals are the thin-wall formed parts with same thickness and certain geometric dimensioning, which are widely used in automobiles, ships, machinery, chemicals, aerospace and other fields. Many relatively complicated and small sheet metal parts are produced by progressive die, which can complete all processing of the stamping part in a punching stroke. It is a kind of precise, efficient, high-speed, high quality and long life stamping die (Y.S. Chen 2012). The structure of the Multi-station progressive die is generally complicated, and seriously considering every step is necessary to complete the work well. In Multi-station progressive die designing, confirming conversion process from blank to product i.e, confirming every manufacturing procedure detail of every station is primary. It needs to get intermediate configuration of every processing station and blank by unfolding product configuration reversely (Y.Q. Li & J.C. Xiao. 2012; C.H. Zhou 2007). The traditional unfolding method of sheet metal needs complicated hand computing and drawing, and has a waste of time, especially for the part with free form surfaces (X.D. Li & X.H. Zhan 2011). In this paper, in the stage of strip design, Analyze Formability-One-Step is used to finish the reverse unfolding for the free form surface to get the immediate configuration on the blank.

THE BASIC IDEA AND HYPOTHESIS OF ANALYZE FORMABILITY-ONE-STEP

Analyze Formability-One-Step is a forming analysis tool based on FEM (Finite Element Method). It can quickly unfold the sheet metal part to get plane profile, and it can also unfold part of the product to get the immediate configuration. It is mainly used for free form surface unfolding in progressive die strip designing. The basic idea of Analyze Formability-One-Step is that starting from the product configuration C, which is the end deformation surface of the part, location P0 in the initial flat blank C 0 corresponding to node P is confirmed when the boundary conditions are satisfied by using FEM. Comparing locations of nodes in blank and product, thickness distribution, stress distribution, and strain distribution can be gotten, as shown in Figure  1 (Y.D. Bao. 2004).

Figure  1. FEM sketch map of Analyze FormabilityOne-Step.

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In Analyze Formability-One-Step, there are the following assumptions: 1. Plane stress. 2. Elasto plastic large deformation, volume invariability of materials plastic forming. 3. Proportional loading of forming process, based on plastic theory. 4. The act of stamping dies shows normal pressure of non-uniformity punches, friction under punch and draw bead. 3

THE FLOW OF ANALYZE FORMABILITY-ONE-STEP

Select sheet metal material from ‘Library Materials’, which is provided by Analyze FormabilityOne-Step. Define the thickness of sheet metal part, and define ‘Surface Type’, and there are 3 kinds of surface type to choose from, they are ‘Outer Surface’, ‘Inner Surface’, and ‘Middle surface’. In the end, mesh the surface of sheet metal, and submit data to ‘Solver’. The results of solving are outlines made by a group of spline curves. Results also display ‘Thickness Distribution’, ‘Stress Distribution’, and ‘Strain Distribution’ etc. 4

Figure  2  shows the usage procedure. When dealing with the sheet metal, firstly choose the unform type—its type includes ‘Entire Unform’, ‘Immediate Unform’, and ‘Advanced Unform’. ‘Entire Unform’ can make product configuration plate blank once. To get the immediate configuration and unfold local of the part, ‘Immediate Unform’ can finish the work. In the process of sheet metal unfolding, it can also add some other advanced constraints according the fact with ‘Advanced Unform’, such as holder force, draw bead, spring back etc. After confirming unfolding type, choose ‘Face’ or ‘Solid’ as ‘Object Type’ and choose ‘Unform Region’ and ‘Target Region’ of sheet metal. The target region is the fixed portion of unfolding. In Boundary Conditions, there are 3 kinds of constraints to be chosen, they are ‘Point to Point’, ‘Curve to Curve’, and ‘Curve along Curve’.

ANALYSIS OF CASES

Figure 3 shows a sheet metal with free form surface, produced by progressive die. The contour can be gotten by cutting scraps, and others can be gotten by forming process. It is a non-parametric model. The assembly structure of strip about forming is shown in Figure 4.

Figure 3.

Figure 2.

The flow of Analyze Formability-One-Step.

The sheet metal part.

Figure 4. The assembly structure of strip about forming.

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Figure 5.

‘Immediate Unform’ of Analyze Formability-One-step.

Figure 6.

‘Entire Unform’ of Analyze Formability-One-Step.

Figure 7.

The formability analysis of sheet metal.

4.1

‘Immediate Unform’ of Analyze Formability-One-step

Make ‘Final-2’ work part, extract outside surface of the part and hide the solid body. ‘Analyze Formability-One-Step’ will be used to unfold the extracted sheet. The part’s unform type is set to ‘Intermediate Unform’, and ‘Object Type’ is ‘Face’.

Make the surface shown in Figure  5(a) ‘Unform Region’ and make the surface shown in Figure 5(b) ‘Target Region’. ‘Constraint Type’ is set to ‘Curve to Curve’, and click the intersecting line of target region and unform region, as shown in Figure 5(c). Define the sheet metal’s thickness, and set ‘Surface Type’ as ‘Out Surface’. Define the surface’s mesh

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size, mesh the surface, submit the defined data to the solver, and then get the unform contour line, as shown in Figure  5(d). Another contour line can be gotten with the same method, as shown in Figure 5(e). Figure 5(f) shows the worked immediate configuration. 4.2

‘Entire Unform’ of Analyze Formability-One-Step

Make ‘Final-3’ work part, extract outside surface of the part and hide the solid body. The part’s unform type is set to ‘Entire Unform’, and ‘Object Type’ is ‘Face’, make all surfaces ‘Unform Region’. ‘Constraint Type’ is set to ‘Curve to Curve’, and the line in Figure 6(a) is the constraint line. Define the sheet metal’s thickness, and set ‘Surface Type’ as ‘Out Surface’. Define the surface’s mesh size, mesh the surface, submit the defined data to the solver, and then get the unform contour line, as shown in Figure 6(a). Extrude the contour line to get the sheet metal blank, as shown in Figure 6(b). The calculated results can also predict the sheet metal formability, such as ‘Thickness Distribution’ shown in Figure 7(a), ‘Stress Distribution’ shown in Figure 7(b), ‘Strain Distribution’ shown in Figure  7(c). The end result of strip design about forming is shown in Figure 8. 4.3

Analyze Formability-One-Step—Unform to designated surface

The designed strip result about forming.

Figure 9.

‘Entire Unform’ to designated surface.

CONCLUSIONS

The paper discusses the application of Analyze Formability-One-Step in strip design at the beginning of progressive die designing. Analyze Formability-One-Step is an utility unfolding tool, which can unfold sheet metal parts, especially those parts with free form surfaces. It can not only unfold whole surfaces of sheet metals to get the blank, but also unfold part of surface to get immediate configuration. The computed results can also show the formability analysis of sheet metal, such as thickness distribution, stress distribution, strain distribution, and spring back etc. In progressive die designing, Analyze Formability-One-Step is a valid, timesaving tool to help designer finish strip design. ACKNOWLEDGEMENTS It is highly appreciated that Siemens PLM software provided the NX software. This work is supported by a grant from the Teaching Quality Project (No. 10110208) and the ‘Three Levels’ backbone teacher training program of Zhuhai College of Jilin University. REFERENCES

It can unform form face to designated surface with Analyze Formability-One-Step. The method is the same as the above-described, and the unform result is shown in Figure 9.

Figure 8.

5

Chen Y.S. 2012. Multi-station Progressive Die Design Manual. Chemical Industry Press: China. Li Y.Q. & J.C. Xiao. 2012. Progressive Die Design Technology Application and Instances. Publishing House of Electronics Industry: China. Bao Y.D. 2004. Research on One Step Inverse Forming FEM and Crash Simulation of Auto Body Part: 14. Ph.D. Jilin University: China. Zhou C.H. 2007. UG NX4 Progressive Die Design examples-From Novice to Professional. Chemical Industry Press: China. Li X.D. & X.H. Zhan. 2011. Study on the rapid progressive die design of sheet metal with free form surface. Advanced Materials Research. 328–330:828–831.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The research on the signal reconstruction of the angular rate sensor L.Y. Yuan, W.G. Zhang & X.X. Liu College of Automation, Northwestern Polytechnical University, Xi’an, China

ABSTRACT: In order to improve the fault tolerance ability of the critical sensor system and avoid the extra cost, weight and maintenance introduced by hardware redundancy, the signal reconstruction method was designed for the pitch angular rate sensor. The signal reconstruction was implemented by the Kalman filter with the adaptive current statistical model. The modified model can approximate the angular motion of the aircraft accurately and increase the precision of the analytic signal. The design scheme was verified by simulation under diverse flight conditions and compared with the typical methods. The simulation results demonstrate the effectiveness and superiority of the proposed method. Keywords: pitch angular rate sensor; fault-tolerance design; analytic signal; signal reconstruction; current statistical model 1

INTRODUCTION

The feedback of the angular rate signal constitutes the damper loop in the flight control system and influences the stability of the aircraft directly. Especially, the pitch rate signal is the critical factor to guarantee the flight safety at takeoff and landing. Therefore, it is of great research significance to employ fault-tolerant design to maintain the operational function under failure conditions. Signal reconstruction, analytic signal estimation namely, is the fundament of the fault tolerance. The common methods of signal reconstruction include the observer method, the numerical difference method and the tracking differentiator method et al. However, multiple uncertainties exist in the kinetics equations of the aircraft. The attitude sensor has low the accuracy and high sampling frequency. The above situations limit the application of the common methods. The small perturbation equations adopted by the observer method are not rational in all parts of the flight envelop. And the aerodynamic derivatives are not sufficiently accurate. The numerical difference method will amplify the measurement noise. The measurement noise will affect the output differential signal of the tracking differentiator as well, although the output differential signal is of better quality compared to the numerical difference signal. The introduction of the low-pass filter will attenuate the noise efficiently, but cause additional delay, which degrades the primary signal in the real-time application. With the discussion above, a signal reconstruction method was proposed for the pitch rate sensor.

The concept borrowed from tracking theory was used. The aircraft was treated as a noncooperative target. And the Kalman filter based on the adaptive current statistical model (denoted as ADCSMKalman) was designed to track the angular motion of the aircraft. 2

SIGNAL RECONSTRUCTION DESIGN

2.1

Frame of signal reconstruction design

The frame of the signal reconstruction design is depicted in Figure 1. q

(θ r ssiin φ ))/co / sφ

(1)

The measurement signal of the pitch angle θ is input into the Kalman filter based on the adaptive current statistic model. The signal of the pitch angular velocity θ is obtained. According to the relationship between the ground coordinate system and the airframe coordinate, shown as equation (1), the signal reconstruction is implemented. 2.2

Typical CSM-Kalman filter design

The current statistic model of the aircraft angular motion is X (k

) = Φ( k )X ( k ) U (k ( k )a + W ( k )

(2)

where X ( k ) = [θ θ θ]T consists of pitch angle, pitch angle velocity and pitch angle acceleration.

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The formula of the angular acceleration variance is:

Figure 1.

⎧ 4 −π 2 ⎪⎪ π (amax − a ) 2 σa = ⎨ 2 ⎪ 4 − π (a − max − a ) ⎪⎩ π

Frame of signal reconstruction.

Φ(k), U(k) are state transition matrix and input matrix respectively. a is the mean value of the current angular acceleration. W(k) is the discrete noise sequence with 0 as the mean value and Q(k) as the variance. ⎡1 ⎢ Φ( ) = ⎢0 ⎢⎣0

( −1+ 1 α T + −α T ) α 2 ⎤ ⎥ (1 − e −αT ) α ⎥ ⎥⎦ e −αT

1 0

(3)

⎡1 ⎛ α T 2 1 e −α T ⎞ ⎤ + ⎢ ⎜ −T + ⎟⎥ ⎝ 2 α ⎠⎥ ⎢α ⎢ ⎥ 1 − e −α T U (k ) = ⎢ ⎥ T− α ⎢ ⎥ ⎢ ⎥ −α T 1 − e ⎣ ⎦

(4)

Q( k ) = 2ασ a2 p

(5)

In the expression of Φ(k), U(k) and Q(k), T is the sampling period, α is the maneuvering frequency, p is the constant matrix related to α and T. With (2) ∼ (5), the state equation is established. The measurement equation is: Y ( k ) = H ( k )X ( k ) + v( k )

(6)

where H ( k ) = [1 0 0 ]T is the measurement matrix; v(k) is the Gaussian white noise with R(k) as the variance. When one-step estimation of the angular acceleration θ( + 1 ) is treated as the current value a , the typical CSM-Kalman process is indicated as follows: Xˆ ( k + 1 k ) = Φ( k )Xˆ ( k k ) + U ( k + 1)a ( k + 1) P(k

k)

( k )P ( k k )

K (k

) = P(k

T

T

k )H (k (k

(7)

( k ) + Q( k )

(10)

η ( k + 1) = Z ( k + 1) − H ( k + 1)Xˆ ( k + 1 k )

(11)

K ( k + 1)H )H ( k + 1)]P ( k

k ) (12)

k

) [I

Modified adaptive model

In the design of the CSM-Kalman filter, the parameters α, amax and a−max are pre-defined based on the experience. The model is not adaptive to the variation of the aircraft angular motion and results in the large the estimation error. The online adjustment of the maneuvering frequency α will cause the instability and divergence of the tracking filter. According to the characteristics of the angular motion of the aircraft, the modified model is proposed based on the adaptive adjustment of the angular acceleration. The effect of the amax and a−max on the filter performance is analyzed first. When the absolute values of the amax and a−max take smaller values, the variance of the system noise is small. The tracking accuracy of the non-maneuvering and weak maneuvering is high while the response rate of strong maneuvering is relatively slow. When the absolute values of the amax and a−max take larger values, the variance of the system noise is large. The response rate of the filter for strong maneuvering is fast while the tracking accuracy of the non-maneuvering or weak maneuvering is low. Therefore, the constant value in the CSM-Kalman design cannot cover the various states of the angular motion. The analytical relationship between the pitch rate and the normal overload can be concluded from the kinematics equations. The variation of the overload reflects the change of the aircraft angular motion, shown in (14):

)

Xˆ ( k + 1 k + 1) = Xˆ ( k + 1 k ) + K ( k + 1)η ( k + 1)

P(k

2.3

Δq (9)

(13) a <0

where amax and a−max are the maximum value of the positive and negative angular acceleration.

(8)

) R( k + 1)]−1

[ H ( k + 1)P )P ( k + 1 k )H T ( k )P

a ≥0

g ( s Zα* )Δnz VZα*

(14)

The kinematics equation of the overload is: w b − g cos θ cos φ + pvb − qub

nz

(15)

Under the non-maneuvering condition, nz0

g cos θ cos φ

(16)

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When the angular motion changes from the non-maneuvering state to the maneuvering state, Δq ≈

g ( s + Zα* )( nz − nz 0 ) VZα*

(17) RMSE ( k ) =

From (14) to (17), the adaptive adjustment equation of the angular acceleration variance is:

σ a2

⎧ 4 −π 2 ⎪⎪ π (amax a ) ⋅ μ =⎨ ⎪ 4 − π (a a )2 ⋅ μ − max ⎪⎩ π

μ

The RMSE (Root Mean Square Error) and MRMSE (Mean Value of RMSE) were selected as the evaluation index.

a ≥0 (18) a <0

λ − exp( − λ )

λ = k1 ⋅ nz + k2 ⋅ ( nz + g cos θˆ cos φ )

(19) (20)

where k1 > 0, k2 > 0 are constant. When the aircraft angular motion is under the non-maneuvering condition, nz = 0

(21)

nz + g cos θˆ cos φ ≈ 0

(22)

λ → 0, μ → 0

(23)

MRMSE S =

1 M

1 N ∑ [ x j ( k ) − xˆ j ( k )]2 N j =1

(24)

M

∑ RMSE ( k )

(25)

k =1

N is the number of the Monte Carlo simulation. xj(k) and xˆ j ( ) are true value and estimation value of the kth step in the jth simulation respectively. M is the step number of each simulation. RMSE(k) is the root mean square error of the kth step sampling value. In order to analyze the filter performance under different maneuvering conditions, the nonmaneuvering condition from 0 to 50 s and maneuvering condition from 50 to 55 s were selected as the contrast basis as shown in Figures 2 and 3. The accuracy of the signal estimated by the Filter-TD method is the lowest. The noise level of the input signal is relative high, which decreases the filter performance of the Filter-TD method. The

The filter tracks the angular motion with relative small variance in order to improve the estimation accuracy. When the aircraft enters the maneuvering state, μ λ , the variance of the system noise becomes larger, so the tracking capacity of the filter is improved.

3

SIMULATION AND ANALYSIS

The ADCSM-Kalman, CSM-Kalman and the tracking differentiator combined with two-order low-pass filter (noted as Filter-TD) proposed in [4] were compared and analyzed through Monte Carlo simulation. The simulation conditions were set as follows: the simulation time t  =  100  s; from 0 to 50  s, the aircraft is under level flight condition; from 50 to 70 s, the aircraft is under maneuvering condition; the random noise with the maximum magnitude of 0.2 is added to the measurement signal of the attitude angle. The sampling period T = 20 ms; the simulation number is 100. The parameters of the algorithms were set as follows: the common parameters of CSM-Kalman and ADCSM-Kalman amax  =  6°/s2, a−max  =  −6°/s2, α = 0.01. The parameters of the adaptive angular acceleration model k1 = −5, k2 = 0.3.

Figure  2. condition.

RMSE curves of the non-maneuvering

Figure 3.

RMSE curves of the maneuvering condition.

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Table  1. Comparison of the MRMSE of the reconstruction methods. Nonmaneuvering

Maneuvering

MRMSE

°/s

°/s

Filter-TD CSM-Kalman ADCSM-Kalman

0.1510 0.0890 0.0737

0.2964 0.2093 0.1866

noise attenuation and tracking delay introduced by the low-pass filter should be compromised. The actual effect of accuracy improvement was limited. The two methods based on the target tracking concept, treat the signal noise as the random process. The description was relative precise. And the estimation accurate was improved obviously. The result of the comparison between the CSM-Kalman and ADCSM-Kalman method were depicted in Table  1. Estimated signal of higher precision can be gained by the latter method. The ADCSM-Kalman method can adaptively adjust the angular acceleration according to the practical angular motion, which results in the better performance under both non-maneuvering and maneuvering conditions relative to the typical CSM-Kalman method.

4

CONCLUSIONS

The signal construction method was proposed for the critical sensor, the angular rate sensor in the flight control system. The problem of the high noise level in the attitude signal was settled based on the target tracking concept. The accuracy of the tracking model was improved by the application of the analytical relationships between flight parameters. The proposed method is easy to implement and has potential engineering application value. REFERENCES Li, W.Q. & Chen, Z.J. 2004. Signal reconfiguration method for aircraft’s pitch angular rate. Flight Dynamics 22(2): 26–29. Xia, J. & Xu, J.J. 2013. Observer-based sensor fault detection and signal reconstruction method. Journal of Beijing University of Aeronautics and Astronautics 39(11): 1529–1535. Chi, C.Z. et  al. 2012. Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System. Chinese Journal of Aeronautics, 25(2): 236–242. Hou, M.M. 2006. A modified nonlinear estimator of LOS-angle acceleration with LOS-rate measurement only. Aerospace Shanghai 23(5): 12–15. Huang, W.P. et al. 2011. A nonlinear maneuver-tracking algorithm based on modified current statistical model. Control Theory & Applications 28(12): 1723–1728.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on the application of Beidou and GPS dual-mode timing system in the space flight tracking ship F. Zhang, Y.B. Ren & Y. Zhou China Satellite Maritime Tracking and Control Department, Jiangyin, China

ABSTRACT: The tracking ship is the major component of the space instrumentation and command network, and the time synchronization is very important to the tracking ship. There are some security risks, because the GPS timing system is mainly used in the tracking ship at present. Specific to the question, a timing system based on Beidou (BDS) and GPS was submitted. First, some common time service methods were introduced briefly. Secondly, the dual-mode timing system was probably designed. After that, error analysis of the timing precision was discussed. Finally, some experiments were carried out. The results show that the project submitted in the paper is necessary and feasible. Keywords: 1

GPS; GNSS; timing system; reliability

INTRODUCTION

Time synchronization is that time in a different place has the equal value at the same time. It is very important to our life and national economy, especially to national defense[1]. With rapid development of the social productive forces and science technology, the time service is more and more widely applied. Certainly, the accuracy requirement of a timing system is becoming higher and higher in some specific fields. Especially, the accuracy requirement of a reach up to a microsecond, even a nanosecond in rocket launching, satellite tracking and other fields. The GPS timing system with its high accuracy and good stability is mainly used in the tracking ship. GPS is the new generation satellite navigation system, which can provides many serves including navigation, fixing position, issuing the official calendar, and so on. Because it is researched and produced by the United States Military, there are some security risks to the timing system under the influence of ideology and political factors. In order to meet the needs of the national economy and defense construction of our country, Beidou (BDS) with independent intellectual property rights needs to urgently replace GPS. Keeping this in view a timing system based on BDS and GPS was submitted. 2

TIMING WAYS INTRO

Up to now, there are five timing ways: HF wave timing, Long wave timing, Internet timing, SDH network timing and Satellite timing[2].

HF wave timing was the earliest to be introduced by by the Americans. It transmits time information and standard signals by making use of the HF wave. The transmission path of the HF wave is between the grand and the ionized layer, and then, the transmission quality is influenced by the ionized layer. When the ionized layer is unstable, the influence is bigger, and the carrier frequency needed is higher, the synchronization precision falls short of the millisecond. Long wave timing overcomes the shortcomings of HF wave. It transmits time information and standard signals by making use of the Long wave. Compared to the HF wave, the covering power of the Long wave is stronger, and its synchronization precision measures to the microsecond. With the popularisation of the computer and Internet, people calibrate the time by Internet timing. This timing way uses the telephone line or optical fiber for the transmission medium with modem, and people use client software to calibrate time in the computer. But the synchronization precision is low up to the millisecond. SDH network timing implants the time information synchronized with Cs atomic clock to the SDH net. The precision is more accurate than satellite timing in some sense. But if the multiplex section overhead of SDH net is cut off, the time information would be missed. Satellite timing is the mainstream way in the information society. GPS is playing the leading role, and its precision reaches up to the nanosecond, because every satellite is equipped with an atomic clock. In recent years, the Beidou navigation satellite system has developed rapidly, and the

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stage-one BDS has finished networking, and the signal has covered the Asian-Pacific region[3]. At the same time, the time service product based on BDS is being worked on. 3

GENERAL DESIGN OF DUAL-MODE TIMING SYSTEM

As shown in Figure  1, the system has a modular design, composed of antenna element, power divider, receiving module, data processing module, human-computer interaction module, time keeping module and interface module[4]-[7]. Every module has its own function. Antenna element: using dual-mode antenna, and receiving GPS & BDS satellite signals at the same time. Power divider: amplifying signals, and sending them to the GPS & BDS receiving module. BDS receiving module: receiving, amplifying, filtering, down-converting, demodulating BDS satellite signal, and outputting 1PPS signal and time information including year, month, day, hour, minute and second. GPS receiving module: receiving, amplifying, filtering, down-converting, demodulating GPS satellite signal, and outputting 1PPS signal and time information, as the backup of BDS receiving module. Data processing module: coordinating work of the system, and outputting time information and 1PPS signal on the basis of mode selection of humancomputer interaction module and system state. Human-computer interaction module: inputting the mode selection, and displaying time information. Time keeping module: using local sync source, and generating 1PPS signal synchronized with BDS or GPS.

Figure 1. system.

Interface module: converting electrical level, and outputting 1PPS signal and IRIG-B signal. In the system, BDS and GPS stand by for each other. It is BDS-based and is supplemented by GPS. The BDS timing mode will be started, as long as the BDS satellite signal is effective, in order to overcome the negative impact of GPS. When the BDS signal is lost, the GPS timing mode will be started if it is effective. If both BDS and GPS are lost at the same time, the time keeping mode would be started. 4 4.1

ERROR ANALYSIS Error sources of satellite navigation

As shown in Figure  2, error sources of satellite navigation include error of satellite, propagation error of signal and error of receiver [8]. 1. Errors of satellite include error of satellite clock, relativistic effect and so on. 2. Propagation errors of signal include delay error of ionized layer, delay error of troposphere, and multipath effect. 3. Errors of receiver include clock error, electron error, antenna phase center error, and delay error of receiver. In practical work, we can correct error by some measures, as follows: 1. Weakening clock error of satellite by using differential technology or binomial expression. 2. Reducing delay error of ionized layer by the correcting model of ionized layer in the navigation message.

The general structure drawing of the timing Figure 2. The schematic of the satellite navigation errors.

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3. Reducing delay error of troposphere by using Hopfield and Black mode. 4. Reducing multipath effect by spatial characteristic of antenna pattern or setting restrains board under the antenna. 5. Avoiding unnecessary man-made operating miss, and equipping redundancy receiver.

and troposphere, τr is the time delay taken in by receiver antenna, antenna cable and receiver. R/c is the fake time difference and can be measured. τR, τi and τt can be computed with data supplied in the navigation message of GNSS. τr can be gained by calibrating the receiver. We can compute Δt. 4.3

4.2

Error analysis of timing

High precision locating, velocity measurement and navigation can be realized by the satellite navigation system, because it is based on sophisticated time measurement. In general, after observing four satellites, the navigation system can minutely ensure the coordinates and speed of the receiver antenna location and the time [1][8]. As shown in Figure 3, tGNSS is the GNSS time, tU is the time of user clock, tSV is the time of satellite clock, tU’ is the user time GNSS signal arrived on receiver, R is the pseudorange, c is the speed of light. Δt is the real time difference of user time and the GNSS time as well as (1). Δt

tGNSS − tU

Experimental result

We designed the system with BD-126 development board, which is shown as Figure 4. As shown in Figure  5 and Figure  6, we compared the 1PPS signal of the dual-mode system with the 1PPS signal of GPS we are using.

(1)

At the same time, we know Δt is as well as (2). Δt

R c

ΔtSV − τ Σ

(2) Figure 4.

The picture of BD-126 development board.

Including:

τΣ = τR + τi + τ t + τr

(3)

As shown in (2) and (3), ΔtSV is time difference of the satellite clock time and the GNSS time, τΣ is the total time delay. τR is time delay of distance, τi and τt is the time delay taken in by ionized layer Figure 5. The general diagram of 1PPS signal comparison.

Figure 3. The schematic of the GNSS time measurement.

Figure 6. The detail diagram of 1PPS signal comparison.

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In Figure  5, the 1PPS signal cycle of the two systems are 1  s, and the leading edges of second stay the same, but the pulse width is different; the pulse width of GPS is 1 ms, while the pulse width of the dual-mode system is 100 ms. Satellite timing is strict with the leading edge to the second, rather than the pulse width, so the change of pulse width doesn’t affect the timing result. In Figure 6, the rising edge width of 1PPS signal of GPS is 80 ns, while the width of the dual-mode system is only 10 ns, which is far and away superior to GPS. The leading edge of the dual-mode system is younger than GPS’s as 1.25 μs. The result is connected with the precision of system. The timing precision of the dual-mode system is 15 ns, while the precision of GPS is 1 μs, because its version is very old. So that, there is any deviation between them. That said, we cannot measure the relative time delay of the system, because we do not have some equipment. 5

CONCLUSION

A dual-mode timing system based on BDS and GPS is molded in the paper, which is designed on the basis of BD-126. The system can output 1PPS signal, locating information and time information in real time. Compared to the GPS we are using, we find that the dual-mode timing system can be certainly be used as the time source of the tracking ship.

REFERENCES [1] Bin Guo. A Study of Electric Power System Time Synchronization Technology Based on Bei-dou and GPS Dual-mode Timing Service [D]. Changsha: Hunan University, 2010. [2] Gui-Jun Chen. Study of Beidou and GPS timing system [D]. Shenyang: Shenyang University of Technology, 2011. [3] Shi-Jun Ying, Wang Kun, Liu Wei, et al. Precision Analysis of BD-2 Marine Receiver [J]. Navigation of China, 2013, 36(1): 24–27. [4] Zhen-Peng Xing. Application of Timing System in Jiaozuo Power Plant Based on Beidou and GPS[J]. Electric Power IT, 2009, 7(7): 103–105. [5] Jian-Hai Li, Da-Jiang Yi, Hao Wang. Design and Realization of Integrated Processing Module of Timing/Passive Location Based on Beidou/GPS. [J]. Computer Knowledge and Technology, 2009, 5(9): 2423–2425. [6] Meng-Yuan Chen, You-Zhu Ling, Guan-Ling Wang. Broadcast Television Time service Unit Using Mutually Backup Synchronization Signals from Beidou Satellite Navigation and Global Positioning System [J]. TV center, 2010, 34(6): 60–63. [7] Yan-Peng Sun, Ying-Shuo Zhang, Er-Shen Wang, et al. Design and Positioning Algorithm of BD-2/ GPS Combined System [J]. Electronic Design Engineering, 2011, 19(23): 74–77. [8] Hai-Tao Wu, Xiao-Hui Li, Yu Hua, et al. Time Basis of Satellite Navigation System [M]. Beijing: Science Press, 2011.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study of the wind-thermal allocation ratio for wind and thermal bundled power as a source to participate in power planning X.M. Cao, T.Q. Liu & X.T. Hu School of Electric Engineering and Information, Sichuan University, Chengdu, China

Z.H. Chen, F.J. Wang & T.Y. Guan Gansu Provincial Power Company, Gansu, Lanzhou, Sichuan Province, China

ABSTRACT: Wind power possesses intermittent and fluctuant features, resulting in the fact that the integration of wind power is not only a power network programming problem, but also needs the enhancement of the peak regulation ability. How to make wind power participate in it is the prime problem in power planning. We use both wind power and thermal power as a source to participate in the traditional power planning. By analyzing the 8760 hours’ wind power output data and five typical days load data of one province, a new wind-thermal model including the restraints of the output and the ramp rate of the thermal power unit was built. Using the Particle Swarm Optimization (PSO) algorithm, the optimal correlation coefficient for different wind-thermal power allocation ratios was found, and then the optimal wind-thermal power allocation ratio was worked out. In addition, the feasibility and practicability of this method was confirmed by calculating the practical data of a certain province. Keywords: 1

wind-thermal bundled; power planning; wind-thermal collocate ratio

INTRODUCTION

In recent years, renewable energy is developing rapidly in China and wind power, as the current generation technology, plays an increasingly important role in the strategic restructuring of China’s energy and power development. Since the output characteristics of wind power is different from conventional hydropower and thermal power (which is controlled by an external output of wind energy), its impact cannot be ignored. This includes the conventional power scheduler operation mode and the selection of spare capacity. Large-scale wind power integration puts forward new demands on our power supply planning theory [1]. Some scholars have studied Power Planning after large-scale wind power was integrated: Reference [2] studied the optimal expansion planning problem using the dynamic programming algorithm for the planning period, for the limited size of the grid system by wind farms and photovoltaic cells composed of N years, keeping in view the meteorological data, the characteristics of historical load data, wind farms and photovoltaic cells, energy costs and other factors. Reference [3,4] assessed the impact of investing in wind power to power planning by Wind turbines

processed into a multi-state unit. Reference [5] established the wind power generation system’s optimal planning model to the objective function of the largest wind power output in case thewind power can be fully absorbed by the grid. Reference [6] re-examined the issue of the wind power generation system planning from the market point of view, and proposed two planning methods, including the objective function of the largest unit cost and unit mounting area to contribute the maximum, and obtain a reliability index such as various types of power generation units, the entire system fuel costs, lack of electricity power shortage probability expectations and reliability. However, these studies design power schemes by only considering wind power output characteristics, and give little consideration to the complementary characteristics of wind power and conventional power. The article proposed a method of Wind-thermal Power ‘bundling’ participation in power balance (based on the correlation between wind power and load calculation analysis) and put forward the power allocation ratio optimization method, and made its calculations and and analysis on an actual provincial grid data. The results show that the method for the actual power supply planning has certain reference value.

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2

CORRELATION OF WIND POWER OUTPUT AND LOAD

Correlation of wind power output and load under the correlation coefficient concept of mathematical statistics was studied. Specific expression of the correlation coefficient is as follows n

l=

∑(

w

−

w )( L

−

L)

t =1

n

∑( t =1

w

−

(1)

n

w)

2

∑(

L

−

L)

2

t =1

where, pw is wind power output of each period; pw is the average output of wind turbines in the study period; pL is the load of each period; pL is the average load study period. l is at [−1,1], when it is negative, indicating that the wind power and the load has a negative correlation; and a positive value indicates a positive correlation between the load; and the greater its value indicating the larger correlation. This article analysed the wind power output historical data of a province 365 days, and the load, selected from five typical operation modes which included the early part of the year, a long summer, a short summer and a long winter as well as a shortwinter. Relevance indicators of 24 hours a day by statistical analysis was noted. Figure  1 is the trend graph of wind power output and typical daily load variation on the day of the maximum correlation between wind and load, and Figure 2 is the trend graph of wind power output and typical daily load variation on the day of minimum correlation between wind and load. After calculating correlation between load and wind power with five typical operating modes include the early part of the year, short winter, long summer, short summer and long winter, it shows that the number of days with positive correlation are

Figure 2. Wind power output and daily load on the day of minimum correlation between wind and load.

168 days, 160 days, 150 days, 155 days, 152 days— and inversely related to the number of days was 196  days, 205  days, 215  days, 210  days, 213  days. These results demonstrated that the number of days with a positive correlation were less than the number of days of negative correlation—indicating that the correlation between wind power contribution and the load is notgreat. The theoretical support of wind power participating in power balance is not strong. This paper proposes the use of wind, thermal, complementary as “playing bundle power” participate in power supply planning to participate in the planning for wind power provides a possibility. 3 3.1

MATHEMATICAL MODELS Objective function n

max l =

∑(

n

) L n )(

L)

t =1

n

∑( t =1

(2)

n

n

n)

2

∑(

L

L)

2

t =1

where, pn is the planning total output power of wind and thermal bundling mode; pn is the planning average output power of wind and thermal bundling mode; pL is the planning load to consume wind power; pL is the average planning load to consume wind power; n is the time area under study. 3.2

Figure 1. Wind power output and daily load on the day of maximum correlation between wind and load.

Constraint conditions

System constraints include upper and lower output constraints of units, minimum time constraint of turning on and shutting off generators, the ramp rate constraints of units and so on. Considering that conventional generators and the wind turbine output can not exceed the upper

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limit, so the constraint in equation (3), (4) as follows Pk ≤ P max

(3)

max 0 ≤ Pwind i k ≤ Pwind

(4)

P min

min

T

on Tmi n

off f

(5)

off Tmi n

(6)

where, T on, Toff expressed the operation and shutoff f on down time of unit i respectively; Tmi n , Tmin expressed the minimum turning on time and minimum shutting down time of the unit i. The unit ramp rate constraints are − PD ≤ Pk − P( k − ) ≤ PU

(7)

where, PU, PD expressed upper limit of the unit ramp rate and lower limit of unit ramp rate respectively. 4 4.1

WIND-THERMAL ALLOCATION RATIO OPTIMISATION METHOD Particle Swarm Optimisation method

Particle Swarm Optimisation from Complex Adaptive System, which Proposed by Kennedy et  al 1995 [7], is carried out in accordance with the laws of movement and gather birds foraging during the resulting search algorithm simulation. In the particle swarm algorithm, solutions of the questions are represented by the position of particles, of which positions fitness good or bad is determined by an objective function to evaluate. Group consists of particles composed of n-dimensional space to search for Q under a Q-dimensional search area, which the i-th particle is represented as xi

(x xi1, xi

xii

xiQ ), ) i = 1, 2, …, m.

(8)

The corresponding particle flight speed can be expressed as vi

(vvi1, vi

Pg

( Pg1, Pg

PggQ 1, 2, …, m. Q ), i = 1,

(10)

max

is upper and lower output of where, Pt , Pt thermal power units; Pk is thermal power output at K time; Pwiind k is wind power output at K time; Pwimax ind is Wind power output ceiling. Taking into account the economics of the plant operation, the turning on and shutting off constraints of thermal power units is as in equation (5), (6): T on

Particles track individual an optimum solution and global optimum solution within the selected area to search. The individual optimum solution is recorded as Pi ( Pi1, Pi PiQ ) , i 1, 2, …, m. Global optimum solution is denoted as

vii

viQ ), ) i = 1, 2, …, m

(9)

Particles update their positions and velocities according to the following general formula vidk+1 xidk+1

ωvidk + c1ξ ( piidkd

k xidk ) + c2η ( pggd d

xidk )

k 1 xidk + rvidk+

(11)

where, ω is the inertia weight; c1 is the optimal weight coefficients tracking particle individual optimal solution history, usually a value of 2; c2 is the optimum weighting factor tracking global optimal solution history, usually a value of 2; , η is a uniformly distributed random number within the range 0–1; r is the constraint factor of speed update, usually a value of 1. Equation (11) is divided into three parts, the first part is the inertial behavior of particles; the second part reflects the cognitive behavioral particles; third part represents the social behavior of particles, representing particles in the process of learning community experience. Particle swarm optimisation calculation process is as follows: 1. Randomly generate the initial position of each particle, then calculate the fitness value of each particle, which will take the minimum as the global optimal solution. 2. For each particle, its fitness value is compared with the individual optimal solution. If the fitness is excellent, the location of the particle at this location is taken as the current best. 3. For each particle, its fitness and global extreme is compared. If the fitness is excellent, the position of the particle is taken as the global optimal location of the current population. 4. Update the position and velocity of the particles. 5. Determine whether the end conditions is met or not. If satisfied, output the globally optimal solution, if not met, continue iteration. 4.2

Wind-Thermal optimal allocation ratio calculation based on particle swarm optimization algorithm

The optimal allocation ratio of wind and thermal based on the particle swarm optimisation algorithm calculation steps are as follows 1. Calculation of 365 days and 24 hours daily correlation of wind power and the typical daily load; to sort;

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2. The first 300  days to find out the correlation of the date of wind power and the typical daily load; read the corresponding wind power output data and load data; 3. Using the maximum wind power output capacity as wind turbine integrated in wind power capacity and the capacity of thermal power and constant change; Wind-Thermal electricity allocation ratio; 4. Particle swarm optimisation of the thermal power; Wind & Fire, 24 hours under the output power allocation ratio to optimise the results of its total output and load the optimal correlation; 5. Changing the proportion of wind, thermal, electrical configuration; repeat steps 4 to obtain the optimal relevance; 6. Get the optimal correlation with changes of wind and thermal power allocation ratio; find the optimal allocation ratio. 5

Figure  4. Correlation coefficient and wind-thermal allocation ratio on the correlation day of 80%.

CALCULATIONS AND ANALYSIS

The wind power output data of a wind farm during 8760 hours and load data, for five typical day of a province, was used to verify the above-mentioned method of the wind-thermal ratio configuration method. The correlation between wind-thermal bundled power output and load changed with wind-thermal power allocation ratio. The results are shown in Figure  3, 4, 5. Figure  3  shows the correlation between wind-thermal bundled power output and load changed with wind-thermal power allocation ratio on the day of minimum correlation between wind power and load. From Figure 3, we can see in the correlation for early winter with typical little loads, that three times the thermal power in wind power was needed to make the correlation between

Figure  3. Correlation coefficient and wind-thermal allocation ratio on the minimum correlation day.

Figure  5. Correlation coefficient and wind-thermal allocation ratio on the biggest correlation day.

wind-thermal bundled power output and load reached 1. The correlation between wind-thermal bundled power output and load changed with wind-thermal power allocation ratio under a day with 80% of correlation between wind and load is as shown in Figure 4. We can see configured thermal power of 2.1 times the wind energy can make the correlation between wind-thermal bundled power output and maximized load. Figure  5  shows the correlation between wind-thermal bundled power output and load changed with wind-thermal power allocation ratio on the day of maximum correlation between wind power and load. In this case, we can see configured how thermal power of 1.7 times the wind energy can make the correlation between wind-thermal bundled power output and maximum load. Because of the wind-thermal power is bundling as a power source for power planning, so the correlation between wind-thermal bundled power

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output and maximum load is not optimal. We allowed the correlation to reach 80% to meet the system’s operation. Figure  4  shows that with the configured thermal power of 1.8 times the wind energy meets the system requirements to run. But this conclusion is only suitable for examples in this article—the specific allocation ratio must be drawn after consideringlocal characteristics of wind power and absorptive wind load. 6

CONCLUSIONS

The wind-thermal allocation ratio optimisation model was established based on analysis of the correlation between the wind power and load analysis, consideringthermal power output constraints, ramp rate constraints, etc. The solution for the optimisation model in specific examples was explored by using particle swarm optimization algorithm, reached wind-fire power optimal allocation ratioand verified applicability of the method. ACKNOWLEDGEMENT This article is created with the support of Gansu Province Electric Power Company funded project (5227201350PM).

REFERENCES [1] Zhang Jie-tan, Cheng Hao-zhong, Huang Wei, et al. Review of Generation Expansion Planning for Power System with Wind Farms [J]. Proceedings of the CSU-EPSA, 2009, 21(2):35–41. [2] Farghal S.A, Abdel Aziz M.R. Generation expansion planning including the renewable energy sources [J]. IEEE Transactions on Power Systems, 1988, 3(3): 816–822. [3] Schenk K.F, Chan S. Incorporation and impact of a wind energy conversion system in generation expansion planning [J]. IEEE Transactions on Power Apparatus and Systems, 1981, PAS-100(12): 4710–4718. [4] Zhang Jie-tan, Cheng Hao-zhong, Hu Ze-chun1, et  al. Power System Probabilistic Production Simulation Including Wind Farms[J]. Proceedings of the CSEE, 2009, 29(28):34–39. [5] Kabouris J, Contaxis G J. Optimum expansion planning of an conventional generation system operating in parallel with a large scale network [J]. IEEE Transactions on Energy Conversion, 1991, 6(3): 394–400. [6] Roy S. Market constrained optimal planning for wind energy conversion systems over multiple installation sites [J]. IEEE Transactions on Energy Conversion, 2002, 22(1): 67. [7] Kennedy J, Eberhart R. Particle Swarm Optimization [C]. IEEE international Conference on Neural Networks, Perth, Australia, 1995: 1942–1948.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A design method of inductance-capacitance filter circuit for reducing current harmonics of high-speed motor Y.Q. Mo & P.J. Dong Harbin Institute of Technology, Harbin, China

ABSTRACT: The low inductance of high-speed motor give its phase currents a high dynamic response speed, which may also result in some control problems, such as high Total Harmonic Distortion (THD) of stator current, high copper and core loss, and large torque ripple. This paper proposes a design method of a three-phase inductance-capacitance (LC) filter connected to the three-phase stator windings to reduce the stator current THD for the high-speed Permanent Magnet Synchronous Motor (PMSM). First, the mathematical model of PMSM with LC filter is introduced. Then the design principle of the LC filter parameters are presented by analyzing the magnitude-frequency characteristic of the PMSM system. And, the value ranges of the parameters are introduced according to of the maximum output current and voltage of the Pulse Width Modulation (PWM) inverter. The simulation and experimental results are finally given to verify the effectiveness of the paper’s design. Keywords: 1

inductance-capacitance filter circuit; Total Harmonic Distortion; high-speed motor

INTRODUCTION

High-speed permanent Magnet Synchronous Motor (PMSM) always has stator inductances lower than 0.5  mH due to its structural features. While the low inductance characteristic is beneficial in improving the dynamic response speed of phase currents, it may exert some adverse impact on the motor control system. If this kind of PMSM is driven directly by a traditional twolevel Pulse Width Modulation (PWM) Voltage Source Inverter (VSI) with a switching frequency at several kilohertz, it is difficult to regulate the stator currents effectively since the phase currents will be mixed with many higher-order harmonic currents. Several methods have been proposed in order to reduce the THD of the three-phase currents for low inductance motor. The simplest one is to add three external inductances in series with the stator windings in order to make the equivalent value of each phase stator inductance larger (Zwyssig 2008, Venkataramanan 1993, Mecke 2009, Kosaka 1999, Mellor 1996, Koshio 2009, Kosaka 2001). However, this method will increase the volume, weight, and cost of the control system. Another method is to increase the switching frequency of PWM-VSI in order to regulate the currents better (De 2012). Yet, the switching frequency of switching devices is limited by their maximum input frequency.

Venkataramanan (1993) proposed a method to add three-phase LC filter circuit between the PWM-VSI and the motor, which can reduce the volume and cost of the control system compared with the adding external inductances method. Caricchi & Crescimbini (1994), Zwyssig & Kolar (2009), Minshull & Bingham (2007), Minshull & Bingham (2010) proposed a method of adjusting the Direct Current (DC) bus voltage to improve the stator current waveforms for brushless DC (BLDC) motors. This method employs a halfbridge DC chopping circuit whose energy is bidirectional flowing in the front end of the PWM-VSI to adjust the DC bus voltage. Su & Adams (2001) adopted multilevel PWM-VSI to subdivide the output voltage and obtain more output levels, which will make the output waveform close to sinusoidal or square waveform. Current Source Inverter (CSI) was also applied to the low inductance motors. This method is always used to drive BLDC (Takahashi 1994, Woolaghan 2009) since the output of CSI is current. This paper introduces a three-phase LC filter circuit to improve the stator current waveforms for the high-speed PMSM. The parameters of the LC filter are designed based on both the purpose of minimizing the stator current THD and satisfying the maximum output current and output voltage capability of the PWM-VSI. The simulation and experimental results are presented to verify the effectiveness of the paper’s design.

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2

MATHEMATICAL MODEL OF PMSM WITH LC FILTER

The control system chart of the high-speed PMSM with three-phase LC filter driven by PWM-VSI is shown in Figure 1. According to Figure 1, the mathematical model of the system expressed by differential equations are as follows (Taking a-phase for example) ua

L

dia + Riia + ea dt

(1)

d 2ia di de + RC a + ia + C a dt dt dt 2

ia′ = LC

ua′ = LL ′C

(2)

Figure  1. Control system chart of high-speed low inductance PMSM with three-phase LC filter. Where R, L, C, and L′ represent stator resistance, stator inductance, external capacitance and external inductance respectively, u and i represent phase stator voltage and current, i′ and u′ represent PWM-VSI output current and voltage.

d3ia d 2ia di + RL C + (L + L′) + a ′ 3 2 dt dt dt

+ Riia + L ′C

d 2ea + ea dt 2

(3)

From (1) and (3), we can see that the control sysytem of high-speed motor turns from first order system into third order system after connecting three-phase LC filter to the stator windings. 3

PARAMETERS DESIGN OF LC FILTER

3.1

Minimizing stator current THD

According to equation (3), the transfer function of the system is as follows Φ( )=

Ia ( ) 1 = U a′ ( ) LL ′Cs3 + RL ′Cs 2 + (

Figure 2. Relationship of characteristic curves of Φ(s) and Φl(s).

)s R (4)

Ignoring the cubic term and quadratic term, the Low Frequency (LF) transfer function Φl(s) can be approximated as Φ l (S ) ≈

1 1 ≈ 2 ( L

L )s  R RL Cs + ( (L L L ) s R ′   Φl 2 ( s )

Φl 1 ( s )

(5) The relationship of the characteristic curves representing (4) and (5) are shown in Figure 2. It can be seen from Figure 2 that these three characteristic curves coincide with each other in the LF band. So the cut-off angular frequency ωb of the system can be derived approximately from (5)

ωb ≈

L

L ′ − ( L + L ′ )2 − 4R 2 L ′C R ≈ 2RL ′C L L′

(6)

From equation (4), we know that the slope of the characteristic curve will turn from −20 dB/dec to −60 dB/dec at the break frequency in the High Frequency (HF) band, so the characteristic in the HF band can be approximated to be an integral unit and a second order oscillation unit in series. Thus, the constant term in the denominator of Φ(s) can be ignored, and the HF transfer function Φh(s) can be approximated as Φh (s) ≈

s( LL C Cs 2

1 RL C Cs

L

L )

(7)

The relationship of the characteristic curves of (4) and (7) are shown in Figure 3. It can be seen from Figure 3 that these two characteristic curves agree well in the HF band. So the natural oscillation angular frequency ωn and

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the damping ratio ζ of the system can be derived from (7).

ωn = ζ=

L L′ LL ′C

(8)

R L ′C 2 L( L + L ′ )

ω n 1 − 2ζ 2 =

1. The resonance angular frequency ωr should satisfy

ω1 ω r ω s

(9)

Thus, the resonance angular frequency ωr and the resonance peak Mr of the system can be derived as

ωr

As a result, the selection of the parameters of LC filter should stick to the following two principles:

L L′ R2 − LL ′C 2 L2

(10)

(12)

In this way, parallel resonance can be avoided at the fundamental frequency, and the harmonic currents around the switching frequency can be effectively attenuated; 2. The resonance peak Mr should be designed as small as possible to reduce the oscillation amplitude at the resonance angular frequency ωr. This can be achieved by increasing the value of the external inductance L′ in series.

4

M r = 10 lg ≈ 20 lg

(

R L L′′ R L ′C 2

L

8L

R L C − R LL ′C + L L ′

)

3.2

RL ′

(11) The harmonic components of the three-phase PWM-VSI output voltage also need to be analyzed In order to facilitate the parameter design of the LC filter. According to W & L (2009), we can obtain the harmonic frequencies of the output phase voltage of the three-phase PWM-VSI as nfs ± kf1, where fs and f1 refers to switching frequency and fundamental frequency respectively. When n is even, k is a positive odd number not divided by three. Thus, we can come to a conclusion that the output phase voltage of the three-phase PWM-VSI contains the harmonics only around, but not at the integer multiples of switching frequency.

Considering the output current capability of PWM-VSI

In general, the maximum output current of PWMVSI is less than 120% of the rated stator current of the motor, so the amplitudes of ia and i′a in Figure 1 should satisfy the following inequality I am ′ ≤ 1.2 I am

(13)

Under the motor’s rated working condition, we have I q sin ωt

ia*

(14)

(

)

2

I am ′ = I q2 ω 2 LC − 1 +

2

C2

(

)

+

2

(15)

where Iq is the rated q-axis current. So Iam and I′am can be expressed as I am

(16)

Iq

(

)

2

I am ′ = I q2 ω 2 LC − 1 +

2

C2

(

)

+

2

(17)

By substituting (17) and (16) to (13), we can obtain

C≤

Iq

(

L Iq

(

R Iq

f

5ω ω L I + R I 2

2

2 q

2

2 q

RI q f

R RII q +

LI q L

f 2

2 f

)

)

(18)

Figure 3. Relationship of characteristic curves of Φ(s) and Φh(s).

Eq. (18) gives the value range of the capacitance C, during which the output current of PWM-VSI will not exceed its permitted maximum output current.

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3.3

Considering the output voltage capability of PWM-VSI

Table 1.

The maximum output voltage of PWM-VSI is related to the DC bus voltage Udc. If the Space Vector PWM (SVPWM) method is adopted to control the PWM-VSI, the amplitude of the output phase voltage U′am satisfies the following equation U am ′ ≤

3 U dc 3

(19)

Under the motor’s rated working condition, we have u′′a* =

(

) (ω L′C − 1) i

+

(

2

Symbol

Meaning

Value

Udc fs p ψf

DC bus voltage Switching frequency Number of pole pairs Amplitude of permanent magnet flux linkage Stator resistance Stator inductance Rated q-axis current Rated rotor angular velocity

300 V 18 kHz 1 0.0184 Vs

R L Iq ω*

0.332 Ω 0.429 mH 5.13 A 2000 πrad/s

ωt

)

+ ω I q ω LL ′C − L − L ′ cos ωt 2

Parameters of the PMSM and PWM-VSI.

(20)

* can be derived from (20). The amplitude U ′am * By substituting U ′am to (19), the value range of external inductance L′ can be obtained when the capacitance C is assigned with the maximum value of (19). The larger the external inductances are, the lower the stator current THD is, however, the volume, weight, and cost of the system will also increase, so the value of the external inductances should be selected just to satisfy the demand of the stator current THD.

Figure 4.

4

A-phase current waveform without any filter.

SIMULATION AND EXPERIMENTS

This section will present the simulation and experimental results of the high-speed low inductance PMSM with no filter, three-phase L filter, and three-phase LC filter respectively, and compare the stator current THD of these three methods. The parameters of the PMSM and PWM-VSI are shown in Table 1. 4.1

Simulation results

If the stator windings of the PMSM are directly connected to the PWM-VSI without any filter, a-phase current waveform is shown in Figure 4. If the stator windings are connected with threephase L filter, a-phase current waveform is shown in Figure 5. The value of the external inductances in Figure 5 is selected as 0.32 mH. The stator current THD in Figure 5 is 12.47%. From Figure  5, it can be seen that the stator current THD of the three-phase L filter method is still high. If it is needed to reduce the stator current THD further, the value of the external inductances should be larger. The magnitude-frequency

Figure  5. A-phase current waveform with three-phase L filter (L′ = 0.32 mH).

characteristics of the and with three-phase L Figure 6. Figure  6  shows that quency decreases as the

PMSM without filter filter are both shown in the cut-off angular frevalue of L′ increases, so

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the larger the external inductances are, the more powerful attenuation capability to high order harmonic currents is achieved. In addition, the slope of both the characteristic curves is −20 dB/dec in the HF band, which is −60 dB/dec for the LC filter method. So the L filter method has worse attenuation capability to high order harmonic currents than the LC filter method. If the stator windings are connected with threephase LC filter to reduce the stator current THD, the value of the capacitances should be selected to satisfy (18). According to (18), the value of C should be less than 5.174 μF, and here we select C = 5 μF. The value of the external inductances is still selected as 0.32 mH. A-phase current waveform of the LC filter method is shown in Figure 7.

Figure  6. Magnitude-frequency characteristic PMSM without and with three-phase L filter.

The stator current THD in Figure  7 is 4.27%. By comparing Figure 5 and Figure 7, it can be seen that the LC filter method can reduce the stator current THD more largely than the L filter method. Similarly, increasing the value of inductances in the LC filter will reduce the stator current THD. If the L filter wants to achieve the same effect as the LC filter, the value of the external inductances in the L filter method should be much larger. The relationship of the stator current THD and the external inductances of the L filter method is shown in Figure 8. From Figure  8, it can be seen that THD is reduced to 10% if the value of L′ is 0.5 mH, which is still higher than that of LC filter method. So the LC filter method is much better than the L filter method. The relationship of the stator current THD and the external inductances of the LC filter method when C = 5 μF is shown in Figure 9.

of Figure  8. Relationship of stator current THD and external inductances of L filter method.

Figure  7. A-phase current waveform with three-phase LC filter (L′ = 0.32 mH, C = 5 μF).

Figure  9. Relationship of stator current THD and external inductances of LC filter method (C = 5 μF).

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It can be seen from Figure 9 that the stator current THD is much higher at some value of L′. It is just because at these points, the resonance angular frequency ωr is at or near the integral multiples of switching frequency, and the resonance peak Mr is relatively high. So we need to design the parameters of LC filter reasonably to obtain better performance. A ideal design criterion of the parameters of LC filter is to make it work on the center of the maximum flat area of the current THD curve. 4.2

Experimental results

Figure  10 is the control board which is based on TMS320F2808 designed by Texas Instruments. The MOSFET with the type STB20NM60 designed by Figure  12. Experimental a-phase current waveform with three-phase L filter (L′ = 0.32 mH).

Figure  10. The photo of control board for the experiments.

Figure  13. Experimental a-phase current waveform with three-phase LC filter (L′ = 0.32 mH, C = 5 μF).

SGS-THOMSON Microelectronics is selected as the switching tube. If the stator windings are connected with no filter, three-phase L filter, and three-phase LC filter respectively, the corresponding phase current waveforms in these three cases are shown in Figure 11, Figure 12, and Figure 13. 5

Figure  11. Experimental a-phase current waveform without any filter.

CONCLUSION

This paper presents a complete design process of three-phase LC filter for the high-speed low inductance PMSM to reduce the stator current THD. The parameters of the LC filter are designed

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in consideration of both the purpose of minimizing the stator current and satisfying the maximum output current and output voltage capability of the PWM-VSI. The simulation and experimental results are carried out with no filter, three-phase L filter, and three-phase LC filter respectively. Both the results show that the LC filter method can obtain the lowest stator current THD, and is the best method of these three methods. REFERENCES Caricchi, F. & Crescimbini, F. 1994. Experimental study of a bidirectional DC-DC converter for the DC link voltage control and the regenerative braking in PM motor drives devoted to electrical vehicles, Applied Power Electronics Conference and Exposition. pp. 381–386. De, S. & Rajne, M. 2012. Low-inductance axial flux BLDC motor drive for more electric aircraft, Power Electronics, IET, vol. 5, no. 1, pp. 124–133. Kosaka, T. & Hasegawa, H. 2001. Experimental investigations into skin effect influences on current distortion and increase in loss for 20  kHz PWM-VSI-fed slotless PMSM drives, Industry Applications Conference, Thirty-Sixth IAS Annual Meeting. Conference Record of IEEE, vol. 4, pp. 2374–2379. Kosaka, T. & Matsui, N. 1999. Drive characteristics of slotless PM motors, Industry Applications Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of IEEE, pp. 894–899. Koshio, N. & Kubota, H. 2009. Improvement of current waveforms of position sensor-less vector controlled permanent magnet synchronous motor at high frequency region, Electrical Machines and Systems, ICEMS. International Conference, pp. 1–5. Mecke, R. 2009. Permanent magnet synchronous motor for passenger ship propulsion, Power Electronics and Applications European Conference, pp. 1–10.

Mellor, P.H. & Allen, T. 1996. Hub-mounted electric drive-train for a high performance all-electric racing vehicle, Machines and Drives for Electric and Hybrid Vehicles, pp. 3/1–3/6. Minshull, R. & Bingham, M. 2007. A back to back multilevel converter for driving low inductance brushless AC machines, Power Electronics and Applications, European Conference on, pp. 1–9. Minshull, R. & Bingham, M. 2010. Compensation of nonlinearities in diode-clamped multilevel converters, Industrial Electronics, IEEE Transactions on, vol. 57, no. 8, pp. 2651–2658. Su, J. & Adams, J. 2001. Multilevel DC link inverter for brushless permanent magnet motors with very low inductance, Industry Applications Conference, Thirty-Sixth IAS Annual Meeting. Conference Record of IEEE, vol. 2, pp. 829–834. Takahashi, I. & Koganezawa, T. 1994. A super high speed PM motor drive system by a quasi-current source inverter, Industry Applications, IEEE Transactions on, vol. 30, no. 3, pp. 683–690, May/Jun. Venkataramanan, G. 1993. Evaluation of inverter topology options for low inductance motors, Industry Applications Society Annual Meeting, Conference Record of IEEE, vol. 2, pp. 1041–1047. Woolaghan, S. & Schofield, N. 2009. Current source inverters for PM machine control, Electric Machines and Drives Conference, IEMDC, IEEE International, pp. 702–708. W, Z. & L. J. 2009. Power Electronic Technology, 5th ed. China Machine Press. Zwyssig, C. & Kolar, J. 2009. Megaspeed drive systems: Pushing beyond 1 million r/min, Mechatronics, IEEE/ ASME Transactions on, vol. 14, no. 5, pp. 564–574. Zwyssig, C. & Round, S.D. 2008. An ultrahigh-speed, low power electrical drive system, Industrial Electronics, IEEE Transactions on, vol. 55, no. 2, pp. 577–585.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Burr detection algorithm based on machine vision Zhan Shi, Chuan-Li Xi & Hao-Lin Li University of Shanghai for Science and Technology, Shanghai, China

Fu-Sheng Tan & Jun-Fan Yan Shanghai Electric Center Academy, Shanghai, China

ABSTRACT: Burr is usually formed and attached to the edge of surfaces during machine operations; it poses a severe risk for component life. In this paper, an algorithm of burr detection, by using machine vision, is proposed. The algorithm includes Gauss smoothing, threshold segmentation, edge detection, removing pseudo burr used to detect burr in a workpiece. Detection data is transmitted to the deburring mechanical arm to remove burr. The experimental results show that the algorithm can perform burr measurement quite efficiently. Keywords: 1

burr detection; image processing; Halcon; edge detection

INTRODUCTION

In the modern machinery manufacturing industry, machinery processing occupies 60–70% of the total amount of mechanical processing [1]. In the process of metal parts, especially precision products, existing processing methods can produce burrs. Burrs are mainly distributed in the workpiece edge, corner and arris. Burr can influence the size accuracy, form and position accuracy, precision and surface roughness of the workpiece. It’s hard to assemble, difficult to meet tolerance requirements [2–5]. Modern processing technology doesn’t allow the occurance of burrs. In recent years, with the emphasis on the burr removal, deburring methods have been coming out one after the other. Now commonly used deburring methods are: cylinder grinding method, Thermal deburring, and extrusion honing, abrasive jet deburring, magnetic deburring, ultrasonic deburring, electrochemical deburring, deburring robots and so on. Although there are many methods, but each method has its own advantages and disadvantages [6]. Roller grinding method has the advantages of a simple operation, high production efficiency— but the collision between workpieces can lead to deformation and damage, and this method can’t remove the hole burr in tiny structures; in treatment of a thin wall or a lower melting point material, Thermal deburring easily causes material deformation; The extrusion honing method cannot remove a burr in a blind hole, Grinding action on the workpiece surface affects the part’s accuracy; the abrasive jet method performance is

good, but the method is tedious and it may remain in the workpiece making it hard to clean; the magnetic grinding method and ultrasonic method have difficulty in removingthe burr; the electrochemical method has an electrolytic effect on equipment and the workpiece surface making, the surface lose its luster, and even affect the size and accuracy. The existing methods basically used adopt a method of whole process of workpiece. It’s easy to cause damage to the workpiece itself. Therefore, we use image processing technology, based on machine vision, to detect burrs on the workpiece. This method has fast detection speed and is strongly accurate. Meanwhile, the algorithm can be used to detect a deburred workpiece to judge its deburring effect. 2

MEASUREMENT SYSTEM

Figure 1 shows the flow chart of the burr detection system. The image acquisition stage uses an industrial camera to acquire the contours of the burrs. Collected images are transferred to a computer to detect a burr. The stage of image processing is divided into three parts: Image pre-processing, burr detection and pseudo burr removal. The system hardware includes bracket, a 5 million pixels grayscale industrial camera and light source. The grayscale camera has higher accuracy to capture images of the edges of workpiece [7]. In machine vision, the selection of the light source is very important. Light shape can be changed according to the shape of the workpiece

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3

ALGORITHM RESEARCH

This paper used Halcon to process captured images. The algorithm is divided into three parts: image pre-processing, detection and pseudo burr removal. Image pre-processing is mainly used for initial detection and to extract the burr area; the burr detection part uses the edge detection operator to detect the burr profile; Pseudo burr removal is used to remove noise. 3.1

Image pre-processing

Image processing mainly includes the following steps: image smoothing, threshold segmentation, erosion and dilation, edge extraction, minimum bounding rectangle to determine the burr area.

Figure 1.

3.1.1 Image smoothing Image smoothing is used to raise low frequency components and suppress the high frequency component of image. It can make the image brightness gentle gradient, reduce mutation gradient and improve the image quality [8]. The aim is to reduce the effect of surface impurities. This paper uses the mean filter. This filter use average gray value of pixels around the pixel instead of its original gray value, such as formula (1).

Burr detection flow chart.

G ( x, y )

1 ∑ f x, y ) M

(1)

f(x,y) is the original image gray value, M is the total number of pixels contained in the template, g(x,y) is the smoothed image. Mean filter commonly used 3*3 or 5*5 template. Figures 3 and 4 are original and filtered images.

Figure 2.

Workpiece surface image.

contour. The surface of the workpiece we used was smooth, the use of prospect light irradiating the workpiece can produce serious specular reflection. Therefore, we used a low angle ring light source to irradiate the surface. This light source had the ability to highlight the tiny fluctuation characteristics on a smooth surface. Figure 2 shows the different results of two light sources. The algorithm is implemented in the machine vision software, Halcon. This software is an image processing database. Various image processing algorithms can find the corresponding function in this software. Compared with other software, it saves the cost of products and shortens the software development cycle.

3.1.2 Threshold segmentation First, the algorithm calculates the average gray value of the filtered image: Gray_mean. According experiments, we selected Gray_mean/2 as the threshold, Obtained region shown in Figure  5. Extracted region contains background of image, but it still contains a lot of noise. So, we use connection operator in Halcon to separate these

Figure 3.

The original image.

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Figure 4.

Filtered image.

Figure 6.

Workpiece edge region.

Figure 5.

Threshold region.

Figure 7.

Boundary extraction.

Figure 8.

Linear boundaries.

Figure 9.

Burr area.

unconnected areas. This operator can divide a region into separate sub-region collection. The largest area region is the background image region, shown in Figure 6. 3.1.3 Contour extraction As the arc segment of the workpiece uses the chamfering processing method, this method does not produce burrs. Therefore, in the image preprocessing stage, we need to remove the arc segment areas. Figure 4 shows that specular reflection caused higher gray value of arc segment. This system uses the expansion algorithm to removing the arc segment. First, the region in Figure 6 expanded 30 and 60 pixels respectively. Then we subtracted two regions to get the boundary of the workpiece contour, as shown in Figure 7. Finally we calculated the average gray values of the region and took it as the threshold to extract linear boundaries, as shown in Figure 8. According to linear boundaries in Figure  8, Halcon operator shape_trans can get the external rectangle of the region, as shown in Figure 9. 3.2

Edge detection

Edge detection is a basic problem in image processing and computer vision; its purpose is to identify brightness and significant change points in image. Significant changes in properties of image usually reflect important events and changes in attributes [9]. Classical edge detection methods are the first order of the derivative maxima algorithm (such as

Robert operator, Sobel operator, canny operator), zero crossing of the second derivative algorithm (such as Laplacian operator, LoG operator) and so on. The new edge detection methods are the mathematical morphology method, fuzzy operator method, wavelet analysis method, genetic algorithm and so on [10]. As is widely used in image processing, the canny edge detection operator has many excellent

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properties. The canny operator has characteristics of high positioning accuracy, low error rate and suppression false edges. The content of the algorithm is as follows. 3.2.1 Image filtering Any edge detection algorithms can’t behave well in original data. The Gaussian smoothing filter was used to remove image noise. The filter is expressed as: x + m y+ m

G ( x, y )

∑ ∑e

−

edge information. Image T2 extracted by the smaller threshold retains more edge information. On this basis, image T2 complements missing information of T1 till the gaps on image T1 are connected. This system, using the Halcon software, has been integrated with the canny operator. Halcon operator edges_sub-pix can extract sub-pixel edges. Extracted sub-pixel edges and partial enlarged edges are as shown in Figures 10 and 11.

x 2 + y2 2σ 2

(2)

x my m

where, m  =  (n−1)/2, n controls the extend of smoothing image. A single pixel noise has almost no influence on smoothed image. 3.2.2

Calculating the value and direction of grads The canny operator adopts first order limited difference of 2 × 2 neighbouring area to calculate the value and direction of grads. X and Y direction of the first-order partial derivatives Px[i, j] and Py[i, j] are: I [i, j 1] I [i, j ] ⎞ 2 Px [i, j ] = ⎛ ⎝ + I [i 1, j 1] I [i 1, j ]⎠

(3)

I [i, j ] I [i 1, j ] ⎞ 2 Py [i, j ] = ⎛ [i, j 1] I [i 1, j 1]⎠ ⎝ + I [i

(4)

The value and direction of grads are: M [i, j ]

Px [i jj]]2 + Py [i [ i , j ]2

θ [i, j ] tan −1 (Py [ , j ]//Px [i, j ])

Figure 10.

Workpiece edge region.

Figure 11.

Partial edges.

Figure 12.

Burr extraction results.

Figure 13.

Burr partial results.

(5) (6)

3.2.3 Non-maximum suppression of gradient In order to get an accurate position and refinement edge, we need to refine the amplitude image edge, keeping only the point which is the largest of the local changes in amplitude—the process is called Non-Maxima Suppression (NMS). The canny operator adopts 3 × 3 neighbouring area, including the direction of 8 neighbour amplitude of the gradient array M(i,j). it takes each pixel partial derivative value compared with the adjacent pixel value; it also takes the maximum value as the edge points. 3.2.4 Connecting edges The canny operator adopts the double-threshold method to extract two images T1[i,j] and T2[i,j]. Image T1 is extracted by the larger threshold which removes most of the noise, but also the loss of useful

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The canny operator achieved good results of edge detection. But there are some defects. The Gaussian filter has poor suppression effect of impulse noise. It’s easy to detect Pseudo edges; Thresholds in double-threshold method have poor adaptive capacity. Therefore, burr detected by canny operator has some Pseudo burr; there is need to remove these false edges. 4

REFERENCES

CONCLUSION

According to the relevant parameters and current measuring methods of burr detection, this paper presents a measurement algorithm based on machine vision. The algorithm reduced the detection range by Gauss smoothing and threshold method;, the Corrosion expansion method was used to remove the interference of arc parts. After edge detection, noise removal, the algorithm finally gets the precise location of the burr. It can provide accurate data for the burr removal stage. At the same time, the system can also be applied to detect burr on the polished workpiece., judging whether or not qualified. ACKNOWLEDGEMENT This work was supported by Shanghai Science and Technology Committee (Number: 13DZ1101601).

[1] Jiyang W. Deburring technology [M]. Science and Technology Literature Press Chongqing branch, 1986. [2] Yunming Z, Guicheng W, Shutian F. Research and application of metal cutting burr expert system based on Neural Network [J]. Chinese mechanical engineering, 2005(12). [3] Yuejun Y. The control and formation of burr milling in the process of aluminum alloy [D]. Shanghai Jiao Tong University, 2005. [4] Sung-Lim Ko, Dornfeld, D.A. Burr formation and fracture in oblique cutting [J]. Journal of Materials Processing Technology, 1996, 62:24–36. [5] Sung-Lim Ko, Dornfeld, D.A. Analysis of fracture in burr formation at the exit stage of metal cutting [J]. Journal of Materials Processing Technology, 1996. 58:189–200. [6] Ziyuan Y. Study on micro hole electrochemical deburring technology [D]. Dalian University of Technology, 2012. [7] Image and machine vision product manual [M]. Beijing Lingyun Optical Technology Co., Ltd., 2013. [8] Shi H, Xiaolu P, Yimin L. An optimal algorithm for the mean filter [J]. Information technology, 2012(3): 133–134. [9] Lihua L. Improved algorithm of Canny edge based on hybrid filter [J]. Science Technology and Engineering, 2011(23). [10] Jinhuan G L Z. Edge detection based on Wavelet Transform [J]. Journal of Qingdao Technological University, 2007, 28(2):65–68.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Numerical simulation and experimental study on the anti-overload ability of cylindrical roller bearing in a short time Y.G. Ni, Y. Li & S.E. Deng School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, China

X.F. Li Beijing Institute of Automation Control Equipment, Beijing, China

ABSTRACT: An elastic-plastic finite element model of cylindrical roller bearing under overload condition is established, analyzing the influences of different overload coefficiency on stress and strain distribution of cylindrical roller bearing; the study shows: under the overload condition (with the occurrence of “fringe effect” at the end part of roller) stress concentration increases remarkably with the increase of load applied; the maximum plastic deformation of the contact area between bearing roller, inner ring, and outer ring—all present a nonlinear increased trend along with the increase of radial load. The measurement of permanent deformation of the bearing after static pressure test verifies the validity of the result of the finite element analysis. Keywords: 1

overload; cylindrical roller bearings; elastic-plastic; permanent deformation

INTRODUCTION

Overload conditions—where the working loads is several times over the rated static load in a short time, usually happens in spacecraft, such as a rocket engine and a steering engine drive. Thus, the selected bearing should not only meet the dimensional requirements of a small size and light-weight, but also avoid the occurrence of failure under a high overload operating condition in a short time. Under short-time high overload condition, the contact area between the rolling element and raceway is going to produce plastic deformation. The bearing contact force and deformation lie in an elastic-plastic non-Hertz contact. The reciprocal action of rolling elements will result in a damage accumulation of plastic deformation. Hence, the low-cycle fatigue problem, caused by cycle elastic—plastic strain, should be taken into consideration when designing this kind of bearing. Analytical calculation on the elastic-plastic stressstrain of rolling contact is the foundation of revealing low-cycle fatigue mechanism of rolling contact. Palmgren[1] purposed a calculation formula of point contact permanent deformation, according to the data of a bearing steel indentation test done in the 1940s, but the permanent deformation calculated according to such formula is greater than that of the deformation generated by high-quality steel modern rolling bearing. Branch N.A. et al[2] have established an elastic-plastic finite element model of bearing

roller and raceway and have conducted an analysis as well on the contact stress field distribution within rolling and raceway, providing thus a foundation for research of bearing fatigue rupture. In recent years, an exploration on the analysis of rolling bearing elastic-plastic contact under a heavy load environment has been carried out by some scholars in China. Zhou Wei and Zhou Hui[3] have analyzed the load distribution of angular contact ball bearing under low velocity overload conditions, the change of contact angle, and the maximum load of steel ball based on the semi-empirical Palmgren formula. Shang Zhenguo[4] et al have established a heavy load turntable bearing static model considering the influence of supporting structure flexibility and material plastic deformation, and analysed the inner load distribution and contact stress of a large-scale heavy load turntable bearing. However, the corresponding experimental verification is not mentioned in the analysis result of the above literature. In this paper, an elastic-plastic finite element model of a cylindrical roller bearing, under overload condition, is established using the nonlinear contact finite element method. The contact stressstrain field distribution between bearing elements of different structural parameter and applied load is analysed and the conclusion on the relation between bearing overload coefficient and plastic deformation and between overload coefficient and contact stress, is obtained. The accuracy of the model was further tested by experiments of

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permanent deformation measurement. The antioverload ability of a cylindrical roller bearing is studied through comparison of front and rear bearing rotary precision, before and after static load. The research results will provide a theoretical foundation and technical support for the performance analysis of a cylindrical roller bearing under short-time heavy load working condition.

2

THEORY ON THE FINITE ELEMENT ANALYSIS OF ELASTIC-PLASTIC CONTACT

Elastic-plastic rolling contact belongs to a highly nonlinear problem. The normal contact between bearing elements is established using the extended Lagrangian Multiplier Method and surface-tosurface contact analytical method. The modified Newton-Raphson Method, with fast convergence and little amount of iterative computations each time, is applied when calculating the system of nonlinear equations. Usually, the nonlinear algebraic equations[5] could be expressed as: P (a ) = Q

(1)

The concrete form of formula (1) usually depends on the nature of problem and discrete element method. P(a) is the vector of a’s nonlinear function vector, and Q is the known vector independent of a.

3

FINITE ELEMENT MODEL OF BEARING UNDER OVERLOAD CONDITIONS

Figure  1. bearing.

Only radial load applied on cylindrical roller bearing, outer surface of outer ring is fixed; the axial translational degree of freedom of inner ring and outer ring is constrained as well as circumferential movement of roller is restricted. Overload coefficient k is defined to analyse stress and strain distribution of bearing. k P0 / Cor , in the formuia, P0 is bearing radial load, Cor is bearing rated static load. The shaft is a rigid part. A radial load Fr is applied on coupling nodes on the inner wall of a shaft hole, Fr 4.6 Po /Z [7], Z is the number of roller. 4 4.1

With the aid of ANSYS, a 3-D model of different kinds of cylindrical roller bearing assembly was established in APDL. A bearing assembly finite element model on the maximum rolling element loading location is built only under applied radial direction load. The finite element model is meshed with the SOLID 187 tetrahedron element, and the material of bearing assembly is GCrl5. The finite element model is shown in Figure 1. In order to avoid the occurrence of the “fringe effect” on both ends of the roller, the logarithmic profile is adopted. Considering the ending chamfer, the equation of logarithmic profile lines is[6]: y=2

1 − v 2 Qmax 1 In π E Lwe 1 − (2 x /Lwe )2

( − Lwe /2 x Lwe / 2 )

(2)

Finite element model of cylindrical roller

ANALYSIS OF SIMULATION RESULTS Influence of different overload coefficient k to contact stress

In the cylindrical roller bearing NU210EM, the design of roller convexity should be designed according to load Q Cor . If theoretical convexity approximate value reaches 0.021, it should establish crowned roller finite element model. Different radial loads should be conducted respectively on finite element analysis (k = 0.2 ~ 2.5 ), and the result compared in the end. Figures 2–3 are respectively served as the distribution cloud chart and distribution curve of contact stress of NU210EM cylindrical roller bearing between roller and inner ring raceway, along the length direction of roller under different loading coefficient. It can be found that when bearing endures lighter applied load, that is k < 0.5 , the maximum contact stress will occur in the contact center due to roller logarithm generatrix convexity. The contact stress curve of roller and inner ring

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Figure 2. Contact stress of roller.

contact line slowly increases from the end to the middle part along the direction of curve. As is shown in Figure 3a, the “fringe effect” of the end part does not occur to roller. But when k is greater than 0.5, with the increase of overload coefficient k, the stress peak occurs around the roller margin, and is greater than the stress of the middle part. The greater k is, which the greater radial load is, the remarkable increased degree of stress concentration will be. 4.2

Influence of overload coefficient k to bearing plastic deformation

If radial load is imposed on different kinds of cylindrical roller bearings, the influence rule of different overload coefficient k to plastic deformation of each bearing contact area will be drawn through changing the overload coefficient k. Figure 4 shows the influence of different overload coefficient k to different models of bearing plastic deformation. It can be found from Figure 4 that under the dual function of material nonlinearity and contact nonlinearity, the maximum plastic deformation of contact area between bearing roller, inner ring, and outer ring, all present a nonlinear increased trend along with the increase of overload coefficient. When the value of overload coefficient k is smaller, the contact plastic deformation of inner ring raceway is greater than that of the outer ring raceway. But with the increase of k value, which is the enlargement of radial load, the velocity of plastic deformation generated by roller accelerates, and finally the plastic deformation of roller, is greater than that of the inner and outer ring. Take NU210EM

Figure  3. Contact stress distribution curve of NU210EM bearing between roller and inner ring raceway along the length direction of roller.

as an example, as is shown in Figure 4a, when k value is smaller than 2.25, the plastic deformation of inner ring will be greater than that of contact area between roller and rollaway; when k value is greater than 2.25, the plastic deformation on roller will be greater than that of inner ring raceway.

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Figure  5. machine.

Microcomputer hydraulic universal testing

Table  1. Contrast of permanent deformation between the test results and finite element analysis result of NU210EM bearing. Test results/ um K = 2 (Q = 138 KN) Roller 2.64 Inner ring 7.29

Figure 4. Influence of different overload coefficient to plastic deformation of bearing.

The plastic deformation between the outer ring raceway and roller will also be the smallest under different external loading.

5

EXPERIMENTAL VERIFICATION

First, a microcomputer hydraulic universal testing machine (Fig. 5) is applied for the static pressure test of 12  sets of NU210EM bearings. The bearings will be divided into 4 groups with k = 2, 2.5, 3, 4 times of radial load imposed on 4 groups of bearing in the process. After the static pressure test, the permanent deformation of each bearing components should be tested after loading through the contourgraph.

Finite element analysis result/um

Relatively error

2.81 7.62

6.43% 4.53%

K = 2.5 (Q = 173 KN) Roller 17.73 Inner ring 12.1

19.11 13.19

7.78% 9.01%

K = 3 (Q = 138 KN) Roller 26.17 Inner ring 18.74

28.26 20.12

7.99% 6.86%

K = 4 (Q = 173 KN) Roller 45.76 Inner ring 32.3

47.12 35.01

2.97% 8.39%

5.1

Permanent deformation

After the static test, the permanent deformation of bearing should be measured with the help of a contourgraph, and comparison analysis should be conducted between the test results and finite element analysis result, as is shown in Chart 1. From the comparison above, it can be seen that the maximum error of the finite element computed result is less than 10%, which indicates that the deformation of each bearing component calculated by finite element method is relatively accurate. 6

CONCLUSION

An elastic-plastic contact finite element model of cylindrical roller bearing is established by ANSYS,

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analyzing the influence of different overload coefficient k to bearing stress-strain distribution. The conclusions drawn are the following: 1. Through the elastic-plastic finite element analysis on cylindrical roller bearing with convexity under overload condition, it finds that the “fringe effect” will occur at end part of roller. With the increase of overload coefficient k, the degree of stress concentration will increase remarkably. 2. Under overload working condition, the maximum plastic deformation in contact area between bearing roller, inner ring and outer ring will present an increasing trend along with the increase of overload coefficient. When the overload coefficient k is relatively small, the maximum plastic deformation will occur in the inner ring raceway contacted with roller. When k is increased to a certain degree, the maximum plastic deformation will occur on the roller. 3. Compared with the finite element result and test result, the error is less than 10%, it indicates that the plastic deformation of each bearing component calculated by finite element method is relative accurate.

Projects of Ministry of Science Technology, JPPTZCGX1-1/5-1 REFERENCES [1] Palmgren, A. 1959. Ball and Roller Bearing Engineering. Philadelphia: Burbank. [2] Branch, N.A. & Arakere, N.K. 2010. Stress field evolution in a ball bearing raceway fatigue spall. Journal of ASTM International 7(2). [3] Zou Wei & Zhou Hui. 2010. Research on the Load Distribution of Low Speed and Overload Aagulai Contact Ball Bearings. Mechanical engineer 8: 1–3. [4] Shang Zhengguo. & Dong Huimin, et  al. 2011. Finite element analysis method of slewing bearing with plastic deformation. Transactions of the CSAE 27(12): 52–56. [5] Zou Wei & Zhou Hui. 2010. Research on the Load Distribution of Low Speed and Overload Aagulai Contact Ball Bearings. Mechanical engineer 8: 1–3. [6] Luoyang Bearing Research institute. 2008. The design method of reinforced cylindrical roller bearing. Luoyang. [7] Deng Sier. & Jia Quny. 2008. Design principle of rollng bearing. Beijing: Standards Press.

ACKNOWLEDGMENTS The authors would like to express their thanks to the support provided by National Special

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Numerical simulation and analysis of wavefront reconstruction iterative method in radial shearing interference Y.F. Wang & Z.S. Da Xi’an Institute of Optics and Precision Mechanics, Xi’an, China

ABSTRACT: The interference graph collected by the radial shearing interferometer does not directly reflect the original wavefront information, so one needs the wavefront reconstruction of the original wavefront. This paper deduced the iterative method for wavefront reconstruction, and conducted the numerical simulation of the wavefront reconstruction algorithm by using Matlab at different shearing ratio. The simulation results were analysed and the conclusions were: with radial shearing ratio decreases or telescope system magnification increases, only a lesser iteration number is needed to reach high precision, so we can choose proper shearing ratio to simplify the numerical computation of the tedious iterative process, improving the speed of computation; Then the analysis provides the basis for the design of the telescope system in the radial shearing interferometer. Keywords: 1

radial shearing interference; wavefront reconstruction; numerical simulation

INTRODUCTION

Radial shearing interferometry technique is an effective method for laser wavefront measurements. Since the radial shearing interferometer, without setting the reference light, can be measured by a relatively large diameter range of temperature lift, air flow, ground vibration and other environmental impacts which are not sensitive to shear, than simply changing the measurement accuracy is also changing, as compared to a conventional interferometer has its own unique advantages; it is widely used in the fields of the optical element surface type detector, high-speed measurement of the pulse wavefront and beam-quality measurements. Foreign scientists have earlier done a lot of research on the shearing interferometer[1–7] and have come up with applications in engineering. Many domestic scientific research institutions in China have added to the study of radial shearing interferometer. Chinese Academy of Engineering Physics, Sichuan University, Zhejiang University, Harbin Institute of technology, Nanjing Institute of Technology and so on have done research in the corresponding[8] field and have achieved much success. The difficulty of radial shearing interferometer is how to obtain the original wavefront information from interference fringes, which requires of wavefront reconstruction. In order to further the analysis of wavefront reconstruction iterative algorithm accuracy (according to the wavefront to be

tested in advance to know about PV value) then one must select the reasonable radial shearing ratio, get higher accuracy and reduce the calculation time. In this paper, the iterative algorithm of wavefront reconstruction is derived, and the wavefront reconstruction iterative algorithm is simulated using Matlab with different shearing ratio. Finally the simulation results are analysed. Since then the radial shearing interferometry telescope system has been based on the analysis. 2

THE PRINCIPLE OF RADIAL SHEARING INTERFERENCE

The radial shearing interferometer produces two interfering wavefronts with identical deformations, but one of the wavefronts is contracted or expanded with respect to the other in Figure 1. The radial shearing interference pattern or the Optical Path Difference (OPD) of one wavefront expanded is given by OPD( ρ , θ ) W ( ρ , θ ) − W ( βρ , θ )

(1)

where W is the wavefront; ρ and θ are the radial and circular coordinates, respectively; and β ( β ) is the radial shearing ratio, which is the ratio of the wavefront radius under test and that of the reference wavefront. If the figure in the central reference areaW ( βρ , θ ) is well determined, the figure measurement shall

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testing, can be done, not only to know the shape of the wavefront, but also with the hope of getting the wavefront aberration information at the same time. Due to the high fitting precision of the optical wavefront of the Zernike polynomial, the orthogonality of the Zernike polynomial to fitting polynomial coefficients are independent of each other, to avoid the confusion caused by the coefficient between the coupling; and the Zernike polynomial itself has rotational symmetry, and has good convergence for solving problems in the process of optical design. Zernike polynomials

Figure 1.

Fringe of radial shearing interference.

be accurate. However, when the central part has errors as is usually the case, some corrections are needed. Here, two correction methods, the Zernike polynomial expansion and iterative method are proposed. Both correction methods have been applied to fringe analysis and showed their effectiveness for error correction. This research work has mainly used theiterative method. 3

ITERATIVE CORRECTION METHOD

Multiplying β to radius ρ, the OPD is given as OPD( βρ , θ ) W ( βρ , θ ) − W ( β 2 ρ , θ )

(2)

OPD( β ρ , θ ) W ( β ρ , θ ) − W ( β ρ , θ )

(3)

OPD( β ρ ,θ ) W ( β ρ ,θ ) − W ( β ρ ,θ )

(4)

2

2

3

3

3

4

Repeating this procedure up to i n and adding these equations in succession, the accurate wavefront can be calculated as W ( ρ, θ )

n

∑ OPD( β i ρ, θ )

W ( β n ρ, θ )

(5)

i =1

where n is the number of repeats and the term W ( β n ρ , θ ) is converging to 0 or a small point with increasing n. 4

NUMERICAL SIMULATION

The iterative method can be used in different exit pupil wavefront reconstructions. Actual

Figure 2. The different iteration results under the shearing ratio is 0.5.

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Figure 3. The different iteration results under the shearing ratio is 0.25.

Figure 4. The different iteration results under the shearing ratio is 0.66.

have a certain relationship with the Seidel primary aberration; the aberration function is easy with the link to the optical design, thus it is often used in fitting to solve optical aberration. The iterative algorithm third parts mentioned above use the Matlab numerical calculation software for numerical simulation, for a different number of iterations, the radial shear of different shearing ratio situation to carry on the simulation, in order to calculate the simple selection of Zernike polynomial of the first eight simulation. The first eight Zernike polynomials in the Cartesian coordinate system is represented as:

where z0 is constant coefficient, z1 is inclined  x direction coefficient, z2 is inclined y direction coefficient, z3 is the defocus coefficient, z4 is the astigmatic coefficient (axis direction of 0  degrees or 90  degrees), z5 is the astigmatic coefficient (axial direction of 45 degree), z6 and z7 are the third-order coma coefficients, z8 is the third-order spherical aberration coefficient. In order to make comparisons between different shearing ratio, we construct a distorted wavefront, we get z0 z1 = z2 z3 = z5 = 0 , z4 = −0.85 , z6 = −0.8, z7 = 0.5, z8 = 0.069 . Getting different shearing ratio of the figures are shown in Figures 2–4. Analysis of the above simulation chart, from Table 1, 2, 3 we know the initial PV value of the distortion wavefront is relatively large and can be considered as a big wave aberration. When the shearing ratio is equal to 0.5, the residual wavefront PV value reaches 1/1000 after 6 iterations;

W ( x, y )

2

2

z0 + z1x + z2 y + z3 ( 1+ 2 x + 2 y ) 2

2

3

2

+ z4 ( x − y ) + z5 2 xy + z6 ( 2 x +3 x +3xy ) 2

3

+ z7 ( 2 y + 3 yx + 3 y ) +

8 (1

2

2

4

2 2

4

6 x − 6 y + 6 x + 12 x y + 6 y )

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5

Table  1. Under different iterations residual wavefront and reconstruction wavefront PV value when shearing ratio is 0.25. Shearing ratio β

Iterations N

Reconstruction wavefront PV/(λ)

Residual wavefront PV/(λ)

0.25

0 1 2 6

14.9978 14.9518 4.1171 9.8437

0.0624 0.0156 6.1035e-4

Table  2. Under different iterations residual wavefront and reconstruction wavefront PV value when shearing ratio is 0.5. Shearing ratio β

Iterations N

Reconstruction wavefront PV/(λ)

Residual wavefront PV/(λ)

0.5

0 1 2 6

14.9978 14.6041 7.5456 7.7452

0.2441 0.1243 0.0078

Iterations N

Reconstruction wavefront PV/(λ)

Residual wavefront PV/(λ)

0.66

0 1 2 6

14.9978 13.5182 23.1392 25.2654

0.9625 0.6334 0.0545

In this paper, the iterative algorithm for wavefront reconstruction is derived. Numerical simulation of the wavefront reconstruction iterative algorithm is done—by using Matlab numerical calculation software, analysis of the influence on the simulation accuracyand the computational complexity in the impact process numerical simulation with different shearing ratio. Through the analysis we can see that, with the radial shearing ratio decreasing or increasing telescope magnification, only less iteration numbers are needed to reach high precision. We can choose the appropriate shearing ratio so as to simplify the numerical computation’s tedious iterative process and improve the operation speed; for wavefront with large complex wave aberration, the estimation of PV value is very important. According to estimates, we can obtain higher precision wavefront in finite times iteration, and also reduce the wavefront reconstruction time needed. This has the practical value for real time wavefront measurement system. But the algorithm has ignored the influence of actual interference error caused by the tilt of the system. Further work needs to be done on this. REFERENCES

Table  3. Under different iterations residual wavefront and reconstruction wavefront PV value when shearing ratio is 0.66. Shearing ratio β

CONCLUSION

shearing ratio is equal to 0.25, the residual wavefront PV value can reach 1/1000 after 3 iterations; shearing ratio equal to 0.66, the maximum number of iterative (6 iterations) simulation reached only 1/100. Illustrating that radial shearing, ratio decreasing or increasing telescope magnification, only less iteration numbers are needed to reach high precision. We can choose the appropriate shearing ratio, so as to simplify the numerical computation tedious iterative process, improving the operation speed. It is worth noting that, with the increase in the number of iterations, the PV value of the reconstruction wavefront with the emergence of a local minimum, then becomes larger.

[1] P. Hariharan, D. 1962. Interferometric measurements of the aberrations of microscope objectives. Opt. Acta. 9: 159–175. [2] P.J. Wegner, M.A. Henesian, and J.T. Salmon, et  al. 1999. Wavefront and divergence of the Beamlet proto type laser. SPIE. 3492: 1019–1030. [3] A.R. Barnes, L.C. Smith, 1999. A combined phase near and far field diagnostic for large aperture laser system, SPIE. 3492: 564–672. [4] Tsuguo Kohno Daiji Matsumoto, Takanori Yazawa, et  al. 2000. Radial Shearing Interferometer for Inprocess Measurement of Diamond Turning. Opt. Eng. 39(10): 2696–2699. [5] Waldemar Kowalik, Beata Garncarz, Henryk Kasprzak. 2002. Corneal topography measurement by means of radial shearing interference: Part I—theoretical consideration. Optik. 113 (1): 39–45. [6] Waldemar Kowalik, Beata Garncarz, Henryk Kasprzak. 2003. Corneal topography measurement by means of radial shearing interference: Part II—measurement errors. Optik. 114(5): 199–206. [7] N.I Toto-Arellano, G. Rodriguez-Zurita, C. MenesesFabian, J.F V´azquez-Castillo. 2009. A single-shot phase-shifting radial-shearing interferometer. Journal of optics A: pure and applied optics 0457 (045704): 1–6. [8] H.X, et  al. 2002. Algorithm study of wavefront reconstruction based on the cyclic radial shear interferometer. High Power Laser and Particle Beams, 14(2): 223–227.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Minimization of stator loss for high speed permanent magnet motor X.Q. Liu Beihang University, Beijing, China

ABSTRACT: This paper presents an optimization design for stator loss of high speed permanent magnet motor using response surface methodology and finite element method. The current frequency of high speed permanent magnet motor is much higher than low speed permanent magnet motor, which results in increasing ac losses. The stator loss of high speed permanent magnet motor which accounts for a large part of total loss includes core loss and winding loss, which has a great influence on the efficiency. The calculation methods of core loss and winding loss are presented in this paper. Then, three optimization variables are selected and the way of influencing the stator loss is analyzed. Finally, response surface method combined with genetic algorithm and FEM software is adopted to minimize the stator loss. A set of optimization variables are determined, which corresponds to minimum of stator loss. Keywords: 1

magnetic field; response surface method; FEM

INTRODUCTION

High-speed-motor-driven systems are widely used in many applications, due to the huge advantages, such as higher system efficiency, higher system reliability, lower size and lower weight. Due to the high operating speed, the motor size and weight can also be reduced greatly. The loss density increases greatly with the power density, which imposes a great impact on the temperature rise of the motor. Additionally, high frequency will impose extra impact on the winding loss and its distribution, such as eddy current effect, proximity effect and circulating current effect, which may be much less remarkable in low frequency occasions with the same power level. The stator loss forms a large part of total loss of high speed permanent magnet motor, which has a close relation with motor efficiency and stator temperature rise. The stator loss is mainly composed of core loss and winding loss, which influences the efficiency greatly. Because rotor eddy current loss is fairly small compared with stator loss and the air friction loss varies to a small extent when rotor dynamic design is finished, the other losses except stator loss can be regarded as constant and neglected in this optimization. 2 2.1

COMPONENTS OF STATOR LOSS AND CALCULATION METHOD

of core loss is a key issue in design of permanent magnet motor all along [1]–[3]. The core loss can be expressed as Pv

K h f Bm )2 + K c fB fBm )2

Core loss

(1)

where, Kh is the hysteresis core loss coefficient, Kc is the eddy-current core loss coefficient, Ke is the excess core loss coefficient, Bm is amplitude of the AC flux component, f is the frequency. The core loss coefficients are derived from iron loss tester. The core loss is calculated by FEM software in this paper. 2.2 Winding loss In high speed permanent magnet motor, the frequency of winding current is much higher than conventional low speed motors. Compared with the low speed motor with same power level, skin effect, proximity effect and circulating effect should be considered especially in the design of high speed permanent magnet motor [4], [5]. 2.2.1 Eddy current effect The ratio of ac effective resistance Rac to dc resistance Rdc by skin effect in round conductor is known to be [6] Rac / Rdc = 1 +

The core loss forms a large part of total losses of permanent magnet motor and the calculation

K e ( fB fBm )1.5

1 ⎛ a⎞ 48 ⎝ δ ⎠

4

(2)

where a is radius of round conductor, δ is skin depth of the conductor in a certain frequency. For high speed motors, thin wires are usually adopted

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to construct the winding. In this case, only a small increase result from eddy current effect to the issue of winding loss [7]. 2.2.2 Proximity effect Proximity effect is the phenomenon that the current distribution in one conductor is influenced by the magnetic field generated by other conductors in the vicinity. And the corresponding loss is called proximity loss. Proximity loss generated in one conductor depends on the dimension of the conductor and the frequency and amplitude of the magnetic field in the region where the conductor is located. The proximity loss of the conductor per unit length is known to be [8].

π d 4ω 2 Bn2 Pe = 128ρc

(3)

where, d is the diameter of the conductor, ω is frequency of magnetic field, Bn is the magnitude of magnetic field, ρc is resistivity of the conductor. 2.2.3 Circulating current effect Circulating current effect is the phenomenon of the uneven distribution of currents among the strands connected in parallel due to the difference in the inductance of each strand. Circulating effect is a different phenomenon from proximity effect, of which the mechanisms are different. There are also differences in the methods of reducing circulating current loss and proximity loss. At a fixed frequency, the proximity loss will be decreased greatly with the decrease of the diameter of the conductor and the magnetic field where the conductors locate. The issue of circulating current is discussed in [9], [10]. Even with thin wire, the circulating current will exist. At the beginning of calculation of the winding loss considering circulating current effect, some assumptions should be made: (a) from the finite element analysis results, the leakage flux in conductor region is parallel with the slot opening. Thus, 1-D model is available, and the conductors will be arranged along the slot center line. And the area each conductor occupies is same; (b) the stands of one coil are arranged in succession along the slot center line, and the sequence of strands remains unchanged; (c) three-phase windings are strictly symmetrical. The inductance of one conductor can be solved by Li = (

1

+

2

+

3

+

4

+

5

/ 2)

On the basis of a single conductor inductance, self and mutual inductances of strands in one branch path can be calculated [11]. One parallel branch can be equivalent to a circuit with electromagnetic coupling among the strands, as shown in Figure 2. If the core loss and core reluctance are neglected, the equations of voltages and currents are ⎧U ⎪U ⎪ ⎨ ⎪ ⎪⎩U

rs I rs I 2

jω L I1 + j M I jω M 21I1 + j L2 I 2

rs I n

jω M1n I n + j M n 2 I

jω M n I n jω M 2 n I n jω Ln I n (5)

where rs is resistance of one strand; Li is selfinductance of one strand in one parallel branch; Mik(i ≠ k) is mutual inductance between strand i and k; U, I1, I2, … In are complex numbers expression of the strand voltage and currents varying sinusoidally; ω is the angular frequency of voltages and currents; j is the imaginary unit. When the current in each strand are derived, the total loss and resistance ratio of the winding can be determined [12].

Figure 1. Equivalent magnetic circuit of one conductor.

(4)

where Λ1, Λ2, Λ3, Λ4 and Λ5 are magnetic permeance of each part, which are shown in Figure 1.

Figure 2.

Equivalent circuit of one parallel branch.

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3

OPTIMIZATION VARIABLES

The most effective parameters are number of strands in one parallel branch, stator tooth width and reserved height of stator slot after the overall design of rotor dynamics. The parameter of reserved height of stator slot is shown in Figure 3. The influence of the three parameters to stator loss is introduced as follows. 3.1

4

Number of strands in one parallel branch

The number of strands in one parallel branch will influence the dc resistance of stator winding. If this value is small, the dc resistance will be large, as well as the average current density. If the number of strands in one parallel branch is too large, the dc resistance will be small, and the loss resulting from dc resistance will be small. However, with the increase of the number of strands in one parallel branch, the area that one branch occupies increases and the differences of inductance between the strands which belong to the same branch in one phase increase. Thus, the winding loss resulting from circulating current effect will be large. From another point of view, with the increase of the number of strands in one parallel branch, the size and the loss of the stator core will increase, as well as the core loss. In conclusion, there will be an optimal value for the number of strands in one parallel branch. 3.2

OPTIMIZATION METHOD

In the process of optimization, response surface method combined with genetic algorithm and the 2-D finite element method is adopted. The response surface function can be built in the form of polynomial function [13], [14] or in the form of base function [15]. In this paper, radical basis function is adopted to construct response surface function. 5

DESIGN OPTIMIZATION FOR STATOR LOSS

5.1

Motor specifications

A high speed permanent magnet motor equipped with AMB bearings is taken as the optimization objective, of which the specifications are shown in Table 1.

Stator tooth width

Stator tooth width will influence the core loss and winding loss simultaneously. When stator tooth width is large, there is less space for the winding. When stator tooth width is small, the magnetic field intensity and the core loss in stator tooth will be large. Keeping the current density of winding constant, the height of stator slot will be increased, which will contribute to winding loss resulting from circulating current effect. 3.3

thickness of stator yoke are kept constant, the increase of the reserved height of stator slot will lead to more core loss, due to the fact that the slot height and tooth length increase with reserved height of stator slot. However, the increase of the reserved height of stator slot will lead to less winding loss, due to the fact that the difference between the strands decreases.

5.2

Optimization objective and constraints

The optimization objective is the total loss of the stator. In this process, the thickness of stator yoke, stack length, air gap length, coil-filling factor and rated parameters of motor are kept constant. 5.3

Range of optimization variables

In this paper, the ranges of optimization variables are shown in Table 2.

Reserved height of stator slot

The parameters of reserved height of stator slot are shown in Figure  3. When tooth width and

5.4

The initial set of sampling points

The initial set of sampling points is composed of 15 points, which is shown in Table 3. The first point is the center of the design space. The following 6 points locate in the center of surface of the Table 1. Main design characteristics of the motor.

Figure 3.

Reserved height of slot.

Parameter

Value

Rotor topology Rated power Rated speed

Surface-mounted magnets 315 kW 20,000 r/min

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Table 2.

Parameter

Range

Number of strands in one branch (mm) Stator tooth width (mm) Reserved height of slot (mm)

[40,80] [5,13] [0,30]

Table 3.

Initial set of sampling points.

Number

Number of strands in one branch

Stator tooth width (mm)

Reserved height of slot (mm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

50 20 80 50 50 50 50 20 20 80 80 20 20 80 80

9 9 9 5 13 9 9 5 13 5 13 5 13 5 13

20 20 20 20 20 0 40 0 0 0 0 40 40 40 40

Table 4.

FEM software. The response surface function of the stator loss with the variables of number of strands in one branch, stator tooth width and reserved height of slot can be constructed. Once the response surface function is constructed, the minimum point of the current response surface function can be determined by genetic algorithm, which is (75, 5, 22.8). After several iterations, the minimum point of the response surface function will converge to a small space. There will be less and less difference between the current minimum point and the one before. When the difference is small enough, the last minimum point can be considered as the global optimum, which is (52, 5, 11) in this paper. And the corresponding stator loss is 4.3 kW.

Range of optimization variables.

6

In this paper, three variables are selected to minimize the stator loss. The influences of three variables are also introduced, which are different compared with low-speed motors. REFERENCES

Stator loss of each design.

Number

Winding loss (kW)

Core loss (kW)

Stator loss (kW)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2.46 4.64 2.87 2.12 3.49 4.16 2.06 4.67 6.43 3.34 15.56 4.59 4.62 1.54 2.18

2.54 2.02 2.94 2.56 3.25 1.93 3.43 1.43 1.77 2.36 3.49 3.05 3.37 3.98 4.62

5 6.66 5.81 4.68 6.74 6.09 5.49 6.10 8.20 5.70 19.05 7.64 7.99 5.52 6.80

CONCLUSION

design space. The rest 8 points are the vertexes of the design space. Then, total stator loss of each design can be calculated, of which the result is shown in Table 4, in which the winding loss is calculated by analytical model and the core loss is calculated by

[1] G.R. Slemon and L. Xian “Core losses in permanent magnet motors”, IEEE Trans. Magn., vol. 26, no. 5, pp. 1653–1655 1990. [2] L. Ma, M. Sanada, S. Morimoto and Y. Takeda “Prediction of iron loss in rotating machines with rotational loss included”, IEEE Trans. Magn., vol. 39, no. 4, pp. 2036–2041 2003. [3] Y. Huang, J. Dong, J. Zhu and Y. Guo “Core loss modeling for permanent-magnet motor based on flux variation locus and finite-element method”, IEEE Trans. Magn., vol. 48, no. 2, pp. 1023–1026 2012. [4] X. Nan and C.R. Sullivan, “An improved calculation of proximity-effect loss in high-frequency windings of round conductors,” in 34th Annual IEEE Power Electronics Specialists Conference, 2003. [5] Gonzalez, D.A, Saban, D.M. “Study of the Copper Losses in a High-Speed Permanent-Magnet Machine with Form-Wound Windings”, Industrial Electronics, IEEE Transactions on Volume: PP, Issue: 99. [6] P.N. Murgatroyd “Calculation of proximity losses in multistranded conductor bunches”, IEE Proceedings, vol. 36, pp. 115–120 1989. [7] R. Wojda, and M. Kazimierczuk, “Analytical Optimization of Solid-Round-Wire Windings,” IEEE Trans. Ind Electron., vol. 60, no. 3, pp. 1033–1041, Mar. 2013. [8] S. Iwasaki, R. Deodhar, Y. Liu, A. Pride, Z.Q. Zhu and J. Bremner “Influence of PWM on the proximity loss in permanent magnet brushless AC machines”, IEEE Ind. Appl. Soc. Ann. Meeting, pp. 1–8 2008. [9] M. Popescu, D.G. Dorrell, “Proximity Losses in the Windings of High Speed Brushless Permanent Magnet AC Motors With Single Tooth Windings and Parallel Paths”, IEEE Transactions on Magnetics, vol. 49, no. 7, July 2013.

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[10] Jussi Lähteenmäki, Design and Voltage Supply of High-Speed Induction Machines, Ph.D. dissertation, Helsinki University of Technology, Finland, 2002. [11] X. Li, Q. Chen, J. Sun, Y. Zhang and G. Long “Analysis of magnetic field and circulating current for HTS transformer windings”, IEEE Trans. Appl. Supercond, vol. 15, no. 3, pp. 3808–3813 2005. [12] Fang, J, Liu, X, Han, B, Wang, K, “Analysis of Circulating Current Loss for High Speed Permanent Magnet Motor,” to be published, DOI: 10.1109/ TMAG.2014.2302412.

[13] K.Y. Hwang, J.H. Jo and B.I. Kwon “A study on optimal pole design of spoke-type IPMSM with concentrated winding for reducing the torque ripple by experiment design method”, IEEE Trans. Magn., vol. 45, no. 10, pp. 4712–4715 2009. [14] K.Y. Hwang, S.B. Rhee, B.Y. Yang and B.I. Kwon “Rotor pole design in spoke-type brushless DC motor by response surface method”, IEEE Trans. Magn., vol. 43, no. 4, pp. 1833–1836 2006. [15] Y. Choi, H. Yoon and C. Koh “Pole-shape optimization of a switched-reluctance motor for torque ripple reduction”, IEEE Trans. Magn., vol. 43, no. 4, pp. 1797–1800 2007.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Iterative Adaptive Algorithm based on the cross covariance matrix of acoustic pressure and particle velocity C.R. Zhang, J.F. Cheng & B.L Ma Department of Weaponry Engineering, Naval University of Engineering, Wuhan, China

ABSTRACT: A vector hydrophone measures the acoustic pressure and particle velocity at a single point in space, we propose a novel Iterative Adaptive Algorithm (IAA) to estimate the direction of arrival based on the cross covariance matrix of acoustic pressure and particle velocity, called PVIAA. Due to the noise of each channel being independent the cross covariance matrix is not affected by the noise. The algorithm is able to cope with very low snapshot numbers and deal with the correlated sources. Simulation results are presented to compare the performance of this method with the original IAA and L1_SVD, and this method is shown to outperform the existing approaches. Keywords: 1

DOA; cross covariance matrix; IAA; vector hydrophone; L1_SVD

INTRODUCTION

The estimation of Direction Of Arrival (DOA) is one of the most important topics of the array signal processing, which playa a fundamental role in many applications involving radar, sonar and seismic sensing. The traditional Delay-AndSum (DAS) method is the most basic approach to estimate the DOA of the targets; however the DAS method is limited by the Rayleigh limit, so it suffers from the low resolution and high sidelobe level. In recent decades, many advanced techniques such as MVDR and MUSIC for localization have achieved superresolution, but these methods cannot deal with the related sources, and need a large number of snapshots. But in a real situation, these demands always cannot be satisfied; for example, when the targets are maneuvered, the available number of snapshots is low; when we need locate the targets in the shallow sea, because of the multipath effect, there will be some coherent acoustic sources. Many extensions to these algorithms have been proposed, like the spatial smoothing algorithm and APES, etc. Recently, the sparsity-based method has been applied in DOA estimation widely; this method has a very high resolution, and can deal with a few snapshots, but there will be a bias of estimations when the sources are spaced closely [1], and it requires large computation times. Iterative Adaptive Algorithm (IAA) is another method which can deal with a few snapshots, and the estimation is accurate [2]; its resolution is the same as MVDR when the number of snapshots is large enough.

Both algorithms are able to deal with coherent sources. A vector hydrophone measures the acoustic pressure and all three components of acoustic particle velocity at a single point in space [3]. Similarly, as with the pressure hydrophone, MVDR, MUSIC, sparsity-based methods can be applied in a vector hydrophone. Due to the noise of each channel being independent, the cross covariance matrix is not affected by the noise, and the vector algorithms are shown to outperform the original method. According to the relation of acoustic pressure and particle velocity, we propose an iterative adaptive algorithm based on the cross covariance matrix of acoustic pressure and particle velocity. This article is organized as follows. In section 2, we describe the mathematical model for DOA estimation and the measurement of vector hydrophone. In section 3, we state the original IAA and PVIAA. In section  4, we give some numerical simulations and conclude the paper in section 5.

2 2.1

MATHEMATICAL MODEL Direction of arrival problem

The goal of source localization is the estimation the DOA of sources based on the data received by an array consisting of a number of sensors whose positions are known exactly. Under the farfield assumption, K narrow band signals at location { 1,θ 2 ,...,θ K } impinging on a linear uniform array

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with M sensors, the number of snapshots is N, the received data can be represented as: As( ) V( ), n = 1, 2, ..., N

y( )

(1)

where As(n) are signals of K sources, V(( ) ∈  M ×1 is noise, A [a(θ1 ), ) a( 2 ), ..., a( K )] ∈  M × K is the array manifold matrix; if we treat the first sensor as reference, the steering vector is: a( ) = ⎡ − ⎣⎢1, e

2.2

j 2π d sin( i (θ ) λ ,

e

j 2π 2 d sin( i ( ) λ , ...,

e

−

T j 2π Md sin(θ )⎤ λ ⎥

{y p ( n )yvH ( n )}

R pv

(8)

Using (6), (7) and the independence of noise and signals, (8) can be written as: E{As( n )s H ( n )AvH }

R pv

{Vp ( n )VvH ( n )}

(9)

Because the noise from different channel is independent, so the cross covariance matrix is not affected by the noise:

⎦

E{

(2)

R pv

H p ( n ) v ( n )} = H

E{

(n)

0

(10)

H v }

( n)

Measurement model of vector hydrophone

A vector hydrophone has four channels: acoustic pressure and all three components of acoustic particle velocity, according to the acoustic theory, under the far field assumption, the relationship of acoustic pressure and particle velocity can be represented as: v( r, t ) = −

p( r,t ) u ρ0 c

(3)

T where u = [cos θ cosψ , sinθ cosψ , sinψ ] , v( r, t ) and p( t ) are acoustic pressure and particle velocity, ρ0 is the density of medium, c is the acoustic velocity, so ρ0c is a constant. θ , ψ are the azimuth and elevation of source respectively, if we only consider the X-Y plane, u = [cos θ , sin i θ ]T , and when there are K signals impinging the array, the receive data of reference sensor is [4]:

3.1

The original Iterative Adaptive Algorithm

Iterative Adaptive Algorithm (IAA) is a method— based Weighted Least Squares (WLS) estimation; it estimates the signal from all DOAs, and the estimated value was updating through the iterative method. When the algorithm estimates the signal from θ j , the cost function of WLS is: N

∑ y(

)

j(

)a( a( j )

n =1

2 Q−1 (

j

)

(11)

where x Q 1  x H Q−1x , Q−1( j ) is the noise covariance matrix: 2

R−

j a( j )a

H

( j)

(12)

(4)

where y p ( n ) , yvx ( n ) , yvy ( n ) are acoustic pressure and particle velocities of X and Y velocity channels respectively, if we combine the X and Y channels, we can get the analytical velocity: yv ( n ) = yvy ( n ) − jy yvx ( n ) = e − jθ y p ( n ) + nv

(5)

Just as in the subsection A, the pressure and velocity can be represented as: yp( )

As( ) Vp ( n ), n = 1, 2, ..., N

(6)

yv ( )

Av s( ) Vv ( n ), n = 1, 2, ..., N

(7)

jθ

IAA BASED ON THE CROSS COVARIANCE MATRIX

Q 1( j )

⎡ yp ( n) ⎤ K ⎡v p ( n ) ⎤ ⎡1 ⎤ s ( n ) + ⎢v ( n ) ⎥ ⎢ y ( n)⎥ = ⎢u ⎥ k ⎢ yvx ( n ) ⎥ k∑ ⎢vvx ( n ) ⎥ =1 ⎣ k ⎦ v ⎣ vy ⎦ ⎣ vy ⎦

jθ

3

− jθ K

where Av [ a(θ1 ), e a( 2 ), , a( K )]. Then we can define the cross covariance matrix of acoustic pressure and particle velocity:

where R is the data covariance matrix, Pj is the signal power at angle θ j , s j ( n ) is the signal waveform that can be estimated by solving the cost function (11): s j ( n ) =

a H ( j )Q−1( j )y( n ) a H ( j )Q−1( j )a( j )

(13)

Using the matrix inversion lemma, (13) can be written as: s j ( n ) =

a H ( j )R −1y( n ) a H ( j )R −1a( j )

(14)

And then Pj can be calculated as: 2 1 N Pj = ∑ s j ( n ) N n =1

(15)

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After estimating the signals at all angles on the scanning grids, we update the covariance matrix by: P = diag( P ) R

(16)

 H APA

(17)

where A is different with A, it contains the steering vector at each angle on the scanning grid. The covariance matrix is fed into the next iterative procedure; the algorithm is summarized in Table 1. In the first iteration, the initial R is set to identity matrix and the WLS is reduced to LS—this iteration is the same as DAS. The empirical experience is that IAA does not provide significant improvements in performance after about 15 iterations [5].

3.2

The IAA, based on the cross covariance matrix (PVIAA) replaces R by R pv which is calculated by the product of the estimations of acoustic pressure and particle velocity, the PVIAA algorithm is summarized in Table 2. Where p j ( n ) , v j ( n ) are the estimations of acoustic pressure and particle velocity respectively, Pj is the acoustic energy flow at the angle θ j , we calculate R pv by: R pv

(18)

NUMERICAL EXAMPLE

The IAA Algorithm.

Initial: R = I Repeat For j

1 2

s j ( n ) = 1 Pj = N

H

a

N NK

H

a

In this section, we compare the PVIAA with the original IAA and L1_SVD algorithm, and analyze the affect of the number of snapshots, SNR and correlation of sources.

(

j )R

−1

y( n )

−1

(

j )R a (

N

∑ s j ( n)

,n

1, 2,

,N

4.1

j)

n =1

1

2

 H; APA

T

NK

] ); R

Until (convergence)

The PVIAA algorithm.

Initial: R pv = I Repeat For j 1 2

N NK H

p j ( n ) =

v j ( n ) =

1 Pj = N

a (

j ) R pv

H

j ) R pv

a ( e

− jθ j

H

a (

H

a (

y p (n)

−1

a (

j ) R pv −1

j)

4.2 Coherent sources

−1

pv

,n

1, 2,

,N

j)

−1

yv ( n )

(

,n

1, 2,

j)

(

,N

We compare the estimation of three coherent sources with three unrelated sources, and N = 100, SNR = 0 dB. Figure 2 shows that the IAA and L1_SVD both have the estimation bias problem, PVIAA can get the accurate results.

N

∑ p j ( n)v*j ( n)

4.3 RMSE approximation

n =1

End P = diag

Different number of snapshots

We consider a uniform linear array with M = 16 sensors and half-wavelength spacing, and three uncorrelated sources at 60°, 82° and 90°, SNR is set to 10dB, the sparsity parameter p = 2, the scanning grid is from 0° to 180°, the scanning step is set to 1°. Figure 1 shows the estimations of DOA of three algorithms in different number of snapshots. Figure  1  shows that IAA and PVIAA have similar results when N = 1, but with the increasing of N the resolution of PVIAA is better that IAA, and the L1_SVD encounters location bias problem.

2

End P = diag ([ P , P , ..., P

Table 2.

 H = Adiag{[  ,  , ...,  ]T }A H APA 1 2 NK

Because the noises from pressure channels and velocity channels are independent, the cross covariance matrix can inhibit the affect of noise.

4 Table 1.

IAA Based on the cross covariance matrix

 

Until (convergence)



); R

 APA ; H

pv

To compare the accuracy of estimations and stability of three algorithms, Figure 3 verifies the RMSE of different algorithms; the sources are at 82.51° and 90.51°; the results are obtained by 100 Monte

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Figure 1.

The results of three algorithm in different number of snapshots.

Figure 2.

The results in different correlated conditions.

Carlo simulations in each SNR, RMSE is calculated by: RMSE =

Figure 3.

RMSE of different algorithm.

Mon 2 1 ∑ ∑ ( j − 2 × Mon i =1 j =1

j)

2

(19)

where Mon is the number of Monte Carlo simulations, we can insight from Figure 3 that PVIAA has a better RMSE in different SNR, so PVIAA is more accurate and more robust.

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5

CONCLUSION

This paper has presented a novel IAA based on the cross covariance matrix of acoustic pressure and particle velocity. The algorithm is able to work under severe snapshot limitations and for uncorrelated, partially correlated, and coherent sources, can be applied to locate maneuvering target in shallow water. REFERENCES [1] Malioutov, D., M. Cetin and A. Willsky, A sparse signal reconstruction perspective for source localization with sensor arrays. Signal Processing, IEEE Transactions on, 2005. 53(8): p. 3010–3022.

[2] Yardibi, T., et  al., Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares. Aerospace and Electronic Systems, IEEE Transactions on, 2010. 46(1): p. 425–443. [3] Pannert, W., Spatial smoothing for localized correlated sources—Its effect on different localization methods in the nearfield. Applied Acoustics, 2011. 72(11): p. 873–883. [4] Hawkes, M. and A. Nehorai, Acoustic vector-sensor beamforming and Capon direction estimation. Signal Processing, IEEE Transactions on, 1998. 46(9): p. 2291–2304. [5] Stoica, P., L. Jian and L. Jun, Missing Data Recovery Via a Nonparametric Iterative Adaptive Approach. Signal Processing Letters, IEEE, 2009. 16(4): p. 241–244.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A method to establish a continuous operational reference station in urban districts S.B. Wang, X.J. Du, H.J. Li & W.P. Xu Beijing Institute of Technology, Beijing, China

ABSTRACT: With the development of GPS, CORS (Continuous Operational Reference System) has played an important role in application areas in cities. Setting good reference stations that can supply observation data of high quality is very difficult with high buildings and electromagnetic interference sources standing nearby. To solve the problem, a method to establsihreference stations as well as a way, based on observation data collected, to monitor the station are proposed in this paper. Keywords: 1

GPS; CORS; reference station; observation data; urban district

INTRODUCTION

specification has been published to establish the basic requirements for setting a reference station:

GPS technology is becoming more and more important in urban surveys with its fast development. Setting Continuous Operational Reference System with the technology of network RTK (Real Time Kinematic) in urban districts has become the hot topic in GPS application area. The core concept of the CORS is the observation data supplied by the reference station, for which reason building a station that can supply data of high quality is very important. In urban districts, satellite signals will be kept out or refracted by tall buildings. What’s more, the radio station and cell tower will be electromagnetic interference sources for the satellite signals, which will make the station unable to receive complete and correct information from the satellite, resulting in bad observation data. In this paper, a method to set reference station in urban districts is proposed and indicators are listed to assess whether the position selected is suitable for an antenna. This paper consists of three parts. In the first part the method is proposed, and then the indicators for the observation data are listed to test whether the point selected is suitable to build an antenna, in the last segment an example is expressed to verify the method proposed useful and practical. 2 2.1

SELECTING PROCEDURES AND ASSESSMENT National specification

The global navigation satellite system continuously operating reference station network construction

a. Observation environment’s requirement 200  meters or more away from the buildings and objects that can cause a multi-path effect or electromagnetic interference; satellite visible angle must be more than ten degrees. b. Geological condition Stations belonging to the National reference station should be set on the stable ground, away from the district that is unstable or easily drowned by the rainwater. Regional station can be set depending on its environment condition. c. Maintenance requirement Network supplied for transporting observation data; stable and reliable power to keep the station running continuously; convenient in transportation. The standard above is not specific enough to guide an engineer to complete the station, for which reason, on the basis of the specification, a more specific way is proposed below. 2.2

Procedures to set a station

1. Selecting proper districts to make the reference net a good geometrical configuration Reference stations have an effective range of fifty kilometers, depending on which we can choose three or four districts. The districts selected must be in good geometry (geographic????) configuration. Now the main network, RTK Technologies, are VRS (Virtual Reference Station), MAC (Master-Auxiliary Concept), CBI (Combined Bias Interpolation), when supplying

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observation data for rover receiver, all this three algorithms need data that comes from more than one station. So a good geometry configuration will make it possible for rovers to get data from more reference stations, and it will enhance the performance of the rover receiver as well. 2. Select the antenna point Make a filed research on the districts selected in the last step to make sure that, there are no buildings tall enough to shield the GPS signals and also that there is no radio station and cell tower that can affect the GPS signal. It is better to collect some photos: they may be needed when analyzing. Make sure also that there is stable and reliable power supply to keep the station running continuously. 3. Collect and process observation data It is the most important step in the entire excercise. This step will decide whether the district selected will be a good point for a station. In this step we should collect observation data from the antenna founded in the last step; this data must be able to reflect the satellite signals completely. Consider the situation that the station runs continuously, the ionosphere error and multipath error change with the satellite elevation, satellite position, ionosphere density periodically, and the GPS satellites’ period of revolution is 11 hours and 58 minutes, while the earth’s rotation is repeated every 24 hours. So the satellite distribution will repeat every 23 hours and 56  minutes. And one day’s observation will be good enough to evaluate the environment. After collecting the data, process it and analyse the result to draw a conclusion. 4. Data analyse The quality of the station’s observation data is relevant to the multi-path error, cycle slip, integrity of the data, validity and carrier-to-noise ratio. TEQC that has been developed by the UNAVCO Facility is used to analyse the observation data. 2.3

Criterions to set a station

Multi-path error: Multi-path error is related to the environment of the station and the height of the antenna. After analyzing the observation data, the TEQC software will give out two parameters MP1 and MP2 that are assumed as the comprehensive index the multi-path effect on pseudo-range and phase in L1 and L2 respectively: 2 ⎤ ⎡ ⎡ 2 ⎤ MP P1 = ρ1 − ⎢1 + ⎥ * Φ1 + ⎢ α − 1 ⎥ * Φ 2 ⎣ α − 1⎦ ⎣ ⎦

(1)

⎡ 2α ⎤ ⎡ 2α ⎤ MP P2 = ρ2 − ⎢ ⎥ * Φ1 + ⎢ α − 1 − 1⎥ * Φ 2 α − 1 ⎣ ⎦ ⎣ ⎦

(2)

where ρ1 and ρ2 represent pseudo-range of L1 and L2 respectively, Φ1 and Φ2 represent carrier-phase of L1 and L2. In addition

α=

f1 2 f2

(3)

IGS’s (International GPS Service) data analysis shows that, for multi-path error, two third IGS’s reference stations’ MP1 is lower than 0.5, while MP2 lower than 0.75. We can use this as a criterion. Cycle slip analysis: Cycle slips only emerge when the PLL (Phase Locking Loop) failed to track the signal. The slip is counted according to the ionosphere residual error; the calculating formulae is shown below: IOD D1

1 ⎡ ( α −1⎣

IOD D2

α ⎡ ( α −1⎣

1

) (

2 j

) j −1 ⎤⎦ / (t j − t j

1

)

(4) 1

) (

2 j

) j −1 ⎤⎦ / (t j − t j

1

)

(5) tj means an epoch, IOD1 and IOD2 are the flags, when the flags are higher than the threshold value the slip number will be added. The analysis result given by TEQC will introduce a parameter o/slps, called ratio of the observation and slip to show the cycle slip, while we use another parameter to describe it. CSR =

1000 o/slps

(6)

More than half of the IGS station’s CSR is no more than 5, which can be used as a criterion to evaluate the station selected. Observation integrity: The actual epochs received divided by theory epochs makes the integrity, and epochs can be calculated by the satellites available plus the time they remain. And the integrity value must be higher than 95%. Observation validity: When an observation contains the information of C/A code, P code, L1 phase, L2 phase and C/N, the data is said to be validity, otherwise it is invalid. The value of validity is expressed by the observation completed and observation deleted. If the reference station can run stable continuously, its observation invalidity rate must be lower than 5%. C/N: Carrier to Noise Ratio (C/N) is an important indicator for the environment evaluation. According to the Interface Control Document (ICD), GPS signal’s power is said to be −130 dbm, while the noise power is about −204 dbm. So the

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ideal C/N is 44, after considering the loss inside receiver, 40 is good enough for a receiver’s output. 3

THE ANALYSIS OF THE EXAMPLE

After detailed research, the roof of the China Transportation Telecommunication building is selected to be the test point. For there are no tall buildings around the roof area, making it easy to receive satellite signals. The reference station is immune to electromagnetic interference as there are no no cell and radio towers nearby. Besides, stable power and convenient traffic condition make the station easy to maintain. After the investigation and survey in this environment, one day’s RINEX ephemeris is collected which is then analysed by the TEQC software; the result is shown in the picture below. 3.1

Figure 3.

Analysis result of the MP2.

Figure 4.

Analysis result of the ionosphere error.

Analysis on multi-path error

Figure 1 shows that the MP1 and MP2 are 0.48 and 0.33 respectively, Figure 2 and Figure 3 below are the multi-path error in some epochs, which show that the multi-path error is not more than 2 meters in most time. After being compared with the IGS results, the selected point’s multi-path error is good enough. 3.2

Analysis on cycle slip

Figure  4 and Figure  5 are some epochs’ ionosphere error and its changing ratio; the slips on

Figure 1. Sum analysis result of the RINEX data given by TEQC.

Figure  5. Analysis result of the ionosphere error’s changing ratio.

Figure 2.

Analysis result of the MP1.

the second figure and the peak on the first figure mean a cycle slip. From Figure 2, it can be seen that the o/slps value is 23583, which will be 0.04 after being changed to CSR. Compared with the IGS data, o/slps do not happen very often.

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3.3

Analysis on integrity

3.5

The Figure 6 shows the analysis results on integrity, and after calculation the result is 99.97%, which is perfect for a reference station. 3.4

Analysis on C/N

From the Figure  8, it is easy to find that most epochs’ C/N is higher than 44, which means that the point selected for antenna is perfect to set up a reference station.

Analysis on observation validity

From the Figure 7, it can be seen that the validity is 100%, which is perfect!

Figure 6.

Result about the integrity by TEQC.

Figure 7.

Result about the validity by TEQC.

Figure 8.

C/N of some satellites in some epochs.

4

CONCLUSION

A method to set reference station in urban district is proposed, and an example is given to verify the method. In the analysis result of the example, the multi-path error, cycle slip, observation integrity, validity and C/N are qualified to be a reference station. So the conclusion can be drawn that the method is useful and practical. REFERENCES [1] Guan Jian An 2010. Application and Certain Technical Question Research of Precise Positioning Service Based on CORS. [2] Li Jun & Wang Jiye 2006. Quality Checking and Analysis on GPS Data in Northeast Asia. Geomatics and Information Science of Wuhan University 2006(3):209–212. [3] Liu WenJian & Yang Li 2008, Research on CORS Reference Station Location. Geospatial Information 2008(6):6. [4] Zhan Fan 2009. Distribution Design and Experiments of FJCORS Reference Station. Geospatial Information 2009(7):74–76. [5] Zhao Guo Qiang & Sun HanRong 2009. Quality checking and Analysis for Crustal Movement Observation Network of China. Urban Geotechnical Investigation & Surveying 2009(3):73–75.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The research on the leak repairing method of the recoil mechanism N. Li, W. Jiang, H.P. Guo, S. Wang & L.M. Chen Wuhan Mechanical Technology College, Wuhan, Hubei Province, China

ABSTRACT: The recoil mechanism is the “heart”, and it is the vital part, of the artillery. The leakage of gas and liquid of the recoil mechanism is researched in the paper. In particular research in this paper focuses on the liquid leakage of the recoil mechanism wall. The paper also studies the leak repairing method I which is significant for recovering the fighting force of the equipment quickly. Keywords: 1

the recoil mechanism; the leakage of gas and liquid; the leak repairing

INTRODUCTION

The recoil mechanism is the “heart”, and it is the vital part of the artillery; and the 60%∼70% of the faults are about the recoil mechanism. The recoil mechanism consists of the recoil brake, including the recoil fluid and the counter recoil, which also includes the recoil fluid and the high pressure gas. The main technical requirement of the recoil mechanism are special liquid and gas. There should be no leakage whatsoever of either. Also the sealing element should be reliable. However, in the shooting, the recoil mechanism works under high pressure in an environment of high temperature. Leakage of gas and liquid is the most important and the most common fault. According to statistics, the number of the faults for the entire artillery is about 50%, and seriously effects combat readiness and mission success. It results in the inconvenience of the use and support of the equipment and the personnel training—thus affecting combat effectiveness of the armed forces. Therefore, the research on the leak repairing method of the recoil mechanism is of great importance and significance to the Repair guarantee of the artillery and the effectiveness in combat of the armed forces. 2

THE FAULT PHENOMENON AND REASON OF THE LEAKAGE OF GAS AND LIQUID OF THE RECOIL MECHANISM

2.1

The copper obturator ring of the thread joints is hardened

2.2

Failure cause: The pressure control mainly in the maintenance is not in sync and the copper obturator ring is hardened because of over-use in high pressure load; this can cause deformation, making the copper obturator ring lose its sealant quality, resulting in leakage. The rubber element loses its elasticity, and the surface is sticky or hard and brittle, and the cracks and exfoliation arises

Failure cause: The rubber element—for example the leather ring, the leather and so on is ageing, and the recoil mechanism is closed by the rubber element; if the rubber element is contaminated by the acid, alkali, oil and other corrosive substances in the repair process, its swells and hardens, which reduces its strength and elasticity, reluting in in the leakage of the recoil mechanism. 2.3

Failure cause: Mainly by the improper operation, the conical surface and the room of the opening and closing lever are damaged, and thereby the conical surface and the room of the opening and closing lever becomes loose resulting in the leakage of gas and liquid. 2.4

The faults of the leakage of gas and liquid of the recoil mechanism are mainly due to the following reasons.

The conical surface and the room of the opening and closing lever makes it loose

Acid free welding breaking

Failure cause: Poor protection of the acid-free weld in the use of the process, and the solder joints being subjected to the physical impact or the

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chemical corrosion, result in the leakage of gas and liquid from the thread junction. 2.5

The liquid leakage of the recoil mechanism wall

Failure cause: The recoil mechanism wall is damaged mainly by shrapnel in the battlefield, resulting in the occurrence of the leakage of gas and liquid. 3

THE CONVENTIONAL METHODS OF THE TRAP

The leakage of gas and liquid of the recoil mechanism can be eliminated by replacing the damaged components such as—the copper obturator ring which has hardened, the rubber component which has lost its effectiveness, the damaged conical surface of the opening and closing lever and so on. But if the recoil mechanism wall is damaged, and there are no spare parts being used for their replacement, how does one solve the problem? The several trapping methods of the recoil mechanism wall are. 3.1

The welding mending method

As shown in Figure 1, the welding mending method entails the welding of the point of leakage with the electrode or the iron directly to ensure the leaking stops [1]. This method is suitable for micro seepage or small quantities of leakage and the pressure is big or can be decreased: the welding ignition can be put

out by fire extinguishers,: it also works well when the tube wall corrosion of the leakage point is not serious or the corrosion of the tube wall can be sustained with the strength generated by the welding: this, of course, does not apply to a flammable and combustible situation. The welding mending method is used in the case of direct welding or the Pasted plate welding to the leak treatment generally. The method is mainly used in weld inclusions and abscess or stress crack micro leakage locations. When being welded the copper hammer beats the welding meat to make the leak shrink before welding, and the welding effect is more obvious. 3.2

The capping remedy method

As shown in Figure 2, the capping remedy method entails the top pressure support or the fixed screw, being set in the tube wall leakage positions; with this method t the top pressure sealing gasket seals the leakage [2]. It is applicable in the case of tube wall leakage, and when the leakage pressure is not great, and also when hot welding can not be taken with pressure. The pressure tool used is based on the actual shape and the size of the pressure tool leak. This method is mainly applied to the corrosion leak site which is easy to operate. Firstly the leakage is stopped by using a filler, the wedge, the lead, the strong magnet plugging and so on, then the top pressing tool is set, then the sealing pad section is installed, and lastly the pressing mechanism is used to plug the leak. 3.3

The hoop trap method

As shown in Figure  3, the hoop trap method is the method in which the leakage is plugged by the

Figure 1.

The welding mending method.

Figure 2.

The capping remedy method.

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Figure 3.

The hoop trap method. Figure 4.

Automatic strapping note glue trap method.

Figure 5.

The structure of note glue nut.

pipe hoop mounted to the tube wall. This is applicable in the case of a tube wall leakage, also when its leakage pressure is big, and hot welding cannot be taken with pressure. According to the leakage of the tube wall dimensions, the appropriate sealing hoop has to be made for the leak treatment. The method is mainly applied in the case of an inconvenient leakage point or if it has an irregular shape. 3.4

The fixture note glue trap method

As shown in Figure 4 and Figure 5, the principle entails that the sealing structure is established in the dynamic operation of the medium and realised by the fixture and the sealants [3]. The fixture is the important tool of the fixture note glue traps technique, and must meet the necessary requirements which are: (1) Sufficient mechanical strength and stiffness. (2) Certain amount of space to facilitate the sealant being infused, and the new sealing structure formed. (3) The number of injection holes which are appropriate and reasonably structured. (4) There must be appropriate clearance between both the original leak and the fixture, and the running glue being used. Application of the fixture note glue trap method is most widely used: it is suitable to the medium of raw gas, purified gas, steam, air, acid gas factory, industrial water, rich solution and so on. This method is also the future development direction of the pressure sealing technology, and many enterprises and research institutions are involved in its research and development. 3.4.1 The caulking trap method The caulking trap method is mainly suitable for defects such as the sand hole and the small hole and so on. The hammer, the punch and other tools beat the leak around the body, and the tube wall of the metal takes the place of the plastic deformation by the impact force, and the leak is stopped with the plugging method. The method is suitable when

the leakage quantity is not great, or the internal pressure of the media is not too much, or when the leakage of the tube wall lacks a certain thickness and so on. The force of the hammer in use to prevent leaks should not be to hard else it will create vibrations. 3.4.2 The repair technique of the cold welding stick The repair technique of the cold welding stick is the method by which the leak point is plugged by the adhesive curing characteristics of the special material. The features of this method include: convenient construction without fire, good plasticity, strong adaptability, high strength after curing, excellent durability and so on [4]. When repairs are done by welding, it needs at least two people. Firstly a piece is cut to the size of the damaged part from the cold welding mud rod, then the piece is kneaded and extruded repeatedly, and mixed together to form a kind of rubber mud. Then it is flapped repeatedly, and shaped like the damaged part: it has a thickness of 5 mm which is then finally pressed into the leak hole. The second person in this method is needed for grinding the cold welding mud rod with the hacksaw, after which

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he removes residual impurities on the surface using a clean cloth. The glass aluminum tape being cut is stuck on the rubber mud and the excess cut away. Again two people are needed in the final stages in which one person presses the rubber mud and the other daubs sealant in the corresponding edge, 2 minutes later, using the hand along the edge of the sealant to ensure there is no feeling of mucosity; it is reinforced using the cold sweat reinforcing agent, before the entire operation is completed. 4

CONCLUSION

This paper introduces several recoil mechanism trap methods, and through the discussion and the research, suggests the method that is feasible and effective for the recoil mechanism repair work; this

saves large numbers of the maintenance resources, and is significant for recovering the fighting force of the equipment quickly, and also improves the fighting capacity of the military.

REFERENCES [1] Hu Yi-wei. Note the Agent Type Technique with Pressure Seal [M]. Beijing, China Machine Press, 1998. [2] Ji Min-hua. The Application of the Pressure Seal Technique [J]. Shanghai Chemical Industry, 1999, 24(8). [3] Li Xin, Yang Mei. The Research on the Objectoriented Design Method of the Pressure Seal Clamp [J] Lubrication Engineering, 2010, 35(8). [4] Huang Zhi-ming. The Key Technique of the Pressure Seal [J]. Fujian Chemical Industry, 2003(3).

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Image retrieval research based on feature points and affine invariant moments B.Q. He & Z.M. Wang Nanchang Institute of Technology, Nanchang, China

ABSTRACT: Image retrieval is the latest rising technology thanks to the great development of computer and digital information processing technology. But, how to search specific kind of images from the multitude of resources now available is the key technology. Now, retrieval, based on the multi-data is a hot and complex research point. In this paper, a new image retrieval algorithm is proposed, which is based on the study of feature points and affine invariant features. This algorithm uses both local features and global features, which can effectively overcome image translation, rotation, scaling, and affine change, and also has higher retrieval precision and efficiency compared to traditional algorithms. Keywords: 1

image retrieval; affine invariant feature; spatial feature point; retrieval precision

INTRODUCTION

The earlier image retrieval algorithm is mainly based on text retrieval, and later, on color, texture, shape and content. Because of the rich image content, a simple algorithm, without manual intervention and content-based image retrieval technology, is widely used presently in the image field. It can be divided into different ways, based on the retrieval method, such as text retrieval [1–2], texture feature retrieval [3–4], and shape retrieval [5–6]. But by only using one kind of feature for image retrieval, one discovers the precision and efficiency are not ideal; so the existing algorithms are mostly based on feature’s fusion. After researching the existing algorithms at home and abroad, this paper proposed a new image retrieval method, which is based on the fusion of the feature points and invariant moments. 2

the matching of Harris-Laplace points, then one uses the Euclidean distance to complete the MSA’s matching. Finally, one takes the weighted formula to finish image retrieval. 3

ALGORITHM PROCESS

Image retrieval process includes pre-treatment, feature extraction, description and matching.

ALGORITHM DESIGN

In this paper the algorithm implementation process is as shown in Figure 1. First, the image should be pre-processed—including filtering and graying. Then one extracts the Harris-Laplace corners [8] and MSA moments [9]. After feature extraction, the next step is feature matching. Annular histogram [10] is a good method of describing corners, and the Delaunay Triangulation net [11] is also a good method to describe corners. In this paper, through the combination of annular histogram and DT net to realise

Figure 1.

Algorithm flowsheet.

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3.1

Image pre-processing

Due to the fact that a colour image has large amounts of data and noise, it is not easy to handle, so the first step is graying. This paper has used the weighted formula in article [12] for graying, and then used 3 × 3 median filtering template to denoise. The pre-treatment result is shown in Figure 2. 3.2

Feature extraction

In this study, the extracted features include Harris-Laplace corners and MSA affine invariant moments, by adopting the algorithms of reference literature [8] and [9]. 3.2.1 Harris-Laplace corner extraction The process of extracting Harris-Laplace corners is a complex process. Firstly, use the formula (1) to calculate Gauss scale space construction of different scale factors: L ( x, σ n )

I (x ) * G (

(1)

n)

Then use formula (2) to calculate Harris secondorder matrix for each layer of scale space. ( μx ,σ I ,

2 D ) σ DG (

⎤ ⎥ 2 ⎥ y( , D ) ⎦

x

y(

,

D)

(2)

where L2x Lx × Lx L2y = Ly Ly , Lx Ly = Lx Ly , σD σI. Next, calculate the Harris corner response in scale-space image: det( μ ( x,σ l

D )))) −

k ttrace 2 ( μ ( x,σ l ,

D ))

3.2.2 MSA feature extraction The MSA feature has good translation, rotation, scale invariance and excellent full affine invariance. Literature [9] introduced the MSA affine invariant feature extraction algorithm. In the image any fourth points that can be expressed by three arbitrary points are not collinear, as formula (4):

β (X 2

X0 )

(4)

where (α , β ) is coefficients, X 0 is the origin point. Define f ′ X ′ ) = f TX T t ), Uαβ can be expressed as formula (5): TUαβ = TX X 0 + α TX X1 TX X0

β (TX 2 − TX X 0 ) (5)

The equation stays the same in spite of random affine transformation; therefore the coefficient was defined as MSA affine invariant moments. 3.3

Feature description

3.3.1 Circular histogram Circular histogram makes the image divide into a plurality of rings; then count the number of corner points in each ring to describe the corner feature. Suppose f x, y ) is one digital image expression, ( xi , yi ) is the centroid for all the image pixels in the ring I, and can be expressed as: xi =

Pretreatment.

X 0 + α X1 X 2

(3)

where k = 0.04 ~ 0.06 . In each layer of scale image, if the point corner response value is greater than the other 8 points’ value of the 3*3 window and is greater than a certain threshold, it is a Harris-Laplace corner.

Figure 2.

Harris-Laplace corners.

Figure 3 is the extraction result of Harris-Laplace corners.

Uαβ I)

⎡L2x ( ,σ D ) ×⎢ ⎢Lx Ly ( ,σ D ) ⎣

R

Figure 3.

1 Ai

∑

( x , y )∈Ai

x yi =

1 Ai

∑

y

(6)

( x yy)∈Ai

After normalisation, the annular histogram has translation, rotation and scale invariance. As you can see in Figure 4, the number of corners in each ring will stay the same.

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distance [16] and others. Euclidean distance is one most common used algorithm; because of simple calculation, easy implement, and high efficiency, it is widely used in feature matching. This paper uses Euclidean distance for matching MSA feature: n

Sf

∑ ( M i − M j )2

(8)

i =1

In the formula Mi and M j are the MSA features extracted from different images. Figure 4.

Figure 5.

Delaunay net. Flowers’ retrieval result.

Table 2.

Elephants’ retrieval result.

DT net description.

3.3.2 Delaunay triangulation Harris-Laplace corner’s distribution is complex, which can not be directly used for information description, therefore one needs a specific method. DT network is a collection of triangular series but with no overlap, and the circumcircle of these triangles do not contain any other point of the plane. 3.4

Table 1.

Feature matching

Because this paper has used a variety of matching methods, it needs to use different similarity measures [13] for feature matching, including annular histogram, DT net and MSA feature. This paper uses the triangular gap evaluation method presented by literature [11]. Suppose there are two triangles ABC and A′ B ′C ′, then the similarity of two angles A and A′ is: Sa = cos3

⎛π ( − d a b )⎞⎠ ⎝2 −

1

(7)

( a b )2 (a

, σ = aP / 3 , P = 50%. where d (a, b ) e 2 Common distance methods include Euclidean distance [14], cosine value [15], Mahalanobis 2

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Table 3.

The contrast of three methods. Rn (%)

Pn (%)

T (s)

Images

M1

M2

M3

M1

M2

M3

M1

M2

M3

Face Trademark Car Elephant Landscape Flower

99.52 99.75 97.51 98.35 97.72 98.97

98.47 99.27 95.45 96.28 92.28 95.10

79.08 75.89 58.69 84.46 75.18 70.39

97.26 98.38 86.04 90.56 90.69 93.69

97.32 96.32 77.84 85.76 85.09 86.30

65.24 64.67 41.87 67.21 69.53 76.32

1.0345 1.0417 0.9673 0.9773 0.9588 1.0270

0.9417 0.9546 0.9048 0.9275 1.0602 0.9817

1.2350 1.6354 1.4115 1.4375 1.4579 1.4863

*M1, M2, M3 is three different methods.

4

EXPERIMENTS AND RESULTS ANALYSIS

In this paper the experimental environment is Windows XP system under the software of Matlab R2009a, CPU Intel Core 2 Quad 2.66 GHz, 4 GB memory’s computer. Image database downloaded from the Internet, includes the common character, animal, vehicle, trademarks and other images; this paper used Photoshop to process 20 images, and divided them into two groups. The first group includes 10 images after translation, rotation and scale changes after treatment- and 90 images without processing; the second group includes 10 images after adding noise, distortion and occlusion: the other 80 images are without processing. Then respectively it use this method for image retrieval experiments to carry out on the two groups of images. In order to prove this algorithm’s advantage, separately use DT net and the annular histogram to carry out retrieval experiments on 200 images in this library. Table 1 is the flowers’ retrieval result; Table 2 is the elephants’ retrieval result. From the analysis based on experimental results, although the images passed a variety of transformations, including scale, rotation, occlusion, distortion, and other transformations, the performance of this algorithm is better than the other two. The evaluation factors of image retrieval algorithm include recall rate, precision and retrieval time the three main indicators. Table 3 is the experiment result using three methods on two groups of image, where M1 is the algorithm in this paper, M2 is the only one using the annular histogram method; M3 is only using the DT net method. 5

retrieval algorithm, based on the MSA affine invariant features and Harris-Laplace corners. Through the contrast of retrieval experiments, this algorithm has better comprehensive properties than the histogram and DT net retrieval method, making up for the deficiencies of only using annular histogram and DT net. The experiments’ result shows that this algorithm has a good retrieval result on character, animal, trademarks and natural scenery images.

REFERENCES

CONCLUSION

Aiming at the deficiency of the existing image retrieval algorithms, this paper proposes a new

[1] J.M. Corridoni, A. Del Bimbo, P. Pala. Image retrieval by color semantics [J]. Multimedia Systems, 1999, 7(3): 175–183. [2] Sangoh Jeonga, Chee Sun Wonb, Robert M. Graya. Image retrieval using color histograms generated by Gauss mixture vector quantization [J]. Computer Vision and Image Understanding, 2004, 94(1–3): 44–46. [3] Ganar A.N., Nagpur, Jambhulkar S.M. Enhancement of Image Retrieval by Using Colour, Texture and Shape Features[C]// International Conference on Electronic Systems, Signal Processing and Computing Technologies. Nagpur, India: IEEE Press, 2014: 251–255. [10.1109/ ICESC.2014.48]. [4] X.Y. Wang, B.B. Zhang, H.Y. Yang. Content-based image retrieval by integrating color and texture features [J]. Multimedia Tools and Applications, 2014, 68(3): 545–569. [5] Torres, R.S., Picado, E.M., Falcao, et al. Effective image retrieval by shape saliences [C]// XVI Brazilian Symposium on Computer Graphics and Image Processing. IEEE Press, 2003: 167–174. [10.1109/ SIBGRA.2003.1241005]. [6] Castellano G., Castiello C., Fanelli A.M. Contentbased image retrieval by shape matching [C]// Annual meeting of the North American Fuzzy Information Processing Society. IEEE Press, 2006: 114–119. [10.1109/NAFIPS.2006.365870]. [7] Amin T., Zeytinoglu M., Ling Guan. Application of Laplacian Mixture Model to Image and Video

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[8]

[9]

[10] [11]

Retrieval [J]. IEEE Transactions on Multi-media, 2007, 9(7): 1416–1429.[10.1109/TMM.2007. 906587]. Wang W.X., Xu H.L., Luo D.J. Image Autoregistration on Harris-Laplace Features [C]. Third International Symposium on Intelligent Information Technology Application, 2009: 559−562. Rahtu E., Salo M., Heikkila J. Affine invariant pattern recognition using multiscale autoconvolution [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(6): 908–918. Zhang X.J., Sun J.G., Zhang Z.Y. An improved algorithm of annular color histgram [J]. Journal of Ningbo University, 2007, 20(2): 155–159. Yu Jie, Lu Ping, Zheng Changwen. The method comparison of Delaunay triangulation construction [J]. China Journal of image and graphics, 2010, 15(8): 1158–1167.

[12] Xing Chun. The multi feature fusion of image retrieval system design and Implementation Based on [D]. Harbin: Harbin University of Science and Technology, 2012. [13] Chen W.B. Several image similarity matching performance’s comparison [J]. Measurement of computer application, 2010, 30(1): 98–110. [14] Teng Zaixia, Liu Yue, Gao Junjun. Substitution rate estimation method based on weighted Euclidean distance [J]. Computer Engineering, 2010, 36(15): 283–285. [15] Li Junfeng, Dai Wenzhan. Fault diagnosis of oil immersed transformer based on the cosine of the angles [J]. Journal of scientific instrument and meter, 2005, 26(12): 1302–1305. [16] Li Yurong, Xiang Guobo. An analysis of linear classification algorithm based on Mahalanobis distance [J]. Computer Simulation, 2006, 23(8): 86–88.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on Electric Vehicle development in Beijing L. Zhang, M.Y. Pan, Z.J. Chi & Y.X. Chen State Grid Beijing Power Research Institute, Beijing, China

X.N. Kang Huazhong University of Science and Technology, Hubei, China

ABSTRACT: The development of an Electric Vehicle (EV) without exhaust emission is beneficial to improve serious air pollution in Beijing, as well as meet daily commuting needs of citizens. In order to rapidly expand personal users, several barriers and countermeasures to EV development are analysed and proposed. Investigation and survey are implemented to measure customer needs, and recommended EV and Electric Vehicle Supply Equipment (EVSE) developing models are given. In the end two application cases about the personal EV promotion project and time-share rent are introduced, and the feasibility of proposed models is incipiently verified. Keywords: Electric Vehicle (EV); Electric Vehicle Supply Equipment (EVSE); developing model; barrier and countermeasure 1

INTRODUCTION

Beijing, also known as Peking, is the capital of the People’s Republic of China and is one of the most populous cities in the world, with a population of twenty-one millions and motor vehicles of over five million (2014). In recent years, serious air pollution leading to the photochemical smog is troubling this great capital. Traffic emission contributes 22.2% to its air pollution. Motor vehicle exhaust emission becomes a major ingredient in the creation of smog in Beijing [1]. Electric Vehicles (EVs) provide a range of important benefits—from reducing greenhouse gas emissions to minimizing dependence on petroleum. As a result, many municipalities such as Beijing are working toward creating policies and programs that encourage EV ownership and usage. The national and Beijing local governments would subsidize maximum 60,000 RMB respectively and exempt the purchase tax to the personal EV buyer [2]. The local government also released the “licenseplate lottery” policy to limit purchasing traditional gasoline vehicles. Meanwhile, EV is much easier to apply for a new license-plate registration. Nowadays, there are 75 charging and battery swap stations and 2870 Electric Vehicle Supply Equipment (EVSE) outlets in Beijing. These EV infrastructures mainly provide services for public transportation which include battery electric buses, taxis and sanitation trucks. On the another

hand, few personal EVs are used due to multiple reasons. This paper focuses on the study of personal EV and EVSE developments in Beijing. Several barriers and countermeasures to EV development are analysed and proposed. According to the investigation of EV customer needs, recommended EV and EVSE developing models are given. At the end two application cases are introduced and the feasibility of proposed models is incipiently verified. 2

BARRIERS AND COUNTERMEASURES TO EV DEVELOPMENT

A series of EV purchase subsidies and tax-free policies have been released in Beijing during the past years. However, the market for personal EVs suffers from slow market sales growth. The reasons are varied and complex. The effect factors of personal EV development contain (are not limited to) the performance of the electric vehicle and power battery, charging convenience, purchase and upkeep costs and business models. A SWOT analysis on Beijing EV development is shown as Figure 1. 2.1

Barriers identification

Range anxiety and charging convenience are important factors for the customer to use Battery Electric Vehicle (BEV). The maximum mileage

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Table 1. Charging types according to national standards of China.

Figure 1.

Type

Rated voltage (V)

Rated current (A)

AC level 1 AC level 2 DC level 1 DC level 2

250/440 250/440 750 750

16 32 125 250

SWOT analysis on Beijing EV development.

for most of the BEVs is between 80 kilometers to 160 kilometers, especially for Tesla Model S, which is upto 500 kilometers. In order to meet daily commuting needs of drivers, it is necessary to develop a convenient charging network. Customers could charge at home, at the workplace, a public station and etc. According to the national standards of China, the charging types of EVSEs include two AC charging interface ratings and two DC fast charging interface ratings, as shown in Table 1 [3–4]. Actually, it is troublesome for the individual customer to install EVSE, because of issues of charging equipment cost, constant parking bay and available superior power supply. It is the preferred choice for customer to use an AC charging spot at home or at the work place. Incomplete statistics indicate that 62.5% of EV customers install EVSE at home, and most of them live in singlefamily houses. It is worth noting that the majority of Beijing residents live in Multi-Unit Dwellings (MUDs). Compared to single-family homes, it is more difficult and uncertain for multi-unit building to install EVSE. Barriers can involve difficulty and cost for different EV customers; however, there are two main complications: Issues stemming from the involvement of multiple stakeholders (difficult negotiations), and increased capital costs resulting from the size of the structure and its electrical system (physical limitations). A third complication is the lack of policies designed to encourage property management to support home-charging in MUD. Currently, only EV drivers or EV makers are allowed to obtain the related subsidies, but building management could play a key role in providing EVSE access for residents since they may be able to achieve economies of scale with multiple installs. Main barriers that exist for MUD are summarized as follows:

Determining payment system for electricity usage and billing models. Insurance coverage for EVSE. Distance from assigned parking to superior power supply or panel. Electrical capacity of superior electric source. Communication scheme in underground parking area. Subsidy to EV maker or driver. The resident requires approval from the landlord or property management of the residence to carry out the installation of EVSE. In most practical cases, Landlords and Homeowners Associations (HOAs) usually reject approving installations and charging at parking places because of the complicated technical aspects— such as details regarding the responsibility for the safety of electrical equipment and electricity usage. There is still a general lack of knowledge about costs related to EVSE installation, electrical capacity, and code compliance, which is a significant hurdle, for parties wishing to install EVSE in MUDs, to overcome. The statistical data by a project of EV drive test indicates that 20% of applicants whose home parking bay meets the conditions of EVSE installation are rejected by the property management. 2.2

Proposal countermeasures

The research on the existing barriers and policies urge us to propose new policies to deal with EV charging challenges mentioned above. The existing policies and measures in Beijing include:

Approval for installation from property management of residence. Pay for charging equipment and installation cost.

Subsidising both EV purchasers and EVSE installation cost. EVSE manufacturer provide services to solve installation and negotiation with property management or electric power company. The electric power company undertakes obligations to the work and cost of capacity increasing and improvement to superior power supply. Determine reasonable billing models and concessional electricity pricing.

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The latest policy, to be released soon enacts that Charge Service Operator (CSO) can get electricity pricings and service charge from EV customers. Home-charging will execute the cheap resident electricity pricing. The proposed policies and measures include: Subsidising the property management of residence, and encourage landlords wanting to add charging as an amenity. Develop a guide explaining the variety of installation/utilization/cost recovery schemes for EVSE installations. Employ an informed mediator to assist with difficult negotiations between residents and property management. Consider expanding and adding flexibility to pertinent Green Building Code requirements of EVSEs. 3

3.1

range and route is regular within 30 kilometers in the city center area, and less than 100 kilometers. Analysis results are shown as Figure 3. Most of users charge the vehicle at home, and later charge at the work place. Customers who are unable to install EVSE at home have to charge in the near by public station. The survey about charging places to users is shown as Figure 4. Majority of potential purchasers prefer charging in the public stations according to the analysis of accumulated statistical data. And the first choice is absolutely home-charging at a private parking bay. In a word, charging convenience is the critical factor to consider all the time. The survey to potential purchasers about charging place is shown as Figure 5.

CUSTOMER NEEDS AND RECOMMENDED DEVELOPING MODELS Customer needs

An investigation and survey to EV customers are implemented to help us confirm their needs and user experience. Respondents include sixteen BEV users and one hundred potential purchasers. Nowadays, most of Beijing EV customers are the middle-income or high-income groups. They are “license-plate lottery” rigid demand for the car, since they need to take kids to school and cannot get a license-plate of a gasoline motor. Charging convenience is the primary factor for potential purchasers to decide whether to buy a BEV. And purchase price, drive safety and licenseplate lottery are later factors to consider. The survey result on purchase decision factors is shown as Figure 2. BEV users usually make detailed route planning due to the driving range limitation. Daily driving

Figure  2. Purchase decision factors to potential purchasers.

Figure 3.

Daily driving range to BEV users.

Figure 4.

Daily charging places to BEV users.

Figure  5. Expectation charging places to potential purchasers.

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3.2

Recommended developing models

According to the survey results above, some suggestions on the public stations are proposed as follow: Establish public EVSEs in the supermarkets, shopping malls, theaters, parks and other public places. Establish ancillary EVSE in the government, state-owned enterprises, institutions and university campus opening to personal EVs. Off-peak parking at night would increase the use ratio of EVSEs and benefit personal EV customers who live nearby. The distribution distance of public charging station should be within three kilometers. More AC or DC fast EVSEs should be built in public charging stations, and AC slow EVSEs might be built at home. In order to fast expand EV to personal customers, convenient home-charging is the most favoured, and public charging is an effective and important measure—and then EV time-sharing rent is the promising new business model. The existing EV development model is shown in Figure 6. Government gives all the subsidies to the EV maker to lower the selling price, and a partial subsidy to EV purchaser for the construction cost of EVSE. Manufacturers install and maintain EVSEs, and electric power company takes responsibility for the EVSE connection to the grid. In addition, personal EV purchasers can be exempt from the purchase tax. A CSO that provides operation service of EVSEs is necessary for customers. NRG’s eVgoSM

Figure 6.

The existing EV development model.

Figure  7. Europe.

EV time-share rent in North America and

subscription model offers an end-to-end solution for installation, permitting, maintenance, and repair of the charging equipment as well as unlimited public charging for a fixed monthly fee [5]. Customers are responsible for home electricity use. Customers can request charging equipment for their home parking bay and access to public charge points. NRG also collaborates with property owners or managers to wire and pre-wire parking spaces in multi-unit dwellings or workplaces for potential customers. This subscription model removes the upfront cost for the customer, in addition to offering a single point of contact. Another business development model is EV time-share rent such as Zipcar or Car2  go in North America and Europe. Customers no longer suffer the high purchase price of EV, and drive and pay conveniently. Compared with the traditional car rental service, time-share rent has two remarkable characteristics: one is that the customer can rent and back the car in any urban parking place, and second, use fees are counted by minute. It has been a favoured and fashionably driving experience for a lot of young people to rent electric vehicles. 4 4.1

APPLICATION CASES IN BEIJING EV drive test project for personal customers

In order to further improve the quality and user experience of the ActiveE, BMW Group cooperated with Beijing Electricity Power Company and put in place the BMW ActiveE, a purely electrically powered vehicle project in Beijing. Twenty trial users have been selected from 851 applicants. The main factors influencing the selection result include: own a fixed indoor parking place, agreement with property management of residence, own residential property rights, and are willing to pay the monthly rent. On the final selection step for 80 applicants to confirm the EVSE installation condition, 18.75% of applicants’ parking places cannot achieve the installation, and 20% of applicants cannot gain the agreement with property management of residence. It indicates that available parking place and agreement with property management are mainly barriers for the home-charging installation. The installation processes of EVSE by all the stakeholders are shown in Figure 8. A BEV rent pilot project has been implemented in Tsinghua Science Park for over a year. The charging station includes seventeen AC charging spots and three DC fast charging spots, and sixteen electrical vehicles for rent. The rent schemes consist of time-share rent and long time rent, as shown in Table 2.

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It shows that a BEV can fully satisfy customers’ daily needs. Because the charging station is built at the work place, drivers usually use charging spots in the daytime. Statistical analysis on the use ratio of charging spots reveals that the annual utilization hours of AC spot is 868.7 hours and DC fast spot is only 138.7  hours. The limited number of rental EVs result in the low use ratio of charging equipments. The survey result on requirements of EV tenants is shown in Table 3. Figure  8. The installation processes of EVSEEV rent pilot project. Table 2.

Price RMB

Time-share rent Per two hours Per four hours At night hours (18:30–8:30) Long time rent Per one week Per one month

Table 3.

59 99 99 999 3999

Requirements of EV tenant. Applicants (N = 150)

Features

drive to work; take kid to school; interests Daily driving range 20–100 km Distance to work 10–50 km Range expectation Above 200 km Charging time – Charging duration – expectation

CONCLUSIONS

Based on investigation and research results, conclusions about EV development issues are summarised as fellow. Beijing is particularly well positioned to be a leader in fleet transition to EVs. The city has many densely populated areas and many of the city’s drivers rely on personal vehicles for commutes that are within the battery range of EVs currently on the market. In addition, the region’s poor air quality produces a strong incentive to reduce vehicle emissions. More beneficial policies should be progressively released in future. EV can meet the demand of daily commute for office workers or housewives. It is incipiently verified by the user demand investigation and statistical analysis on the practical projects. Available parking place and agreement with property management are mainly barriers for the home-charging installation. Public charging should be rapidly developed, and measures of legislation, financial incentive as well as effective negotiation between stakeholders should be taken. New and emerging EV business models are worth developing. EV time-share rent is a promising business model that will help primeval EVs rapidly promote.

Rent schemes for BEV.

Scheme

5

Rental users (N = 20)

Rental purposes

20–50 km 20–30 km Above 180 km 9:30 am–16:00 pm Within one hour

REFERENCES

Figure 9.

EV rent pilot project in Beijing.

Analysis on the EVs use data indicates that a car averagely daily drives 31 kilometers, and charges 0.6 times per day, then consumes 7.8 kWh electricity power which DC fast charging account for 30%.

[1] Zhang Jize, Liu Jingran, Kong Yibao. The correlation of vehicle emission and haze phenomenon in Beijing [J]. Northern Environment, 2013, 11: 115–117. [2] Gu Ruilan. Study on the Fiscal and Taxation Policies to Promote the Development of China’s New Energy Vehicles Industry [D]. Beijing: Research Institute for Fiscal Science, Ministry of Finance, 2013. [3] Connection set for conductive charging of electric vehicles-Part 2: AC charging coupler [S]. GB/T 20234.2-2011. [4] Connection set for conductive charging of electric vehicles-Part 3: DC charging coupler [S]. GB/T 20234.3-2011. [5] Emerging Electric Vehicle Business Models [R]. NPC Future Transportation Fuels Study, 2012.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Lubrication performance analysis of three axial-grooved gas-lubricated journal bearing with micro grooves Y.J. Lu & F.X. Liu School of Mechanical and Instrumental Engineering, Xi’an University of Technology, Xi’an, P.R. China Sate Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an, P.R. China

Y.F. Zhang School of Printing and Packaging Engineering, Xi’an University of Technology, Xi’an, P.R. China

C. Tian & M. Li School of Mechanical and Instrumental Engineering, Xi’an University of Technology, Xi’an, P.R. China

ABSTRACT: Lubrication performance of three axial-grooved gas-lubricated hydrodynamic journal bearing with micro grooves was investigated. Parabolic micro grooves were textured on the surfaces of the pads of three axial-grooved gas-lubricated hydrodynamic journal bearing and a mathematical model of the dimensionless gas film thickness of the gas-lubricated bearing was established. The dimensionless steady Reynolds equation was solved by the finite element method, and then the nonlinear gas film pressure distribution was obtained. The numerical results showed that the finite element method for solving the steady Reynolds equation of three axial-grooved gas-lubricated hydrodynamic journal bearing is effective, and the dimensionless load-carrying capacity can be increased and the friction coefficient can be reduced when micro grooves are textured on the surfaces of the pads of the gas-lubricated bearing. Keywords: axial-grooved gas-lubricated bearing; finite element method; surface texturing; load-carrying capacity; friction coefficient 1

INTRODUCTION

The gas-lubricated journal bearings have been widely used in the rotating machinery [1–4]. The gas is dragged into the clearance between the bearing and the rotor, and then a hydrodynamic gas film is formed when the rotating machine works. The gas film can provide high stiffness, but is never broken down during starting, running, and stopping. More recently, many scholars have paid attention to surface texturing. Blatter et al. [5] applied a laser ablation technique to fabricate the surface of sapphire plates for studying the effect of the patterns of grooves on the sliding friction. Experimental results showed that wear could be reduced by an appropriate size and form of the micropattern. Ogawa et al. [6] examined the effect of dimple and groove textures on the tribological properties of slideways and has concluded that the friction coefficient can be reduced by texturing dimples and grooves on the contacting surface. Tala-ighol et al. [7] studied the tribological performance of the contacting surface by optimizing the geometric parameters of textured surfaces. It was found that

the textured surfaces have an important influence on the lubrication performance of journal bearing. Fu et al. [8] investigated the hydrodynamic lubrication performance of conformal contacting surfaces with parabolic grooves by multigrid method. The results revealed that the geometric shapes of parabolic grooves have a significant impact on the hydrodynamic lubrication. In this paper, the finite element method is employed to solve the steady Reynolds equation of three axial-grooved gaslubricated journal bearing with parabolic grooves, and then the effect of the orientation, dimensionless depth, width and distance of parabolic grooves on the dimensionless load-carrying capacity and friction coefficient is investigated. 2 2.1

SYSTEM EQUATION Steady governing equation of the gas-lubricated journal bearing with three axial grooves

Figure  1 shows schematic diagram of the gaslubricated journal bearing with three axial grooves.

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2. Gas pressures at the both ends of the bearing are equal to the ambient pressure pa. 3. Gas pressure is continuous at λ = 0. 2.2

Dimensionless load-carrying capacity and friction coefficient

The finite element method is employed to solve the dimensionless Reynolds equation of the gas-lubricated journal bearing with three axial grooves. Equation (1) is written in the following form: ⎡ ∂ ⎛

∫∫ ⎢ ∂ϕ ⎜⎝ PH Ω

⎣

×δ

Figure  1. Calculation coordinate for axial-grooved hydrodynamic gas-lubricated journal bearing.

3

∂P ⎞ ∂ ⎛ ∂( PH ) ⎤ 3 ∂P ⎞ + ⎥ ⎜⎝ PH ⎟⎠ − Λ ⎟ ∂ϕ ⎠ ∂λ ∂λ ∂ϕ ⎦

ϕdλ = 0

(2)

where δP is the variation of P, Ω is the computational domain of the gas film. Integration by parts and Green’s theorem are used to solve equation (2), and then equation (3) is obtained. ⎡ H 3 ∂P 2 ∂(δ P ) H 3 ∂P 2 ∂(δ P ) ∂(δ P ) ⎤ + − ΛPH ⎥ 2 ∂ ϕ ∂ ϕ 2 ∂ λ ∂ λ ∂ϕ ⎦ ⎣

∫∫ ⎢ Ω

× dϕ d λ = 0 (3)

Figure 2. Local coordinates for calculation in a rectangular element.

The dimensionless steady Reynolds equation of the gas-lubricated journal bearing with three axial grooves is described as follows: ∂ ⎛ ∂P ⎞ ∂ ⎛ ∂( PH ) 3 ∂P ⎞ PH 3 + ⎜ PH ⎟=Λ ∂ϕ ⎜⎝ ∂ϕ ⎟⎠ ∂λ ⎝ ∂λ ⎠ ∂ϕ

(1)

where P = p/pa is the dimensionless pressure distribution, pa is the ambient pressure, H is the dimensionless gas gap between the journal and the bushing, ϕ and λ are the coordinates for calculation which are shown in Figure 2, Λ = ( μ / pa ) (R / )2 is the bearing number. The boundary conditions of the dimensionless Reynolds equation are stated as follows: 1. Gas pressure along the axial grooves is equal to the ambient pressure pa.

The pressures of the gas-lubricated journal bearing with three axial grooves are equal to positive values at any instant. The rectangular element with four nodes is described in Figure  2. The shape function in the rectangular element can be written as N1 N3

1 (1 ξ )(1 4 1 (1 ξ )(1 4

1 (1 ξ )( ) 1 − η )), 4 1 )), N 4 = (1 − ξ )( ). 4

)), N2 =

(4)

where ζ and η are the local coordinates of each element. By using the coordinate transformation method, the relationship between (ζ, η) and (ϕ, λ) can be expressed as:

ζ

2 2 ϕ − ϕ 0 ), η = ( λ l1 l2

λ0 ).

(5)

where l1 and l2 are the length and width of each element respectively, (ϕ0, λ0) is the coordinate of computational grid center.

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2.3

Geometric shape of the textured pad

A textured pad of three axial-grooved gas-lubricated journal bearing with parabolic micro grooves is shown in Figure 3. The gas film thickness of any point in the textured pad is H(ϕ, λ). When 0 ≤ γ ≤ 900, ⎧ ⎪ 4H g ⎪H 0 + H g − 2 ⎪ Wg ⎪ ⎪ ⎪ ⎪ H (ϕ , λ ) = ⎨ ⎪H + H − 4 H g g 2 ⎪ 0 Wg ⎪ ⎪ ⎪ ⎪ ⎪ ⎩H 0

1 ⎧⎡ ⎤⎫ ⎪⎪⎢⎣ttan γ (ϕ − ϕ s ) 2 L2 λ ⎥⎦⎪⎪ ⎨ ⎬ ⎪ccos γ − ⎛ n + 1⎞ W 1 Ws ⎪ ⎝ 1 2⎠ ⎪⎩ ⎪⎭ 2

2 ⎧ ⎤ 4 H g ⎡ϕ ϕ s ⎪ ⎢ 1 ⎥ ⎛ϕ ϕ s ⎞ ⎪H 0 H g − W 2 ⎢− fix i W Wg⎥ ⎪ g ⎝ ⎠ 2 ⎦ W ⎣ H (ϕ , λ ) = ⎨ ϕ s + n2W ϕ ϕ s n W Ws ⎪ ⎪ ⎪⎩H 0 others (7)

2

where

n1

λ1 < λ < λ2

1 ⎤⎫ ⎧⎡ γ (ϕ − ϕ 0 ) + L2 ⎥⎪ ⎪⎪⎢⎣λ − 2 ⎦⎪ ⎨ ⎬ W ⎪cos γ − nW − g ⎪ 1 ⎪⎩ ⎪⎭ 2

1 ⎡ ⎤ ⎢⎣tan γ (ϕ − ϕ s ) − 2 L2 − λ ⎥⎦ cos γ ffix , W

2

λ3 < λ < λ 4 others

(6) When γ = 90 , 0

Figure  3. Schematic diagram of parabolic microgrooves textured on the pads of axial-grooved gaslubricated bearing.

Figure 4. (a) Dimensionless load-carrying capacity versus different textured pads; (b) friction coefficient versus different textured pads.

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The detailed parameters of the bearing are listed as follows: the bushing arc angle of the bearing α is

115°, the groove width angle of the bearing ς is 5°, the clearance of the bearing c is 3.0 × 10−5 m, the gas viscosity μ is 1.8 × 10−5 Pa⋅s, the radius of the journal R is 0.05 m, the width-to-diameter ratio is 0.9, the rotating speed ω is 1500 rad/s, the bearing number Λ is 4.44, β is 62.5°, θ is 0°. Figure 4 shows the dimensionless load-carrying capacity and friction coefficient over the textured pad for the case of γ = 60°, Hg = 1, Wg = 0.05 and Ws  =  0.05. The load-carrying capacity can be increased and the friction can be reduced by texturing micro grooves on an appropriate pad. Figure 5 shows the load-carrying capacity ratio Fav  =  (Fy− Fy0)/Fy0 and friction coefficient ratio μav = (μ−μ0)/μ0 for the case of Λ = 1, Λ = 4.44, Λ = 10 and Λ = 15, respectively. Figure  6 shows the load-carrying capacity ratio Fav = (Fy−Fy0)/Fy0 and friction coefficient ratio μav = (μ−μ0)/μ0 for the case of bd = 0.5, bd = 1, bd = 2, respectively. Fy and μ is the dimensionless load-carrying capacity and friction coefficient respectively when the pad is textured. Fy0 and μ0 is the dimensionless load-carrying capacity

Figure 5. (a) Load-carrying capacity ratio versus different bearing number; (b) friction coefficient ratio versus different bearing number.

Figure 6. (a) Load-carrying capacity ratio versus different width-to-diameter ratio; (b) friction coefficient ratio versus different width-to-diameter ratio.

n2

fix f

ϕ − ϕs , W 1 2 1 γ (ϕ ϕ s ) − 2 1 γ (ϕ ϕ ) − 2 1 γ (ϕ ϕ s ) − 2

λ1

γ (ϕ ϕ s ) −

λ2 λ3 λ4

sec γ ,

2

− ( n1 + 1)

2

− ( n1W + Ws )sec γ ,

2

+

1

sec γ ,

2

+

1

s γ + Ws sec

γ,

fix is rounding a number towards zeros.

3

NUMERICAL EXAMPLES AND RESULTS

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and friction coefficient respectively when the pad is untextured. It is found that the load-carrying capacity of three axial-grooved gas-lubricated journal bearing can be still increased and the friction can be also reduced by selecting appropriate pad and texture parameters with the variation of the bearing number and the width-to-diameter in Figure 5 and Figure 6. 4

CONCLUSIONS

Lubrication performance of three axial-grooved gas-lubricated journal bearing with microgrooves is investigated. The mathematic model of the dimensionless gas film thickness of the gas-lubricated bearing with micro-grooves is built and the finite element method is employed to analyze the dimensionless load-carrying capacity and friction coefficient versus different textured pads, and the load-carrying capacity ratio and friction coefficient ratio versus different bearing numbers and width-to-diameter ratios. The results show that the load-carrying capacity can be increased and the friction coefficient could be reduced when micro grooves are textured on the surfaces of the pads of the gas-lubricated bearing.

ACKNOWLEDGEMENT This work is supported by National Natural Science Foundation of China (Grant No. 51375380), Open Project of State Key Laboratory for Strength and Vibration of Mechanical Structures (Grant No. SV2014-KF-08), National Natural Science

Foundation of Shaanxi Province of China (Grant No. 2013JQ7008). REFERENCES [1] Wang C.C., 2012, “Bifurcation and nonlinear dynamic analysis of united gas-lubricted bearing system,” Comput. Math. Appl., 64(5): 729–738. [2] Lu Y.J., Zhang Y.F., Shi X.L., Wang W.M. & Yu L., 2012, “Nonlinear dynamic analysis of a rotor system with fixed-tilting-pad self-acting gas-lubricated bearings support,” Nonlinear Dyn., 69(3): 877–890. [3] Zhang Y.F., Hei D., Lü Y.J., Wang Q.D. & N. Müller, 2014, “Bifurcation and chaos analysis of nonlinear rotor system with axial-grooved gas-lubricated journal bearing Support,” Chin. J. Mech. Eng., 127(2): 358–368. [4] Zhang Y.F., Zhang S., Liu F.X., Zhou C., Lu Y.J. & N. Müller, 2014, “Motion analysis of a rotor supported by self-acting axial groove gas bearing system with double time delays,” Proc IMechE Part C J. Mech. Eng. Sci., DOI: 10.1177/0954406214523581. [5] Blatter A., Maillat M., Pimenov S.M., Shafeev G.A., Simakin A.V. & Loubnin E.N., 1999, “Lubricted sliding performance of laser-patterned sapphire,” Wear, 232(2): 226–230. [6] Ogawa H., Sasaki S., Korenaga A., Miyake K., Nakano M. & T. Murakami, 2010, “Effects of surface texture size on the tribological properties of slideways,” Proc IMechE Part J J. Eng. Tribol., 224(9): 885–890. [7] Tala-Ighil N., Fillon M., and Maspeyrot P., 2011, “Effect of textured area on the performances of a hydrodynamic journal bearing,” Tribol. Int., 44(3): 211–219. [8] Fu Y.H., Ji J.H. & Q.S., Bi, 2012, “Hydrodynamic lubrication of conformal contacting surfaces with parabolic grooves,” ASME J. Tribol., 134(1): 210–218.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A new configuration of current source converter applied in HVDC J.Y. Zhao, F.M. Zhang & F.G. Liu School of Electrical Engineering, Hebei University of Technology, Tianjin, China

Y.H. Liu Faculty of Electric Power, Inner Mongolia University of Technology, Hohhot, China

ABSTRACT: Due to the defect of the slow fault response and higher power loss of the Voltage Source Converter (VSC), this paper proposed a novel Current Source Converter (CSC) which could solve these problems. The new topology, with Multi-Level Current Reinjection CSC (MLCR-CSC), is based on the DC-ripple reinjection concept. It adds an auxiliary control circuit to the traditional 12-pulses converters which is used in IGBT as extra main switches. MLCR-CSC is a very attractive way of transmission as it generates lower THD on the AC-side and realises Zero Current Switching technology (ZCS) and runs at a high power factor. The advantages and disadvantages of VSC and CSC are shown first. Then is introduced the DC-ripple reinjection concept and the new topology; this is followed by a detailed analysis of the harmonic elimination theory and discusses the control strategy. MLCR CSC can realise ZCS, use thyristors as the power device, ensure the safety and stability of the phase communicating, low THD and high power factor, which was effective certificated in HVDC transmission by Matlab/-Simulink at the end. Keywords: 1

DC-ripple reinjection; MLCR-CSC; lower THD; ZCS; high power factor

INTRODUCTION

High Voltage Direct Current (HVDC) power transmission had developed for many years, which solves lots of tricky problems that Flexible AC Transmission System (FACTS) is not able to handle. It is widely accepted that HVDC transmission system can provide a cost-effective solution compared to traditional AC transmission in applications. HVDC also has the following advantages: the smaller line active loss, not restricted by system stability limit, little interference between DC connected to grid, the suitablity for the sea bottom transmission and the involvement of the high-voltage direct current transmission which will not increase the original power system short circuit current capacity. HVDC converters consist of VSC and CSC, which are classified by the energy storage component. VSC [1–5] can transmit power transmission in four quadrants. It can realise harmonic eliminates and is self-commutated and greatly improves the system stability. High speed charge and discharge between gate and source presents produces larger loss. CSC [6–10] has good economic benefits in high power and long distance transmission. The typical CSC can work in two quadrants and bidirectionally transmit active power in an HVDC project; its drawback is consumption

bulk reactive power in operation. CSC has been widely applied because of its rapid fault response and lower power loss than VSC. This paper presents a novel multi-level reinjection CSC which is constituted of a traditional 12-pulse convertor and an auxiliary injection circuit based DC-ripple reinjection concept. Due to the difficulty in generatinga practical reinjection current from theoretical research; MLCR CSC has no actual engineering except laboratory studies. While, the new topology can realize lower THD, ZCS and high power factor, it will be a very attractive scheme in HVDC transmission. 2

DC-RIPPLE REINJECTION CONCEPT

Generalised DC-ripple reinjection technology [11–14] is investigating a DC-ripple current into the neutral point of the converter transformer, through a single-phase bridge transformer connected DC side, and the injected current exists as times harmonic current three in approximate rectangle phase current waveform. The single bridge three-phase converter produces a ripple voltage at the DC output, with a period of (1/6) T, where T is fundamental frequency. However, each DC pole has a non-sinusoidal ripple voltage of period (1/3) T. The principle of DC

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ripple reinjection is applicable to the line commutated three-phase current source converter bridge and the circuit configuration is shown in Figure 1. The converter transformer constitutes star-connected on the converter side. It requires an auxiliary single-phase transformer with two primary windings connected to the common mode DC ripple voltage via blocking capacitors. This transformer provides the commutating voltage for a single-phase full-wave rectifier connected to the secondary windings. The output of the reinjection converter is connected in series with the DCoutput of the six-pulse converter bridge. The reinjection transformer secondary current consists of quasi-rectangular 60  degrees DC current pulses, the magnitude of which is determined by the main converter DC current and their phase position is always in synchronism with that of the main converter current. These current pulses, after being appropriately altered by the injection transformer ratio, are added to the otherwise conventional DC current output of the main converter and channeled to the appropriate phases of the main converter current by the conducting thyristors.

Figure 1.

Figure  2. Figure 1.

Bridge rectifier with ripple reinjection.

When the thyristors of the reinjection converter are fired. 30 degrees after the firings of the main converter valves, the waveforms in Figure 2 result. The triple frequency commutations of the reinjection bridge also combine with the DC output voltage to produce a twelve pulse voltage waveform. The result is that the original six-pulse converter configuration has been converted to a 12-pulse converter system from the point of view of AC and DC system harmonics.

3

MLCR-CSC TOPOLOGICAL STRUCTURE AND OPERATING PRINCIPLE

MLCR-CSC topological structure is shown in Figure 3. In the pulse multiplication scheme described above, the magnitude and duration of the reinjection steps were optimised to achieve maximum harmonic cancellation, which, as already explained, for the five-level reinjection configuration provides 60-pulse conversion. However, this reinjection waveform does not completely cancel the converter valves currents during the commutation and, therefore, turn-off devices are still needed to provide self-commutation. Next is shown that the use of a non-optimal reinjection waveform can force a ZCS condition for an interval of 6 degrees (or 333us at 50 Hz) during the commutation. This should permit the outgoing thyristors to recover their blocking capability and thus make the conventional thyristors converter self-commutating. To achieve the ZCS condition the quality of the AC and DC voltage waveforms is somewhat reduced (from the optimum 60 to 48 pulse for the five-level reinjection scheme). It is therefore possible to achieve selfcommutation, as well as pulse multiplication, using conventional thyristors for the converters and IGBTs for the reinjection switches. This alternative

Current waveforms for the converter of Figure 3.

Five-level current reinjection CSC.

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Table 1.

Reinjection switching combinations and 7-level reinjection current.

On-state switches windings ratio Spj1/Snj1 Spj2/Snj2 Spj3/Snj3 Spj0/Snj0 Spj4/Snj4 Spj5/Snj5 Spj6/Snj6

n0: n1 + n2 + n3 n0: n2 + n3 n0: n3 0 n0: n3 n0: n2 + n3 n0: n1 + n2 + n3

Ij1

I1

Ij2

I2

Idc 0.66Idc 0.33Idc 0 −0.33Idc −0.66Idc −Idc

2Idc 1.66Idc 1.33Idc Idc 0.66Idc 0.33Idc 0

−Idc −0.66Idc −0.33Idc 0 0.33Idc 0.66Idc Idc

0 0.33Idc 0.66Idc Idc 1.33Idc 1.66Idc 2Idc

gives the thyristors converter a similar flexibility as a forced commutated VSC, i.e. the ability to control both the DC voltage and current, with a leading or lagging power factor, as well as reducing the harmonic content. In other words, it combines the benefits of the robust and efficient conventional converter and the controllability of the advanced self-commutated technology. This is an important breakthrough that should give greater flexibility to thyristors-based HVDC transmission [15–18]. 4

CONTROL STRATEGIES

The primaries of the two single-phase transformers are connected across the DC BUS through DC blocking capacitors (C). The DC current (Idc) flows through the reinjection IGBTs, load inductance Ldc, and the load. It is modified into AC waveforms in the secondary windings of the reinjection transformer with the help of reinjection switches (Sp1/Sn1, etc). These currents are coupled to the reinjection transformer primary winding to form multi-level currents Ij1 and Ij2, which combine with Idc to shape DC bus currents I1 and I2 into multi-level waveforms. This reinjection circuit generates seven current steps in I1 and I2. Six levels are generated due to reverse connected switches (Sp1/Sn1 to Sp6/Sn6), and one additional level is obtained by firing Sp0/Sn0 when Ij1 and Ij2 are both zero. Table  1  shows the corresponding relationship between I1 and I2, Ij1 and Ij2 with the corresponding reinjection IGBT on-state. The reinjection transformer is a one-phase four-winding transformer with transformer turns ratio n3/ n0 = 0.33, n2/n0 = 0.33, n1/n0 = 0.33. 5

Figure 4.

Simulation dc current on the DC-side.

Figure 5.

Simulation of SLCR-CSC on the AC-side.

10 °, 20 ° and 30 ° AC-side current waveform and harmonic spectrum analysis. 5.1 Current zero switching and eliminating harmonics

SIMULATION RESULT

This section mainly verified the topological structure, eliminating harmonics and high power factor operation. It significantly includes MLCR—CSC current zero switching, eliminating harmonic, and 7 level current reinjection CSC firing at angle 0 °,

At 0.05  seconds, Ij1 reaches maximum amplitude, the dc side current IBΔ is zero which provides Δ bridge commutation switch off at Zero Current Switching (ZCS) conditions; At 0.06  seconds, Ij2 reaches maximum amplitude, the DC side current IBY is zero which provides Y bridge commutation

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switch off at Zero Current Switching (ZCS) conditions. Thus the converter can commutate without the assistance of a turn-off pulse or a line commutating voltage, i.e. it can be of the conventional thyristors type. Figure  3 expresses 7 level current reinjection CSC simulation result, the interface transformer secondary side Y bridge connection generates 7 level DC current reinjection IBY, the interface transformer secondary side Δ bridge connection generates 7 level dc current reinjection IBΔ. On the secondary side of the transformer Y bridge ac output current IaY conducts 120 °, Δ bridge AC output current IaΔ conducts 120 ° either. Due to secondary side Y and Δ coupling the interface transformer, it obtains a phase alternating current at primary side of transformer which is formed by the multilevel and infinite similar to sine wave. 5.2

Figure 8. Firing at 20°, a SLCR-CSC current waveform Spectrum analysis on AC-side.

AC-side current waveform and harmonic spectrum analysis

In order to further verify the convertor AC side harmonic content and excellent characteristic of high

Figure 9. Firing at 30°, a SLCR-CSC current waveform Spectrum analysis on AC-side.

Figure 6. Firing at 0°, a SLCR-CSC current waveform Spectrum analysis on AC-side.

power factor operation, 7 level current reinjection AC side current harmonic waveform and spectrum analysis is simulating firing at angle 0 °, 10 °, 20 ° and 30 ° as follows. Figures  6–9 presented a SLCR-CSC current waveform Spectrum analysis on AC-side with different triggering angle which is reaching an ideal sine wave. Among them, firing at 0 °, THD is 3.59% on AC side; firing at 10 °, THD is 3.58% on AC side; firing at 20 °, THD is 3.63% on AC side; firing at 30 °, THD is 3.55% on AC side. The main characteristic harmonic is 3, 5, and 13, 25 times. It obviously showed that THD is extremely strict accordance with the harmonic requirements standards of the state grid. 6

Figure 7. Firing at 10°, a SLCR-CSC current waveform Spectrum analysis on AC-side.

CONCLUSIONS

MLCR CSC has been shown to provide fast dynamic response. Additionally, it shows no signs of commutation failure in the main bridge thyristors for the operating conditions with the help of MLCR circuit. It is also known that MLCR-CSC

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can realise ZCS, using thyristors as the power device, ensure the safety stability of the phase communicating, low THD and high power factor. Therefore, MLCR CSC will play an important role in HVDC. With the shortcomings of MLCR CSC it is difficult to find an entirely appropriate reinjection current waveform which could reduce THD to 0%. The proposed MLCR CSC also does not permit completely independent control of the reactive power at both ends of the link as in present PWM controlled VSC based HVDC schemes. The independent reactive power control of these HVDC schemes is not discussed in this paper and will be a subject for further research. REFERENCES [1] Solas E, Abad G, Barrena J.A, et  al. Modelling, simu-lation and control of modular multilevel converter [C]. 14th Inter- national Power Electronics and Motion Control Conference. Ohrid, Macedonia: [s.n.] 2010: 90–96. [2] Xiguo Gong. A 3.3kV IGBT module and application in Modular Multilevel converter for HVDC. Industrial Electronics (ISIE), 2012 IEEE International Symposium on, Page(s): 1944–1949. [3] U Q.R, XU Z. Impact of sampling frequency on harmonic distortion for modular multilevel converter [J]. IEEE Transactions on Power Delivery, 2011, 26(1): 298–306. [4] Chang Hsin Chien and R.W.G. Buckoall, “Analysis of harmonics insubsea power transmission cables used in VSC-HVDC transmission systemsoperating under steady-state conditions”, IEEE Trans. Power Del., vol. 22, no. 4, pp. 2489–2497, Oct. 2007. [5] N. Flourentzou, V. G. Agelidis, and G. D. Demetriades, “VSC-based HVDC power transmission systems: an overview”, IEEE Trans. Power Electron., vol. 24, no. 3, pp. 592–602, Mar. 2009. [6] P. Riedel, “Harmonic voltage and current transfer, and AC- and DC-side impedances of HVDC converters”, IEEE Trans. Power Del., vol. 20, no.3, pp. 2095–2099, Jul. 2005. [7] W. Hammer, “Dynamic Modeling of Line and Capacitor Commutated Converters for HVDC Power Transmission,” Ph.D., Dept. Elect. Eng., Swiss Federal Inst. Technol., Zurich, Switzerland, 2003.

[8] R. Li, S. Bozhko, and G. Asher, “Frequency control design for offshore wind farm grid with LCC-HVDC link connection,” IEEE Trans. Power Electron., vol. 23, no. 3, pp. 1085–1092, May 2008. [9] R.B. Gimenez, S.A. Villalba, J.R. D’Derlee, S.B. Perez, and F. Morant, “Diode-based HVDC link for the connection of large offshore wind farms,” IEEE Trans. Energy Convers., vol. 26, no. 2, pp. 615–626, Jun. 2011. [10] L.O. Barthold, H.K. Clark, and D. Woodford, “Principles and applications of current-modulated HVDC transmission systems,” in Proc. IEEE T&D Conf., Dallas, TX, USA, May 21–26, 2006, pp. 1–7. [11] B.P. Das, N.R. Watson, and Y.H Liu. m-Level Thyristor based MLCR CSC: A Comparative Study. Power System Technology (POWERCON), 2012 IEEE International Conference on, Oct. 30 2012–Nov. 2. [12] Murray, NJ; Arrillaga, J; Watson, NR; Liu, YH. Twoquadrant Power Control for Large-current, Lowvoltage Rectification with Reference to Aluminium Smelters. Australian Journal of Electrical & Electronics Engineering, Vol. 7, No. 3, 2010: 235–246. [13] Bhaba Das, Neville Watson, and Yonghe Liu. DC Ripple Reinjection: A Review. International Journal of Emerging Electric Power Systems, 2011, Vol. 12: Iss. 5, Article 6. [14] YH Liu, J Arrillaga, NR Watson. Reinjection concept: a new option for large power and high-quality AC–DC conversion. The Institution of Engineering and Technology Power Electron., 2008, Vol. 1, No. 1, pp. 4–13. [15] Liu, Y.H. Multi-Level Voltage and Current Reinjection AC–DC conversion. PhD thesis, University of Canterbury, 2003, New Zealand. [16] Lasantha Bernard Perera. Multi-Level Reinjection ac/dc Converters for HVDC. PhD thesis, University of Canterbury, 2006, New Zealand. [17] Amirnaser Yazdani, Reza Iravani. Voltage-sourced converters in power systems: modeling, control, and applications. Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2010. [18] Arrillaga, J., Y.H. Liu, N.R. Watson and N.J. Murray. Self-Commutating Converters for High Power Applications. John Wiley & Sons, Ltd., 2009.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Application of high-order grey forecast model in the short-term load forecasting X.Y. Huang & L. Yang College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang Province, China

ABSTRACT: Taking into account the characteristics of exponential growth and periodic variation of power load, a third-order short-term load forecasting model based on grey prediction theory is proposed. This paper gives the introduction of grey forecast method and expounds on the main steps of the power system short-term load forecasting. By numerical example, this paper verifies the probability and validity of this method. Simulation results show that the proposed method is effective and practicable. Keywords: 1

power system; short-term load forecasting; grey prediction theory; third-order model

INTRODUCTION

Load forecasting is one of the most important foundations for the power system planning, power generation programming and economic operations. Accurate short-term electrical load forecasting is usually in favor of the timely and reliable decision of load supply and is of great significance for the safe and reliable operation of the power system as well as the development of the national economy. However, as the power load cannot be accurately controlled in nature, the best effective method is to research historical data and explore suitable methods combined with the current situation and information. Grey Theory is one of the practical theories in load forecasting. The Grey System Theory is essentially to turn some known data sequence regularly from a scattered state through accumulating technology, and then use differential equations to fit, and seek the future development of the state of research subjects after fitting curves to predict. When the load is in strict accordance with exponential growth, this method has advantages such as high prediction accuracy, less sample data, simple calculation, testability and so on. The ordinary Grey forecasting model has some shortcomings, such as it has failed to consider the exponential and cyclical regularity of load data at the same time, while the second-order discrete sequence of the Grey model can only represent one of the characters in exponential and periodic. At present, there are many domestic and international papers on the use of the Grey model to solve power system load forecasting problems. As the traditional Grey model has some flaws in load

forecasting, especially short-term ones, several numbers of papers have made a certain degree of improvement. In [1], the least square support vector regression algorithm is combined with the Grey model to improve its accuracy. In [2], a criterion of abrupt change of weather factors is established to correct local distortion points. In [3], the corresponding relation between the Grey model parameters and BP network weights is established to enhance the forecasting accuracy of medium and long-term power load forecasting under the conditions of small sample data. Reference [4] sets up an ant colony Grey model, which is combined with a neural network model. However, training the neural network usually takes a long time. Due to the changes in the input data is not the desired result most times; it will require multiple training, which is not applicable for the timeliness requirements of short-term or ultra-short-term load forecast. This paper mainly proceeds from the variation rules of short-term power load, combined with the characteristics of the high-order of the Grey prediction model, and proposes a relatively strong timeliness prediction method. Analysis shows that the method is in sync with the construction of the Grey model and has a theoretical basis. Finally the paper demonstrates the correctness and effectiveness of the proposed method. 2

THIRD-ORDER GREY FORECASTING MODEL

Power system load forecasting is a procedure which firstly establishes relevant models based on historical data of power load and its influencing factors,

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and then uses the model to forecast the future power load data scientifically. Short-term load indicates the traits of both randomness and uncertainty, and the prediction accuracy of load is affected by the history, weather, date, forecasting models, and social events, etc. Due to the impact of various factors, short-term load is manifested as non-stationary random process in time series. But the impact of system load factors in most of regularity, which provides a method for load forecasting. This paper, starting from the general, proposes a third-order grey forecasting model in short-term load forecasting. Assuming that the original data is a string of discrete series which is time-varying but interrelated, we need to firstly smooth the original data. The purpose of the original data series smoothing is primarily weakening the impact of outliers, and substantially strengthening the trend of the original series. One of the most commonly used smoothing methods is the moving average smoothing method. Its arithmetic averages the value of several adjacent moments, and then denotes the processed N-dimensional historical electric load data sequence as x ( ), that is x

( )

{x {x

( )

( k ), k = 1, 2, …, N }

According to the definition of the derivative, there is dx x(t + t ) x(t ) Δx = lim = lim Δt → 0 Δt dt Δtt → 0 Δt

If expresses in the form of discrete representation, the upper equation turns into a differential equation: Δx x( k + ) − x( k ) = = x( k + ) − x( k ) Δt k k

dx ( ) Δx ( ) = lim = lim ( x ( ) ( k + 1) x ( ) ( k )) Δtt → 0 Δt Δt → 0 dt ≈ x ( ) ( k + 1)

x

{x ( k ), k = 1, 2, …, N } {x

(2) k

( )

In the above equation, x ( k ) = k∑=1 x (i ), define { ( ) } as the r-th generation of sequence, then we can get the relationship after r-th accumulated generating as follows ( )

k

x(r ) (k ) = ∑ x(r

)

(i )

(3)

i =1

Obviously x ( r ) ( k ) can be decomposed as: k

x (k ) = ∑ x (r )

i =1

(r

)

(i ) = ∑ x

= x ( k − 1)) (r )

k −1

(r

)

(i ) + x

(r

)

(

)

(k )

Assuming the load sequence satisfy three-order differential equation, then 3

≈ x( ) (

d

2)

) (10)

Mark x( k ) as the generated value of the k-th point, there is x( k ) = γ 1x ( ) ( k ) + γ 2 x ( ) ( k

(11)

)

When γ 1 γ 2 = 0.5 , the generating happens in adjacent values, and the generated value is called as neighbour equal weights generated value. Take the neighbour equal weights generated value of x ( ) as the average value of k and k + 1, that is (3)

( k + 1)

(3)

( k ))

(12)

Use 1/ 2( x (3) ( 1) + x (3) ( )))) instead of x(t), d , x( ) (k ) instead of x ( k ) instead of dx /dt 2 2 ( ) ( /dt /dt ), x ( ) instead of d 3x / dt 3, and then substitute into the Eq. (5) and get

2

d x d x dx +a 2 +b + cx( cx(tt ) dt dt 3 dt

(9)

d 3x (3) d ⎛ d 2 x (3) ⎞ x (1) ( k 3) x (1) ( k = ⎜ = lim ⎟ 2 dt dt ⎝ dt ⎠ Δt → 0 Δt

(k ) (4)

2) − x(2 ) ( k ) Δt

≈ x (1) ( k + 2)

1 ( 2

i =1

(8)

d 2 x (3) d ⎛ dx (3) ⎞ x(2) (k = ⎜ = lim ⎟ dt dt ⎝ dt ⎠ Δt → 0

(1)

( )

(7)

According to the above definitions of the derivative and the discrete form, and combined with Eq. (4), there is

Processing through a cumulative procedure, the pre-processing data sequence will generate a new data sequence, that is ( )

(6)

(5)

( )

x( ) (k

) ax ( ) ((k k

1 + c (x( ) (k 2

) bx ( ) ( k

) x ( ) ( k ))

d

) (13)

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k

According to the upper equation, when , , …, N − 3 , we get the following equations

⎧x ( ) ( ) ax ( ) ( ) bx ( ) ( ) ⎪ 1 ⎪ − c ( x ( ) (2)) x ( ) ( )) d ⎪ 2 ⎪ ( ) a ( ) ( ) bx b ( )( ) ⎪x ( ) = −ax ⎪ 1 ⎪ − c ( x ( ) (3)) x ( ) ( )) d ⎨ 2 ⎪ ⎪ ⎪ ( ) ( ) (2) ( N − 2) ⎪x ( N ) = − ax ( N − 1) − ⎪ 1 ⎪ − c ( (3) ( N − 2 ) + (3) ( N − 3)) + d 2 ⎪⎩

xˆ (0) ( k + 3) = − ax (1) ( k + 2) − bx (2) ( k + 1) 1 − c x (3) ( k + 1) + x (3) ( k ) + d 2

(14)

(15)

BN A

(21)

This equation is the Grey prediction of specific equations of third-order differential equation form. Reuse the regressive reduction equation and Eq. (4), then get x(r ) (k

Define YN

Solving the equation above, B and YN can be obtained. Take B and YN into Eq. (13) and obtain

) = x(r ) (k ) − x(r

)

(k )

(22)

Reverse derivation, and find the original data in each data class in accordance with the difference degree of the price, weather forecast and other factors, and finally plus the class averages to get the predictive value of power load.

where 3 ⎡x( ) ( ) ⎤ ⎢ ( ) ⎥ x ( ) ⎥ YN = ⎢ ⎢ ⎥ ⎢ ( ) ⎥ ⎢⎣ x ( N ) ⎥⎦

(16)

⎡a ⎤ ⎢b ⎥ A=⎢ ⎥ c ⎢d ⎥ ⎣ ⎦

(17)

⎡ ( ) ⎢ −xx ( ) ⎢ ⎢ −xx ( ) ( ) ⎢ BN = ⎢

⎢ ⎢ ⎢ (1) 1) ⎢ −xx ( ⎢⎣

Steps of power system load forecasting are shown in Figure 1. 4

1 () ( ) − ( x ( ) + x (1)) 2 1 (3) (3) − ( (2) (1)) 2

( )

x ( ) ( )

x ( )

(2)

x (

2)

1 (3) − ( (N − 2) 2 ( ) + x (N ))

STEPS OF LOAD FORECASTING

EXAMPLE ANALYSIS

According to the above model derivation, use this model to forecast the short-term power load of

⎤

1⎥

⎥

1⎥

⎥ ⎥

⎥

⎥ ⎥ 1⎥ ⎥⎦ (18)

Then, use the least squares method to estimate the sequence A and get the approximate solution of least squares A. Rewrite Eq. (15) as YN = BN Aˆ + E

(19)

Define E as the error term. Then the approximate solution of least squares A is Aˆ = ( BT B )−1 BTYN

(20)

Figure 1.

Flow chart of steps of load forecasting.

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Table 1.

Load forecasting results.

Time (h) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Average relative error (%)

Traditional BP network

Third-order grey model

Real value (MW)

Predictive value (MW)

Relative error (%)

Predictive value (MW)

Relative error (%)

73.5 60.4 57.3 53.7 53.1 52.6 55.3 59.7 65.6 83.5 88.1 95.1 91.0 84.3 84.5 86.8 86.5 87.6 86.7 87.3 87.9 99.6 97.0 83.5

74.61 61.45 56.52 52.75 54.07 51.22 56.14 60.52 66.46 82.34 89.84 93.10 92.28 81.78 83.33 87.96 88.20 86.03 87.72 88.65 89.09 98.53 100.35 82.33 1.723

1.512 1.744 1.358 1.772 1.818 2.620 1.511 1.374 1.305 1.389 1.978 2.104 1.409 2.988 1.386 1.337 1.971 1.789 1.173 1.548 1.348 1.072 3.455 1.400

72.30 59.58 56.54 54.27 52.39 53.56 56.00 60.63 66.49 82.53 86.99 93.63 92.83 85.88 83.18 85.67 85.34 89.01 88.70 88.78 89.23 98.59 95.82 84.66 1.480

1.633 1.358 1.326 1.061 1.337 1.825 1.266 1.558 1.357 1.162 1.260 1.546 2.011 1.874 1.562 1.302 1.341 1.610 2.307 1.695 1.513 1.014 1.216 1.389

each point of time in a single day. The original data comes from the load data of the past three months from a power supply bureau. In order to reduce the fluctuation of load sequence and improve the accuracy of forecasting, it is necessary to separate the weekday load data and weekend data. In this paper, use the weekday load data as the base. Use Matlab R2010b software programming to complete the simulation process. The prediction results of thirdorder grey model are compared with traditional BP network model. The prediction results are shown in Table 1. And the relative error comparison between the two models is shown in Figure 2. Compared with the traditional BP network model, the error of the third-order Grey model is smaller and more stable. The average error of the prediction results is about 1.480 percent, reaching the prediction accuracy. We can conclude that the third-order discrete series of Grey model method has higher prediction precision and better prediction effect.

Figure 2.

Error comparison between two models.

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5

CONCLUSION

The Grey prediction model has some shortcomings which cannot consider the exponential and cyclical regularity of load data. Meanwhile, the secondorder Grey model can only take into account one characteristic of the load data. Therefore, this paper proposes a third-order Grey discrete series model, which can take into account both the characteristics of the load data. Example proves this model is suitable in power system load forecasting, and is able to improve the accuracy of the prediction. REFERENCES [1] Tang Jieming, et al. 2009. Short-Term Load Combination Forecasting by Grey Model and Least Square Support Vector Machine. Power System Technology. 03:63–68. [2] Jiao Runhai, et al. 2013. Short-Term Forecasting by Grey Model with Weather Factor-Based Correction. Power System Technology. 03:720–725. [3] Zhou Deqiang & W.U. Benling. 2011. Optimization and power load forecasting of grey BP neural network model. Power System Protection and Control. 21:65–69. [4] Wang Jie, et al. 2009. Application of ant colony gray neural network combined forecasting model in load forecasting. Power System Protection and Control. 02:48–52. [5] Ge Shaoyun, et  al. 2012. A Gray Neural Network Model Improved by Genetic Algorithm for Short-Term Load Forecasting in Price-Sensitive Environment. Power System Technology. 01:224–229.

[6] Zhang Cheng, et  al. 2013. Middle and long term power load forecasting based on grey discrete Verhulst model’s theory. Power System Protection and Control. 04:45–49. [7] Zhang Jingzhi, et  al. 2007. Establishment of third order grey neural network model and its application. Modern electronic technology. 01:141–143. [8] Li Ying, et  al. 2002. Application of GM(1,1) improved model in the electric power load forecasting. Journal of Guilin Institute of Technology. 04:418–420. [9] Liao G.C. & Tsao T.P. 2003. Integrating evolving fuzzy neural networks and tabu search for short term load forecasting. Transmission and Distribution Conference and Expositon. IEEE PES: China. 7555–762. [10] Jin M., et al. 2012. Short-term power load forecasting using grey correlation contest modeling. Expert Systems with Applications. 39(1):773–779. [11] Reis A.J.R. & Silva A.P.A. 2006. Feature extraction via multi-resolution analysis for short-term load forecasting [J]. IEEE Trans on Power Systems. 21(1):392–401. [12] Li G.D., et  al. 2011. A research on short term load forecasting problem applying improved grey dynamic model. Electrical Power and Energy Systems. (33):809–816. [13] Alfares H.K. & Nazeeruddin M. 2002. Electric load forecasting: literature survey and classification of methods. International Journal of Systems Science. 33(1):23–34. [14] Yusra S. & Nanang H.F. 2011. Spatial short-term load forecasting using grey dynamic model specific in Tropical area. 2011 International Conference on Electrical Engineering and Informatics. Bandung: Institut Teknologi Bandung. E15-1.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Offshore wind power scale development trends and related policy research Y. Zeng Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China

J.S. Luo & X.L. Wang New Institute, Gouda, The Netherlands

P. Song, H.W. Huang & H.L. Bao State Grid Shanghai Municipal Electrical Power Company, Shanghai, China

ABSTRACT: In this paper, the scale development trends of offshore wind power were predicted and analysed in terms of the offshore wind power’s scale potential, combined with the history and current status of all the major countries’ offshore wind power research. On this basis, we studied the related policies of offshore wind power and summarised the characteristics and experiences of the major countries’ offshore wind power policies in the world. Then we analysed the relevant policies of our country and came up with relevant recommendations. So it has good reference value. Keywords: 1

offshore wind power; scale development; policy; power grid

INTRODUCTION

As an important form of renewable energy, wind power has many features such as it has abundant reserves, is renewable, is widely distributed, has almost no pollution and so on, making it an important direction for renewable energy development [1]. The development pace of wind power in China is fast. The new wind turbine installed capacity in 2012 were 12960 MW, the cumulative installed capacity of wind turbines was 75324 MW, which were both ranked first in the world [2–3]. However with the rapid growth of China’s wind power installed capacity, onshore wind resources were less and less, leading to the wind turbine market growth rate falling sharply. In addition, due to the limitation of current large-scale wind power grid transmission capacity, the situation of abandoned wind is severe; consuming onshore wind power has become the biggest obstacle to wind power development. Compared to onshore wind, offshore wind is good quality, close to the traditional load centers, easy to be consumed by power grid, which eliminates the problems of long-distance transmission; so in the future wind farm construction will be transferred from land to the ocean [4–6]. So far, China’s wind power is mainly onshore; offshore wind power is still in its infancy [7]. Over the years, the development of onshore wind power

has accumulated a lot of experience in construction and operation, formed a complete industrial chain and has developed a relatively complete set of relevant laws, regulations and policies on development of wind power [8–9]. To promote offshore wind power technology development, the National Energy Board announced China’s relevant development plan; in 2015 the total offshore wind power installed capacity should reach 5 million kw and in 2020 reach 30 million kw; the relevant departments have organized an offshore wind power concession bidding. The State Grid Corporation has developed a standard grid connection of offshore wind power and typical electricity access system design, for participation in the development of offshore wind power projects actively. For instance, Luneng company is involved in the Jiangsudongtai offshore wind projects. All of these will help to lay a very good foundation for the development and construction of China’s offshore wind power, so that the next few years will be the golden period of offshore wind power development [10]. However, compared with onshore, offshore wind power technology is more difficult and faces more new challenges [11–12]. For the benefit of offshore wind power development and construction, we should explore the offshore wind power development mechanism actively, to formulate a suitable development and construction management

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approach and also related policy measures for offshore wind power. 2

2.4

OFFSHORE WIND POWER SCALE DEVELOPMENT TRENDS

2.1

Gradual decline in the cost of offshore wind power

With the increasing offshore wind farm installed capacity, expanding the size of the fan size and fan arrangement, maturing technology of development, transportation and installation of high-power fan, offshore wind power costs and operating costs are in gradual decline, so offshore wind power will be further developed. 2.2

The scale and unit capacity of wind farm increases constantly

The British offshore wind farm London array first phase 630 MW project, with the largest installed capacity in the entire world, was put into operation in February, 2012. The demonstration offshore wind farm first phase project with installed capacity of 150 MW built by Guodian Longyuan Corporation in Rudong, Jiangsu province, which was put into use in December, 2011, is the largest built offshore wind farm in China. China Huarui Wind Power Corporation undertook the central budget investment project of China National Energy Board, the 10 MW-class large offshore wind power generators development and demonstration project. The project will build the world’s first, largest 10 MW-class large offshore wind power generators and be installed in the coastal area of Jiangsu Province for demonstration. 2.3

The trend of world offshore wind farm scale development

According to the European Wind Energy Association’s calculation, the installed capacity will reach 180 GW, of which the offshore wind is about 80GW. Table  1 shows the offshore wind power

Table 1. The offshore wind power development goals of major European countries in 2020. Country

Installed capacity

British German France Holland

47,000 MW 10,000 MW 6,000 MW 6,000 MW

development goals of the major European countries in 2020. The development potential of offshore wind power of China

According to estimation, the offshore wind energy resources that can be developed is twice more than onshore in China, whose wind energy reserves are much higher than onshore and the space for future development is huge. According to China’s Twelfth Five-Year renewable energy plan, it is estimated that the installed capacity of offshore wind farms in China will reach 5 GW in 2015, and it will reach 30 GW in 2020. The next five years, offshore wind farms of China will enter the accelerating development period. Before 2020, several million kW class offshore wind farms will be built in Jiangsu, Shanghai, Shandong and other sea areas, and the 10 million kW class power wind base in Jiangsu and Shandong coastal areas will be initially formed. In other sea areas, the main focus will be on building dozens of 100 MW class offshore wind farms. 3

RESEARCH ON POLICY SCHEME OF OFFSHORE WIND FARM

A favourable policy environment is able to promote the rapid development of the offshore wind power industry, and can lead to the decrease in offshore wind power investment cost, which may further promote the development of offshore wind power industry. Now the offshore wind power industry has entered a good development model. 3.1

Analyses on policies of offshore wind farm

Government policies for offshore wind power industry involve four aspects, including energy policy, approval process, financial support and grid connection. Many countries, such as Denmark, England, Germany, China, America and so on, have specifically made policies for offshore wind power industry. From the current development status of offshore wind power industry, and their development pace of offshore wind power is much faster, which also shows the importance of offshore wind incentive policies. Policies that may affect the development of offshore wind power industry can be divided into two categories: the direct policies and indirect policies. Direct policies are those that have a direct impact on the local wind power industry development; indirect policy is mainly aimed at promoting investment in wind farm projects and, providing a

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good environment and development space for local wind power industry. Direct policies mainly include: compulsory localization rate, preferential policies for local wind generator manufacturing and localization rate, tax incentives, certification and inspection system as well as research, development and demonstration projects, which could promote the development of local offshore wind industry, improve the localization rate of equipment, reduce the cost of wind generators, improve the reliability of wind generators, and promote the development of offshore wind power technology. Through the involvement of government, more funds will be attracted to be invested into the offshore wind power construction project. Indirect policies that can be taken to involve: fixed price policy, mandatory renewable energy targets, government tender or concession policy, fiscal incentives, green power markets and so on. Using these policies one can provide a long-term and certain profit electricity price space, reduce exploitation risk of offshore wind power, encourage investors and manufacturers of offshore wind power industry to provide long-term investment, inspire electricity suppliers be active in offshore wind power construction and encourage electricity users to support offshore wind power development by purchasing green energy. 3.2

Operational experience of national offshore wind power policy

1. Implement a fixed price policy, and tender for the concession Denmark, Germany and other countries have a stable, profitable fixed price policy to promote investment in the country’s offshore wind farm construction project, while the United Kingdom implements government-led offshore wind power concession projects. 2. Simplify service procedures; the use of one-stop service Denmark’s one-stop service is big leader in this. The Danish Energy Agency (DEA) as the sole competent authority greatly simplifies the workflow of Denmark; successful bidder applicants can obtain a license from the DEA’s office. The British established Offshore Renewable Energy Approval Unit (ORCU) for permit applications. 3. Extends onshore grid; distributes grid costs reasonably Denmark and the UK take offshore wind farms grid as an extension from the onshore area to the sea. Denmark make the grid cost socialization, the Transmission System Operator (TSO) and System Operator (SO) is responsible for investments of planning and implementation in

the UK offshore wind farm grid: its cost comes from transmission fees levied by the wind farm developers. Germany requires grid companies to bear transmission line construction costs of offshore wind power and the mainland grid, while in Netherlands, the developer takes charge of offshore grid. For developers in Denmark, the UK and Germany, upfront costs and financial risks are reduced, while in the Netherlands, the development costs have greatly improved. 4. Establish fan quality certification, and use the standardized system The EU has established an offshore wind turbine technology standardization quality certification system. This could help consumers build confidence in the offshore wind turbine products and thus help form a stable demand for wind turbine products. Usually, attractive local wind power market is a precondition to promote the development of wind turbine manufacturing localization, and the wind turbine technology standardization quality certification system is good for promoting the formation of the local wind power market and thus for promoting the development of the local wind turbine manufacturing industry. In addition, to some extent for emerging countries to develop wind, power, establish nationally appropriate wind turbine technology standardization quality certification system can also protect the domestic wind turbine manufacturing industry. 3.3

Present policies of offshore wind power in China and related suggestions

Some laws and regulations which are related to offshore wind power in our country are published by the government. Some of them are specifically for offshore wind power construction. At the same time, enterprises related to offshore wind power also introduced some enterprise standards and enterprise management rules. The laws and regulations including Renewable energy law, development guidance catalogue of the renewable energy industry, The eleventh five-year plan of renewable energy development, Offshore wind power development and construction management interim measures, Offshore wind power development and construction management interim measures for the implementation details, The Twelfth Five-Year Plan for wind power technology development. The development of offshore wind power in China has just begun and the laws and regulations are not perfect. For example, as for project examination and approval, the existing laws and regulations are published for oil and gas, or shipping requirements. These laws and regulations may seriously hinder the development of the offshore wind

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power, make the examination and approval procedure very complex and full of uncertainty, increasing the cost and time of the project and eventually increase the risk of failure of the project. Besides, at present as our country adapts the franchise bidding policy for grid purchase price and the lowest bidder has higher weight, it will inevitably make some powerful enterprises depress electricity price in order to be able to get the offshore wind power bidding rights and management rights; eventually it will harm the development of offshore wind. For the sake of the healthy and orderly development of China’s offshore wind power, foreign advanced experiences are also referenced, the following suggestions are put forward: 1. At the beginning of the offshore wind power development, the government should publish some policies to support the offshore wind power development. Laws and regulations systems, especially for offshore wind power should be established. 2. In reference to the Danish experience, a specialized management institution for offshore wind power should be established by the central government to improve the efficiency of offshore wind power project management. The management institution is responsible for the project examination and approving and issuing the license. 3. The country should strengthen the unified planning of construction of offshore wind power and power grid. The government should give overall consideration of wind energy resources, power capacity, the electricity market, power grid planning and then formulate the national offshore wind power development planning. After the completion of offshore wind farms, power generation enterprises should set up specialised managements which are responsible for the operation of the offshore wind farm management under the unified dispatching of the power grid and realise the coordinated operation of the offshore wind farm and power grid. 4. A standard system of offshore wind power and fan certification system should be established; the ability of design and manufacture of offshore wind turbines and the innovation ability should be improved, sea wind data collection should be strengthened and the level of offshore wind power construction and operational technology should be raised. 4

wind and policy system. Foreign experiences are also referred. The report gives a rational policy system of offshore wind power in China. These proposals aim as a guide to national policy to help the state grid company learn about the the trends of offshore wind development, to make the rights and obligations of the parties involved clear, to create good conditions for offshore wind power industry development and to promote its healthy development. ACKNOWLEDGEMENT This paper is supported by National Nature Science Foundation of China (51337005) and the project of State Grid Corporation of China for Key Technologies of Offshore Wind Farm Development and Integration. REFERENCES

SUMMARY

This paper analyses the trend of the offshore wind development both at home and abroad, as well as policies and regulations and standard management. The study analyses the developing situation of offshore

[1] Thomas Ackermann et al. 2007. Wind Power in Power Systems [M]. Chichester: John Wiley & Sons, Ltd. [2] Chen Yao. 2008. Research on Full-scale Gridconnected Power Conversion Technology for Directdriven Wind Generation System [D]. Beijing: Beijing Jiaotong University. [3] Hao Zhenghang, Yu Yixin. 2011. The influence of doubly-fed induction generator on stability of power system [J]. Power System Protection and Control, 39(3): 7–12. [4] Slootweg J.G., Kling W.L. 2003. The impact of large scale wind power generation on power system oscillations [J]. Electric Power Systems Research, (67): 9–20. [5] M. Ma, Y.H. Liu, D.M. Zhao. 2010. Research on the impact of large scale integrated wind farms on the security and stability of regional power system [J]. International Conference on Power System Technology. [6] F. Shewarega, I. Erlich, José L. Rueda. 2009. Impact of large offshore wind farms on power system transient stability [J]. PSCE. [7] Chi Yongning. 2006. Studies on the Stability Issues about Large Seale Wind Farm Grid Integration [D]. China Electric Power Research Institute. [8] Zhang J.L., Li Y.R., Xie L.L. 2014. The Novel Control Technology of Wind Turbine in VSCF Wind Power Generation System [J]. Applied Mechanics and Materials, 532: 616–619. [9] Zhenghang Hao, Yixin Yu, Yuan Zeng. 2011 Power angle transient behavior and control strategies of Double-fed wind generator. Electric Power Automation Equipment, 31(2): 79–83. [10] Bogalecka E. Power Control of a Doubly Fed Induction Generator without Speed or Position Sensor. EPE Vol. 9, pp. 224–228. [11] Hinrichsen E.N. Controls for Variable Pitch Wind Turbine Generators [J]. IEEE Transactions on Power Apparatus and Systems PAS-103(4): 886–892. [12] Sorensen J.N., Kock C.W. A Model for Unsteady Rotor Aerodynamics [J]. Wind Engineering and Industrial Aerodynamics Vol. 58, 259–275.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A power response characteristics equivalent model for the hybrid energy storage system P. Chen, F. Xiao, X.W. Wang & H. Yang State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China

Z.L. Yang & L.J. Wang State Grid Hubei Electric Power Company, Wuhan, China

ABSTRACT: Aiming at the different power response characteristics between the battery and supercapacitor for the hybrid energy storage system in the distributed generation system, this paper puts forward a power response characteristics equivalent model for the hybrid energy storage system. In the model, the transfer function is used to describe the power response characteristics for the battery, supercapacitor and power converter system. Then, control strategies of the hybrid energy storage system are used to analysis the model in simulation. The results show that the equivalent model can accurately reflect the power response characteristics of the hybrid energy storage system. Keywords: strategies 1

hybrid energy storage system; power response characteristics equivalent model; control

INTRODUCTION

The output power of distributed power (such as photo-voltaic power and wind power) is affected enormously by the external environment factors, showing the characteristics of instability and randomness. After they are accessed to the power grid distributively in high density, their fluctuating power might present a tremendous influence on the main power grid, with the consequence of harmonic pollution and system’s unstable operation. For this reason, it can stabilize the power fluctuation of the main power grid by the connection of energy storage system, so as to improve the power quality and system stability. The traditional battery provides the advantages of high energy density, easy operation and maintenance. But the disadvantages are its low power density, short cycle life and slow velocity of charge/discharge response. As a new type of energy storage elements, Super-Capacitor (SC) has the virtue of high power density, long cycle life and high efficiency of charge/discharge. Nevertheless, because of the relatively low energy density, it will reach the limit within a short period of time. As a consequence, if mixing battery up with SC to constitute a hybrid energy storage system, it can make full use of the advantages of both to improve the performance of energy storage system and stabilize the power fluctuation of system effectively.

In order to make a research on the stabilizing function of power fluctuation by the storage energy system, scholars established different kinds of models for the energy storage system. Generally speaking, the modeling of energy storage system is composed of three aspects: battery, SC and power converter system. The circuit models of both are established respectively in [3,4], based on the circuit characteristics of battery and SC, which reflected the charge/discharge characteristics of theirs preferably. Reference [5] established a circuit model of power converter system and conduct an in-depth study on its control algorithm as well. For the single energy storage system of battery, in [6,7] the battery was simulated by adopting the general battery simulator, based on which designing a control strategy. Reference [8,9] set up the DC bus type topology of hybrid energy storage system, in which battery and SC were connected by bi-directional DC converter and fulfilled the grid connection of DC bus by the use of power converter system, and also designed the control strategy of bi-directional DC/DC converter and power converter system correspondingly. A detailed circuit model of hybrid energy storage system in AC bus type is built in [10], then by the use of filter, distributed the power commands of hybrid energy storage system between the battery and SC to achieve the function of stabilizing the power fluctuation of in hybrid energy storage system effectively. By means of analyses on control model of power

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converter system, Reference [12] developed the simplified model of power response characteristic of power converter system, yet lacking the analyses on power response characteristic of battery and SC. To sum up, in the majority of studies on the energy storage system nowadays, a detailed circuit model of energy storage system will be built at first and then the control strategy of it will be tested. Because of complex circuit models of energy storage system, the circuit models of battery and SC cannot accurately reflect the response characteristic on power correspondingly. In addition, the circuit models of battery, SC and power converter system used by researchers are usually different, which has a negative effect to compare the power tracking effects of hybrid energy storage system with different control strategies. Therefore, considering the disadvantages of the exiting circuit model for energy storage system, this paper established the equivalent model for power response characteristic of hybrid energy storage system based on the response characteristic of reference power executed by battery, SC and power converter system, which was aimed at the hybrid energy storage system in AC bus type. Then the power equivalent model was tested by different kinds of hybrid energy storage system control strategies in PSCAD. The simulation results proved the power equivalent model for the hybrid energy storage system raised by this paper, with the advantages of simplicity and reliability, could be used to check out the effectiveness of hybrid energy storage system control strategies rapidly. 2

STRUCTURE OF THE HYBRID ENERGY STORAGE SYSTEM

will use the structure in common AC bus type, as shown in Figure 1. In Figure 1, the battery was connected directly to the AC bus via PCS. SC and bi-directional DC/ DC converter were linked together, then connected in parallel to the AC bus via PCS. This kind of topology structure is conducive to extend and easy to be joined up by the AC load [5]. 3

EQUIVALENT MODEL OF POWER RESPONSE CHARACTERISTIC OF HYBRID ENERGY STORAGE SYSTEM

The power response characteristic of energy storage system is not only related to its element but also closely related to the control strategy of PCS. Reference [12] takes the control strategy of PCS into account and derives the power response characteristic of PCS. When studying the real-time power tracking process of energy storage system, PCS power control process can be simplified as 1 order dynamic model, which is shown in Figure 2. In Figure  2, Pset–p and Qset–p is the active and reactive power reference command value of PCS, Pp and Qp is the actual input and output power, Tp is the power response time constant of PCS. Because of the huge difference in response speed of the reference power between battery and SC, at the same power reference level, the response speed of SC is faster, usually it has a power response time of 10 ms level, while battery of 100 ms level [6]. In a practical application, the power response time of power converter system is in 10 ms level. The power response time of all three is shown in Figure 3.

There are various kinds of topology structures of hybrid energy storage system [11], in this paper, we

Figure 2.

Figure  1. The structure diagram of the hybrid energy storage system.

Equivalent control diagram of the PCS.

Figure  3. The power response time of SC-PCS and Battery-PCS.

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Figure 4. The structure diagram of the power response characteristics.

In Figure 3, the power response time of PCS is tp which is small, and the power response time of the battery and SC is tb and tsc respectively. Now this paper connects battery and PCS together to be Battery-PCS, whose power response time is tb–p, and tb–p= tb + tp; Accordingly, the power response time of SC-PCS is tsc–p, and tsc–p = tsc + tp. Therefore, the significant difference between battery and SC on the power response time is the main reason that causes Battery-PCS and SC-PCS’s differences on response characteristics of reference power, and PCS’s access increases the power response time tp and their delayed response effect of reference power fixedly. For the battery and SC and PCS are nonlinear units, it’s difficult to obtain their precise mathematical expression on response characteristics. To describe the delayed effects and simplify the equivalent process of power response, and test hybrid energy storage system control strategy quickly and efficiently, this paper puts forward a power equivalent model for the hybrid energy storage system, which adopts transfer function to consider the response characteristics of the complex circuit to reference power as simple first-order inertia link to express its power response process. The response process of energy storage system to reference power in Figure 3 is equivalent to Figure 4. For the power response equivalent process of SC-PCS above, its mathematical expressions in the complex frequency domain is showed as follows. Psc − p ( s ) = Psc sc Gsc − p ( s ) =

p _ ref e

( s ) ⋅ G sc

1 1 + sT Tsc − p

p (s)

(1) (2)

where, Psc − p ref represents reference power of SCPCS, and Psc–p expresses its actual input and output power, and Gsc–p(s) is equivalent to its power response process, and tsc–p is its power response time constant and indicates its tracking delayed degree to power. tsc–p values 3tsc–p on account of the tracking time of first-order inertia link in the Figure 3. As shown in Figure 3, the power response characteristics equivalent model of SC-PCS above also applies to Battery-PCS, the power response time constant in the model reflects their different

response characteristics to reference power. Wherein the power response of Battery-PCS is slow, and its model’s power response time constant is large; but SC-PCS responses fast and the time constant is small. So this paper adopts transfer function to describe power response process of hybrid energy storage system and establish a complete hybrid energy storage system power response characteristics equivalent model. 4

CONTROL STRATEGY

For the response characteristics equivalent model of hybrid energy storage system, this paper adopts a low-pass filter on the active power reference instruction of hybrid energy storage system, considers the low-frequency component of active power reference instruction as reference power instruction of Battery-PCS and its high-frequency component as reference power instruction of SCPCS, and takes into account the overcharge and over-discharge protection and maximum charge/ discharge power limit control of energy storage elements. And through input and output power in real-time computing, the state of charge of the energy storage system is t

QSOC _ b

p

QSOC _ b

p0

∫ Pb − 0

Eb

p (t )dt

(3) p_N

t

QSOC _ sc

p

QSOC _ sc

p0

∫ Psc − p (t )dt − 0 Esc − p

(4)

N

For hybrid energy storage system of traditional circuit model, there are Battery-PCS and SC-PCS’ reference power instructions through the same method. The battery’s terminal voltage changes little, so we could adopt power control on the Battery-PCS directly, and considers Battery-PCS reference power instruction as targets of PCS power tracking. The terminal voltage of SC changes a lot in the charge/discharge process, so connecting bidirectional DC/DC converter between the SC and PCS, and coordinating and switching control for bidirectional DC/DC and PCS. 1. When the reference power Psc − p ref > 0 (SC discharges), PCS is in the inverter operation. The way that DC voltage of PCS is controlled by bidirectional DC/DC maintains PCS’ stable DC voltage and ensures that it works properly; meanwhile, PCS adopts power control to track the reference power instruction of SC-PCS. 2. When the reference power Psc − p ref < 0 (SC charges), PCS is in rectification work status.

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And PCS control its DC voltage to maintain its normal work. Meanwhile, the power control on bidirectional DC/DC converter is used to track the reference power instruction of SC-PCS. 5 5.1

SIMULATION ANALYSIS Parameter settings

Test 1: Compare the power response situations of traditional hybrid energy storage system circuit model (model ) and response characteristics equivalent model (model ) of hybrid energy storage system. In simulation, the inherent delay time of PCS is tp = 60 ms, and the power response time of battery is 100 ms level, and set the power response time constant of Battery-PCS as Tb–p = 0.1; the power response time of SC is 10 ms level, and set the power response time constant of SC-PCS as Tsc–p = 0.015 ; low-pass filter values Tf0 = 5. Test 2: Compare the power tracking effect of hybrid energy storage system under different control strategies. Based on the hybrid energy storage system power equivalent model, this paper uses control strategies (control strategy ) of Table 1.

Test 1 and the control method of literature [10] to test the model partly. Literature [10] takes into account the charge/discharge state of battery and SOC values of SC, adjusts filter time constant of the low-pass filter in real time and coordinates the overcharge and over-discharge protection and maximum charge/discharge power limit control for battery and SC energy storage system to optimize the regulating capacity of the whole system. In the trial, set the low-pass filter time constant initial value Tf0 = 5, and the filter time constant Tf varies between [2,8]. Set the normal operating voltage of the battery as [500 V, 550 V], whose initial value of SOC is 0.7; set normal operating voltage of the SC as [100 V, 400 V], whose initial value of SOC is 0.8, and energy storage element parameters of the battery and SC are shown in Table 1. Setting the power response characteristics equivalent model of hybrid energy storage system and the circuit model system of traditional hybrid energy storage with the control model in PSCAD. Simulation time of the system is 9 s. For convenient, analyzing the reference active power and actual active power in simulation time [70 s, 73 s]. The simulation results are obtained as shown in Figure 5.

The main parameters of energy storage element.

Parameters

Qsoc min

Qsoc max

Qsoc low

QSOC high

Pmax/W

EN/(kW⋅h)

Battery SC

0.1 0.2

0.99 0.95

0.4 0.4

0.8 0.8

±50 ±50

10 0.4

Figure  5. The situation of the hybrid energy storage system power tracking.

Figure  6. tracking.

The situation of the Battery-PCS power

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Figure 5 shows that the tracing results for reference active power of the power response characteristics equivalent model of hybrid energy storage system and the circuit model system of traditional hybrid energy storage are basically uniform. From Figure 6 and Figure 7 analysis, two kinds of model of SC-PCS power command tracking the situation is basically the same, because the power of SC-PCS traditional circuit model of SC response time is very short, can be ignored, its response to the reference power is mainly embodied in the power of the PCS response characteristics; While the two Battery-PCS models’ power tracking is slightly different mainly because of the BatteryPCS traditional circuit model can not effectively reflect the battery power response characteristics of power battery, the PCS response also just reflects the power of the PCS response characteristics. The reason of the two Battery-PC models’ power response characteristics different is that traditional hybrid energy storage system circuit model’s problem. The simulation results show that the proposed hybrid storage energy system power response characteristics model can better reflect

Figure 7.

Figure  9. The filtering time constant for control strategy .

Figure 10.

the energy storage system response characteristics of the reference power. Based on power response characteristics equivalent model for the hybrid energy storage system, using the control strategy 1 and control strategy 2 to control the system. The simulation results are obtained as shown in Figuers 9 and 10. Figure  9  shows that the change progress of hybrid energy storage power response characteristics model’s low pass filter time constant using the control strategy 2. While using the control strategy 1, the time constant is a constant. In the control strategy 1, the energy storage element’s variation range is wider. That illustrate the use of control strategy 2 can effectively reduce the charge/discharge depth of energy storage element, which can consistent with the design purpose of control strategy 2. The power response characteristics equivalent model for the hybrid energy storage system is effective. And the power response characteristics model can quickly test the validity of the hybrid energy storage system control strategies.

The situation of the SC-PCS power tracking.

6

Figure 8.

The SOC of the battery and SC.

The SOC of the battery and SC.

CONCLUSION

Aiming at the hybrid energy storage system with the AC bus, this paper puts forward a power response characteristics equivalent model for the hybrid energy storage system. The model accurately reflects the hybrid energy storage system’s power response characteristic. Based on that, using different hybrid energy storage system control strategies to test the model. The results show that

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power response characteristics equivalent model for the hybrid storage energy system proposed in this paper is simple and effective. In following study, based on the present model, some better control strategy will be put forward and used in engineering. REFERENCES [1] Lu Zongxiang, Wang Caixia, Min Yong. Review of Researches in Micro-grid. Automation of Electric Power Systems, 2007, 31(19):100–107. [2] Duryea S, Islam S, Lawrance W. A battery management system for stand-alone photovoltaic energy systems. IEEE Trans on Industry Applications, 2001, 7(3):37–41. [3] Olivier Tremblay, Louis-A. Dessaint, Abdel-Illah Dekkiche. A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles. Vehicle Power and Propulsion Conference, 2007. VPPC 2007: 284–289. [4] R.M. Nelms, D.R. Chela, R.L. Newsom. A comparison of two equivalent circuits for double-layer capacitors. Applied Power Electronics Conference and Exposition, 1999. APEC ‘99. Fourteenth Annual:692–698. [5] Li Zhanying, Hu Yufeng, Wu Junyang. The research for the large capacity battery energy storage system PCS topology structure. Southern Power System Technology. 2010, 4(5):39–42. [6] Zhang Kun, Mao Chengxiong, Xie Junwen. Optimal Design of Hybrid Energy Storage System Capacity for Wind Farms. Proceedings of the CSEE. 2012, 32(25): 79–87.

[7] Katsuhisa Yoshimoto, Nanahara, Koshimizu. New Control Method for Regulating State-of-Charge of a Battery in Hybrid Wind Power/Battery Energy Storage System, Power Systems Conference and Exposition, 2006. PSCE ‘06.2006 IEEE PES. [8] Zhang Guoju, Tang Xisheng, QI Zhiping. Application of hybrid energy storage system of super-capacitors and batteries in amicrogrid [J]. Automation of Electric Power Systems, 2010, 34(12): 85–89. [9] Tang Xisheng, QI Zhiping. Active hybrid energy storage solution of super-capacitors and batteries in independent PV system. Advanced technology of electrical engineering and energy. 2006, 25(3): 37–41. [10] Li Fengbing, Xie Kaigui, Zhang Xuesong. The hybrid energy storage system control method based on the lithium battery state of charge and discharge. Automation of Electric Power Systems. 2013, 37(1):70–75. [11] Tao Weiqing, Sun Wen, Du Chen. Research on Super-capacitor and Battery Hybrid Energy Storage System applied in Micro-grid. 2012 International Conference on Control Engineering and Communication Technology:157–160. [12] Li Yan, Cao Jing Panpan, Wang Li. A Mathematical Model of Versatile Energy Storage System and Its Modeling by Power System Analysis Software Package. Power System Technology, 2012, 36(1):51–57. [13] Yan Xiaoqing, Yang Jun, Wang Zhaoan. The mathematical model and stability analysis of the parallel type active power filter. Transactions of China Electrotechnical Society. 1998, 13(1). 41∼45, 64.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

The research for power allocation strategy of the hybrid energy storage units in distributed generation system Z.L. Yang State Grid Hubei Electric Power Company, Wuhan, China

P. Chen State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China

L.J. Wang & Y.H. Wang State Grid Hubei Electric Power Company, Wuhan, China

F. Xiao, X.W. Wang & H. Yang State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China

ABSTRACT: Aiming at the hybrid energy storage units with AC bus lines in distributed generation system, this paper puts forward a power allocation strategy for the hybrid energy storage units. The method retarded the over-charge/over-discharge phenomena of the energy storage units by adjusting the SOC values of the energy storage units in a certain range, through the power command of low-pass filter’s effective distribution between the battery and super-capacitor, according to the SOC value of the battery and super-capacitor. At the same time, it took the state of charge/discharge into account, adjust the filter time constant to ensure that the SOC value of the energy storage units is maintained in a certain range. In PSCAD, the simulation results prove the effectiveness of power allocation strategy in hybrid energy storage units. Keywords: 1

hybrid energy storage units; low pass filter; power allocation strategy

INTRODUCTION

When the output power in distributed generation system is connected to the power grid, its fluctuating power might present a tremendous influence on the grid, with the system’s unstable operation. For this reason, it can stabilize the power fluctuation of the main power grid by the connection of energy storage units, so as to improve the power quality and system stability. There are many types of energy storage units, usually including two kinds: energy type storage units, such as the battery, provides the advantages of high energy density with easy operation and maintenance. But the disadvantages of low power density, short cycle life and slow velocity of charge/discharge response are also obvious. Power type storage units, such as Super-Capacitor (SC), flywheel energy storage, has the virtue of high power density, long cycle life and high efficiency of charge/discharge. Nevertheless, because of the relatively low energy density, it will reach the limit within a short period of time.

A hybrid energy storage units can make full use of the advantages of both to improve the performance of energy storage system and stabilize the power system fluctuation effectively. In order to improve the operation and the control performance of the energy storage units, Reference [6] proved that hybrid energy storage units could improve its ability of power output by taking advantage of both battery and SC. References [7,8] build DC bus energy storage units model. In this model, the battery connected SC through bidirectional DC/DC converter, then connect the DC bus via Power Converter System (PCS). This model used the mul-hysteresis control to make energy storage system flexible, but the DC bus is not conducive to extend. According to the filter of power signal’s filter processing for power signal and the measured battery State Of Charge (SOC, State Of Charge), References [9,10] took the capability of the energy storage units into account, then adjust the time constant of the filter to make an adjustment on charge/discharge power regulating of the

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battery. Because this system only used a structure of single battery storage unit, it was difficult to make response of high frequency components of power signals accurately. By the power command of low-pass filter’s effective distribution between the battery and SC, Reference [12] pointed out that, based on the use of hybrid energy storage units with AC bus type topological structure, it could have a realization SOC value of SC optimization adjustment by adjusting the filter time constant, in consideration of the SOC value of the battery and the SC and the state of charge/discharge. However, the units have a lack of optimal adjustment for the SOC value of the battery. On the basis of existing research, aiming at the AC bus type of the hybrid energy storage units, this paper made a research of the power allocation strategy of hybrid energy storage units. The first mission is to identify the determination of equivalent model of battery, SC and PCS; Then, for the shortcomings of traditional hybrid energy storage power allocation strategies in control system, it made a design of power allocation strategy with variable filter time constant based on the SOC value of the battery and the SC and the state of charge/discharge of the two; Finally, through PSCAD simulation, the results proved the effectiveness of power allocation strategy in hybrid energy storage units. 2

STRUCTURE OF THE HYBRID ENERGY STORAGE UNITS

This paper uses the structure in common AC bus type, which is conducive to extend. The battery is connected directly to the AC bus via PCS. SC and bi-directional DC/DC converter are linked together, then connected in parallel to the AC bus via PCS, as shown in Figure 1. In this paper, the battery used PNGV model because the battery in wind power need charge/ discharge frequently, and the current fluctuated

acutely. SC used first-order RC model to simulate its charge/discharge process, while PCS of three-phase voltage rectifier/converter. Then PCS designed its control strategy to improve the charge/ discharge process of battery and SC. Bi-directional DC/DC converter was linked between SC and PCS, which could transfer power between them effectively. 3

As the voltage of battery and SC changes differently when charging/discharging, this paper designs corresponding control strategies for them with input/output active instruction given by the energy storage units, and ignores reactive power support of the energy storage units for the grid. The battery’s port voltage changes little in the charge/discharge process, and the DC voltage of PCS can maintain the normal operation of the PCS. So this paper adopts power control on the Battery-PCS directly, and considers Battery-PCS reference power instruction as targets of PCS power tracking whether PCS works in rectifier or inverter status. 1. When the reference power of SC-PCS Psc* − p > 0, the SC is in a discharged state that PCS works in the inverter operation. The bidirectional DC/DC is controlled to stabilize DC voltage of PCS to ensure the normal working in the inverter status; at the same time, the power control of PCS is used to track the reference power Psc* − p of SC-PCS. 2. When the reference power of SC-PCS Psc* − p < 0, SC is in a charged state that PCS works in the rectifier operation. And the DC voltage controlled by PCS maintains its normal work. Meanwhile, the current control on the bidirectional DC/DC converter is used to track the reference power Psc* − p of SC-PCS. 4

4.1

Figure  1. The structure diagram of the hybrid energy storage units.

CONTROL STRATEGIES FOR THE HYBRID ENERGY STORAGE UNITS

A POWER DISTRIBUTION STRATEGY BASED ON THE SOC AND CHARGE/ DISCHARGE STATUS OF ENERGY STORAGE UNITS Power allocation

According to the characteristics of hybrid energy storage units architecture, the low-pass filter performs filtering on the active reference instruction of hybrid energy storage units and considers its low-frequency components as the reference power Pb* p of Battery-PCS and high frequency components as the reference power Psc* − p of SC-PCS. The expressions in the complex frequency domain are as follows.

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Pb* p ( s ) =

1 * PHESS (s) 1 + sT Tf

(1)

* * Psc* − p s ) = PHES HESS ( s ) − Pb − p ( s ) =

sT Tf 1 + sT Tf

* PHESS (s)

(2)

where, Tf is the filtering time constant of the lowpass filter, and its value is determined by fluctuation band active reference instruction and the energy storage capacity of battery and SC. Due to the limited capacity, the battery and SC are easily in over-charge and over-discharge states in practical work which cause damage of energy storage units. Therefore, this paper takes into account the overcharge and over-discharge protection and maximum charge-discharge power limit control of energy storage units. And through the input-output power in real-time computing, the state of charge of energy storage units is t

SOC Cb p (t ) = SOC Cb

p0

∫ Pb − 0

Eb

p (t )dt

(3)

p_N

t

SOC Csc − p (t ) = SOCsc sc

p0

∫ Psc − p (t )dt − 0 Esc − p

(4)

N

Cb p (t ) and SOC Csc − p (t ) are Among them, SOC the real-time SOC value of the battery and SC; SOC Cb p 0 and SOC Csc − p 0 are their initial value respectively; Pb p (t ) and Psc − p (t ) are their realtime input-output power respectively; Eb p _ N and Esc − p N are their energy storage capacity respectively.

Due to the limited capacity of energy storage units, the way that tries to minimize the SOC variation range of energy storage units can relieve the overcharge and over-discharge phenomenon to some extent. The SOC value of energy storage units is divided into three zones as shown below, and [ SOC low , high ] is the normal working zone of energy storage units. To control the SOC value of the lithium battery and SC to maintain the value at the target zone [ SOC C low , high ] , it is necessary to judge chargedischarge state of battery and SC, and the region of SOC value comprehensively. If Pb* p 0, Psc* − p > 0, which means that BatteryPCS and SC-PCS are in a discharged state, in this case the SOC value of energy storage units becomes smaller, and tends to return to the target zone [ SOC C low low , high ] , so Tf remains unchanged. If Pb* p 0, Psc* − p < 0, the Battery-PCS is in discharged status and the SC-PCS is in charged status. At this moment. The control objectives should be to increase discharge power of the battery, reduce charge power of the SC, and decrease the filtering time constant of low-pass filter, so T f T f 0 − ΔT f . If Pb* p 0, Psc* − p > 0 , the Battery-PCS is in charged status and the SC-PCS is in discharged status. The control objectives should be to increase discharge power of the SC and reduce charge power of the battery, so T f T f 0 + ΔT f . If Pb* p 0, Psc* − p < 0 , which means that BatteryPCS and SC-PCS get charge instruction and are in a charged state. The way that changes the filtering time constant directly can’t make an optimal adjustment of the charge/discharge power between them. Therefore, the charge power should be both reduced in the meantime, and the reference power adjustment formula are as follows:

4.2 Control criterion To further alleviate the overcharge and overdischarge status of energy storage units, this paper regulates the filtering time constant of the low-pass filter by feedback through the SOC value and charge/discharge status of battery and SC to adjust the charge/discharge power of battery and SC in real-time and improve the power allocation strategy of hybrid energy storage units. The larger the filtering time constant Tf of the low-pass filter is, the smaller the cut-off frequency fc Tf is, the wider spectrum the SC are assigned, therefore, the larger power the SC charges/discharges and the less charge/discharge power the battery decreases; On the contrary, the smaller the filtering time constant is, the larger power the battery charges or discharges and the less charge/discharge power the SC decreases. Thus, changing the value of the filtering time constant can adjust the charge/discharge power of battery and SC in real time.

⎧ ⎪Pb* p( ⎪⎪ ⎨ ⎪ * ⎪Psc− p ⎪⎩

⎧⎪ SOC Cb p max SOC Cb p ⎫⎪ Pb* p max ⎨0, ⎬ Cb p max SOC Cb phigh ⎪⎭ ⎪⎩ SOC

)

⎧⎪ SOC Csc− p − SOC SOCsc− p ⎫⎪ = Ps*c− p max ⎨0, ⎬ Csc− p max − SO S Csc− phigh ⎭⎪ ⎩⎪ SOC (5)

Figure 2.

Zoning the energy storage units SOC.

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Table 1.

The control results of the filtering time constant and power adjustment.

SOC regional judgment SOC b

≥ SOC Cb

p

SOC sc − p

State of charge and discharge

phigh ;

*

Pb

SOC sc − phigh

≥ SOC Cb

p

SOC sc − p

phigh ;

SOC sc

p

Pb

≥ SOC Cb

p

phigh ;

Tf 0 − ΔTf

p

<0

Tf

Tf 0 + ΔTf

*

Tf

Tf 0 − ΔTf

*

Tf no change

*

Formula (6)

*

Tf

Tf 0 +ΔTf

p

0, Psc − p > 0

p

0, Psc − p < 0

p

0, Psc − p > 0

p

0, Psc − p < 0

*

Pb b p

SOC Cb

phigh ;

SOC sc − phigh

p

>0

Tf

Tf 0 + ΔTf

p

<0

Tf

Tf 0 − ΔTf

*

Pb

*

Pb Target area SOC Cb

Any charge and discharge status

plow

SOC sc − p

b p

SOC Cb

phigh ;

SOC sc − plow

p

< SOC Cb

SOC sc − p

plow ;

Tf

Tf 0 − ΔTf

*

Tf

Tf 0 + ΔTf

*

Tf

Tf 0 + ΔTf

*

Formula (7)

*

Tf no change

*

Pb

SOC sc − phigh

p

0, Psc − p > 0

p

0, Psc − p < 0

p

0, Psc − p > 0

p

0, Psc − p < 0

*

Pb

*

Pb

*

Pb SOC low _ li ;

SOCli

SOC sc − p

SOC sc

*

< SOC sc

Pb

>0

Tf

Tf 0 + ΔTf

p

<0

Tf

Tf 0 − ΔTf

*

p

SOC sc − p

< SOC Cb

plow ;

SOC sc − plow

Tf 0 − ΔTf

phigh

Pb SOC b

Tf

p

*

p

Tf no change

*

Psc − p > 0 Psc − p < 0

SOC b

Formula (5) Tf

*

Pb

plow

0, Psc − p < 0 >0

*

SOC sc − p

Tf 0 + ΔTf

>0

p

*

Pb

SOC sc − plow

Pb

SOC Cb

0,

*

p

*

SOC sc − p

Tf 0 − ΔTf

Tf

phigh

Pb SOC b

Tf

* Psc − p

0, Psc − p < 0

*

< SOC sc

*

p

*

SOC b

Tf no change

0, Psc − p > 0

* Pb p

Pb

*

p

*

Pb

Tf

*

Pb

Tf

Tf 0 + ΔTf

*

Tf

Tf 0 − ΔTf

*

Tf no change

0, Psc − p < 0

p

0, Psc − p > 0

p

0, Psc − p < 0

*

Pb

*

p

*

Pb

Formula (8)

0, Psc − p > 0

*

Pb

*

p

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Table 2.

The main parameters of energy storage units.

Parameters

SOCmin

SOCmax

SOClow

SOChigh

Pmax/W

EN/(kW ⋅ h)

Battery SC

0.1 0.2

0.99 0.95

0.4 0.4

0.8 0.8

±50 ±50

10 0.4

Among them, Pb* p( ) is the reference power of Battery-PCS after power adjustment, and Psc* − p( ) is the reference power of SC-PCS after power adjustment. According to the type, SOC region and charge/ discharge status of energy storage units, there can be 36 different kinds of working conditions of battery and SC as shown in Table 1. In the table, the power adjustment formula (6) (7) (8) and the power adjustment formula (5) adopt the similar way to adjust the charge/discharge reference power of the energy storage units. ⎧ ⎪Pb* p( ⎪⎪ ⎨ ⎪ * ⎪Psc− p ⎪⎩

)

⎧⎪ SOC Cb p max SOC Cb p ⎫⎪ Pb* p max ⎨0, ⎬ Cb p max SOC Cb phigh ⎪⎭ ⎪⎩ SOC ⎧⎪ SOC Csc− p − SOC SOCsc− p min ⎫⎪ = Ps*c− p max ⎨0, ⎬ Csc− plow − SO S Csc− p min ⎭⎪ ⎩⎪ SOC (6)

⎧ ⎪Pb* p( ⎪⎪ ⎨ ⎪ * ⎪Psc− p ⎪⎩

)

⎧⎪ SOC Cb p SOC Cb p min ⎫⎪ Pb* p max ⎨0, ⎬ Cb plow SOC Cb p min ⎭⎪ ⎩⎪ SOC SOC Csc− p − SOC SOCsc− p ⎪⎫ ⎪⎧ = Ps*c− p max ⎨0, ⎬ SOC C − S O OC Csc− phigh ⎭⎪ sc− p max ⎩⎪ (7)

⎧ ⎪Pb* p( ⎪⎪ ⎨ ⎪ * ⎪Psc− p ⎪⎩

)

SOC Cb p SOC Cb p min ⎪⎫ ⎪⎧ Pb* p max ⎨0, ⎬ SOC C SOC Cb p min ⎭⎪ b plow ⎩⎪

5.1

Variable low pass filtering time constant in

by using low pass filter. The over-charge and overdischarge protective control and maximum power limit control of charge/discharge are used respectively to battery and SC. Test 2 uses the hybrid energy storage units power allocation strategy which proposed in this paper. Considering the battery and SC’s SOC value and their charge/discharge state. Time constant of low pass filter is adjusted all the time. In order to optimize the storage elements’ regulating ability and relief the storage elements’ charge/discharge state. The initial time constant is set to T f 0 = 5 . Filter time constant changes between [2,8]. Normal working voltage of battery is between [500 V, 550 V]. The initial SOC value is 0.7. Normal working voltage of SC is between [100 V, 400 V]. The initial SOC value is 0.8. Parameters of battery and SC energy storage units are shown in Table 2. 5.2 Simulation results

⎧⎪ SOC Csc− p − SOC SOCsc− p min ⎫⎪ = Ps*c− p max ⎨0, ⎬ Csc− plow − SOC OCsc− p min ⎪⎭ ⎪⎩ SOC (8)

5

Figure  3. Test 1.

SIMULATION ANALYSIS Parameters setting

In order to verify the correctness of the hybrid energy storage units power allocation strategy, this paper build the hybrid energy storage units model and the modular of hybrid energy storage units power allocation strategy in PSCAD. In the simulation, setting two contrast test: test 1 is basic control strategy. The power is distributed

The simulation time is 90  s. The simulating step Δt = 10us. In order to analyze conveniently, the reference power and real input power of simulation time [80 s, 83 s] is extracted to analyze. The simulation results are obtained as shown in Figure 3. Figure 3 is the change progress of low pass filter in test 2. According to the battery’s SOC value and SC’s SOC value and their charge/discharge state, we can get the comprehensive judgment. From the analysis of Figure  4, 5, 6, the power tracking ability of Battery-PCS and SC-PCS are basically the same, with using the two kinds of control strategies. Power allocation strategy of hybrid energy storage units, which is proposed in this paper, is right. From the analysis of Figure  7, battery’s SOC value is working in normal region. In the test 1,

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6

CONCLUSION

According to hybrid energy storage units with the AC bus type, this paper proposes the variable filter time constant power allocation strategy which basic on battery’s and SC’s SOC value and their charge/ discharge state. The power allocation strategy can adjust the SOC value of battery and SC comprehensively, and reduce the change region of the SOC value, and reduce the charge/discharge phenomenon of energy storage units to some extent. REFERENCES

Figure  4. The situation of the hybrid energy storage units power tracking.

Figure  5. tracking.

The situation of the Battery-PCS power

Figure 6.

The situation of the SC-PCS power tracking.

Figure 7.

The SOC of the battery and SC.

the SC’s SOC value changes between [0.325, 0.852]. However, in the test 2, the SC’s SOC value changes between [0.375, 0.819]. Therefore, in the test 1 the change range of energy storage units’s SOC value is greater than that in the test 2. It shows that using the power allocation strategy in the test 2 can reduce the depth of over-charge and over-discharge, which consistent with the design objective.

[1] Consulting Group of State Grid Corporation of China to Prospects of New Technologies in Power System. An Analysis of Prospects for Application of Large-scale Energy Storage Technology in Power Systems. Automation of Electric Power Systems, 2013, 37(1):3–8, 30. [2] Duryea S, Islam S, Lawrance W. A battery management system for stand-alone photovoltaic energy systems. IEEE Trans on Industry Applications, 2001, 7(3):37–41. [3] Olivier Tremblay, Louis-A. Dessaint, Abdel-Illah Dekkiche. A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles. Vehicle Power and Propulsion Conference, 2007. VPPC 2007: 284–289. [4] R.M. Nelms, D.R. Chela, R.L. Newsom. A comparison of two equivalent circuits for double-layer capacitors. Applied Power Electronics Conference and Exposition, 1999. APEC ‘99. Fourteenth Annual:692–698. [5] Li Zhanying, Hu Yufeng, Wu Junyang. The research for the large capacity battery energy storage system PCS topology structure. Southern Power System Technology. 2010, 4(5):39–42. [6] Dougal R.A, Liu S, White R.E. Power and life extension of battery-ultracapacitor hybrids. IEEE Trans on Components and Packaging Technologies, 2002, 25(1):120–131. [7] Zhang Guoju, Tang Xisheng, Qi Zhiping. Application of hybrid energy storage system of super-capacitors and batteries in amicrogrid [J]. Automation of Electric Power Systems, 2010, 34(12):85–89. [8] Tang Xisheng, Qi Zhiping. Active hybrid energy storage solution of super-capacitors and batteries in independent PVsystem. Advanced technology of electrical engineering and energy. 2006, 25(3):37–41. [9] Bingchang Ni, Sourkounis, C. Control strategies for energy storage to smooth power fluctuations of wind parks. MELECON 2010–2010 15th IEEE Mediterranean Electrotechnical Conference, Valletta, 2010. [10] Xie Junwen, Lu Jiming, Mao Chengxiong. Battery energy storage system optimization control method based on the variable smoothing filter time constant. Automation of Electric Power Systems. 2013, 37(1): 96–102. [11] Zhang Kun, Mao Chengxiong, Xie Junwen. Optimal Design of Hybrid Energy Storage System Capacity for Wind Farms. Proceedings of the CSEE. 2012, 32(25): 79–87. [12] Li Fengbing, Xie Kaigui, Zhang Xuesong. The hybrid energy storage system control method based on the lithium battery state of charge and discharge. Automation of Electric Power Systems. 2013, 37(1):70–75.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on the control of temperature of batch reactors by multi-media in the reactor jacket H.B. Li, D.Y. Feng & H. Liu Mechanical Engineering and Automation College, Liaoning University of Technology, Jinzhou, China

ABSTRACT: Addressing the need of temperature control of batch reactors, which makes a great difference in multi-section reaction temperature, the jacket process with which heating or cooling multi-medium was designed, and liquid hot-or-cold mediums with similar properties which share the temperature control valve and to simplify the system structure. The design of the jacket medium auto-identifying, autoemptying and auto-switching control system, solved the problems of a complex operation process, low efficiency and an error-prone factor when a multi-medium switches to manual operation. By using the memory valve operation to identify the current media of the jacket, it worked out the difficulties of sensor measuring various medium components. The influencing factors at the time of emptying the jacket was analysed, the relation of emptying time with the pressure of compressed air was associated, and the emptying end was determined on the basis of the time, to solve the problems of detection of emptying endpoint in multi-medium and complex conditions. The practical application shows that the system has the advantages of a simple structure, medium high efficiency switching as well as high safety and reliability. Keywords: 1

batch reactor; auto-switching; medium identification; emptying jacket

INTRODUCTION

2

In the production process of the batch reactor, the reaction temperature conditions were always changing by the reaction process. Single heating or cooling medium was difficult to meet the needs of temperature control when the reaction temperature amplitude in different stages was great, so the technical needs of the temperature was met by changing the different heating or cooling mediums in the jacket at different stages of the reaction temperature. Due to the complex process of multi medium, multiple valves, complex operation process and the low efficiency of manual switching jacket medium, the swift need of process temperature control could not be met, and different mediums confused each other easily because of the false operation, which lead to production losses. In this article, addressing the needs of temperature control of batch reactor of a good chemical company, the jacket multi medium switching process was designed, the automatic switching control was introduced, and the functions of the former medium in jacket such as automatic identification, emptying and switching were embedded in the DCS control system and applied to a practical process: as a result production efficiency and reliability were improved.

THE DESIGN OF JACKET MULTI MEDIUM SWITCHING PROCESS

The temperature response amplitude of the different process steps was different to a great degree in the batch reactor reaction process of a good chemical company. According to the analysis of the process, the jacket needed to switch 4 kinds of hot-or-cold medium in a production cycle, for example, steam, hot water, cooling water and brine ice (calcium chloride). Different medium pressed the current medium in the jacket back to its storage tank by using compressed air before switching: this was named the jacket “empty” operation, which included air used for emptying and emergency water. 6 different mediums were connected by the jacket and these mediums were strictly prevented from mixing in the switching process of different mediums. Various mediums all had supply and return valves (Emergency water and cooling water shared the backwater and return circuit to discharge in the circulating pool). The liquid medium was good for filling the jacket when entering from low and discharging from high. The steam and compressed air entered from the upper part of the jacket and the steam condensate water discharged from the bottom of the jacket. In order to simplify the system, all kinds of liquid mediums went into

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Figure 1.

The flow diagram of reactor jacket medium.

the jacket through the common temperature control valve to bring out the automatic control of temperature; because the difference between the performance parameters of low pressure steam and fluid state was great, the steam temperature control valve was used independently to control the temperature. The switching process of reaction jacket multi medium is shown in Figure 1. It can be seen from Figure  1 that to complete a switch of different medium it needs operating many valves accurately. For example, the operation of switching from cooling water cooling to ice water cooling it needed 4  steps—such as closing the supply or loop of cooling water, connecting the emptying loop, closing the emptying loop and switching to the ice water circulation loop, Furthermore, it needed to operate 14 times to complete the switching valve, which had the advantages of low efficiency in manual operation and were error prone. So it was necessary to use the automatic control. 3

Figure 2.

4 4.1

The multi section temperature control system of batch rector was a part of the production line of the DCS system: the DCS system consisted of a Siemens S7-300PLC control station and operation station based on WinCC. The automatic emptying program which was embedded in the DCS system controlled the pneumatic valve by the S7-300 control station to consist different medium circulation loops, realising the identification, emptying and switching of the jacket medium, and achieved the automatic temperature control of different processes by the common control valve entered in the jacket. The structure of the control system is shown in Figure 2.

THE DESIGN OF CONTROL SYSTEM The program structure

S7-300 step procedures use the modular structure to make the functions of the clamping sleeve current media recognition, jacket pressure air operation, medium switch and emergency management, etc into different FC logic block. In the production process controlling FC function, different FC functions are called according to temperature controlling technology in the process, so that the jacket medium can switch automatically. The modular structure of the procedure is easy to cooperate each other, convenient debugging. Because the logical block calls conditionally, and can improve the utilization rate of CPU. The program structure medium switch is shown in Figure 3. 4.2

THE HARDWARE CONFIGURATION OF CONTROL SYSTEM

The structure of the control system.

The automatic identification strategy of the jacket medium

To realise the automatic switching of the jacket medium, firstly the media in the jacket need to be identified accurately, so that the current media in the jacket can be moved back to the storage tank by the circulation loop without medium confusion. In order to make the operation of emptying of the jacket medium safe and reliable, the final pass into the jacket medium must be accurately identified, no matter if one is producing normally or stopping producing for repairing and operating manually or automatically. The medium of public project in the jacket includes steam, hot water, cooling water and brine ice and so on. But substance sensors are difficult to identify the current medium in the jacket. Through the research and analysis, the recognition strategy to judge the current media in the jacket according

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Figure 3.

The structure of the program.

to the state of valve open position feedback when the medium went into the jacket (supply) valve. Just when the state of the open position of some medium valve was effective, it could be regarded that the jacket passed into this medium. The program design set identifier of the jacket medium to identify the medium in the jacket, when the identifier of different mediums was 1, it could be determined that it was this medium that in the jacket. The last operation of the medium supply valve could be identified rapidly and accurately with this method, and it was then that the jacket current medium could be deduced. The identification strategy might misjudge in two situations: (1) When the supply valve of some medium opened but did not reach the right position. Because PLC had not received the state feedback signal of the valve to a right position, the program could not judge the valve operation and the actual medium in the jacket would not change, that is why there was a misjudgment. (2) Opening another valve for the medium artificially in the field when emptying was in the process of operation, causing different mediums to mix, at this time; automatic recognition program still went on emptying through the original medium. Condition (1) belonged to the valve failure, the program would send the alarm for processing. When the processing was completed, the normal recognition would return; Condition (2) belonged to illegal operation, which was strictly prohibited in the production. Therefore, the probability of error was small, and this kind of misjudgment had not appeared in the practical application. 4.3

Jacket automatic emptying

4.3.1 The terminal judgment of emptying Called the program of jacket emptying to empty the current medium in the jacket first when switching

the jacket medium was needed. Emptying operation was the key of medium safety switch. The key of jacket emptying operation was to determine the emptying terminal accurately, to prevent mixing of different medium. Because the medium included organic and inorganic substances and mixtures and other components, the operating parameters included low temperature, high temperature and different pressure conditions, there were varies of composition and parameters. And the medium in the reactor intermittent operation of pipeline flowed unsteadily, always causing pipeline vibration, and the action of the compressed air in the liquid medium was prone to bubbles; the jacket condition was very complex and this brought in a lot of uncertain factors to determine the endpoint of emptying. The conventional terminal of the emptying was that the liquid switch detected the jacket, which was prone to misdescription, causing misjudgment of the jacket emptying. Through the research and analysis and field test, taking the emptying operation time as the judgment standard of jacket emptying terminal was an economical and feasible scheme. According to the pipeline energy balance between jacket entrance and medium storage entrance, the following formula was derived P1 1 2 P2 1 2 + u1 = + u2 ρ 2 ρ 2

∑ hf

(1)

In the formula, P1 is the pressure of compressed air for the jacket, Pa; P2 is the medium storage tank entrance pressure, P2 = 0 (gauge pressure) Pa; u1 is the flow rate of the liquid in the jacket entrance, u1 ≈ 0 m/s; u2 is the flow rate of the liquid in the storage tank entrance, u2 = u, m/s; u is the flow rate in the pipe; is the total resistance of pipeline. The flow rate of the system changed little, for the circulation loop that had a certain structure (k is constant). Consolidation formula (1) to get P1 ⎛ 1 ⎞ 2 = +k u ⎠ ρ ⎝2

(2)

Type (2) can be written as u

P1

emptying time is proportional with the velocity of the fluid in the circulation loop, then

θ = K P1

(3)

K is a constant.

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4.3.2 The logic of jacket emptying The logic of Jacket emptying was designed into an independent FC function, to be called for the production process when the temperature changed. The current medium in the jacket needed judging first to execute the emptying. Through closing the valve of the current medium, the opening or closing of the valve on the corresponding circulation, the pathway for compressed air and the medium storage tank was formulated, followed by the opening of the valve of compressed air to emptying. Closing of the valve of compressed air was done when it reached the terminal time of emptying. The subprogram of jacket emptying is shown in Figure 4. 4.4

The switching of jacket medium

When the technological needs of the production process required a medium heating or cooling, the program would judge whether the sub-program should be called according to the identifier of the current medium in the jacket. The operation condition of medium switching was that the current medium in the jacket and the medium needed to switch were common or to be air in the jacket, once the current medium in the jacket and the medium needed to switch were different, the program would operate emptying automatically and when it was completed, medium switching would work automatically. 5

CONCLUSIONS

The jacket of batch reactor is needed to transfer into multi hot-or-cold medium to meet the requirement of temperature control in the production process.

Figure 4. jacket.

The flow chart of the program for emptying

To get the emptying time coefficient K of reactor in different structures through field experiment, and calculate the emptying end time according to formula (3), then consider a certain safety margin to be the basis of the judgment for the terminal of jacket emptying.

1. The liquid mediums share the temperature control valve in the jacket multi-media switching processes. 2. By using the memory valve operation to realise intelligent identification of current media, which does not add any hardware. Its structure is simple and reliable and it has not seen miscarriage of justice in the practical application. 3. The emptying end is determined on the basis of the time, to solve the problems of the difficulties of sensor measuring components in multimedium and the complex conditions, which simplify the system structure.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Three-phase voltage source PWM rectifier based on space-vector algorithm and one-cycle control Long-Fei Ma, Cheng Gong, Zhong-Jun Chi & Jia-Mei Cao State Grid Beijing Power Research Institute, Fengtai District, Beijing, China

Lin Zhu School of Electrical Engineering and Automation, Tianjin University, Tianjin, China

ABSTRACT: The traditional three-phase voltage source PWM rectifier has a complicated structure and can easily produce harmonic voltages. Based on the study of PWM rectifier control algorithm in a single week, the control law of three-phase voltage source PWM rectifier is deduced, and the vector mode of one-cycle control algorithm is proposed in this paper. According to the grid voltage cycle which is divided into six power frequency intervals, in each interval a separate one-cycle control model is established. This strategy can reduce the switch tube cut-off frequency, at the same time it also does not need a multiplier, and its control law is easy and can work at a constant frequency. On the basis of the analysis, modeling on the simulation and experimental study, the results show that it has good dynamic response and static performance, and has a high power factor and low current distortion. Keywords: 1

one-cycle control; space-vector; PWM rectifier; fixed frequency; without multipliers

INTRODUCTION

In the electric vehicle charging field, with the advantages of the grid side current harmonic, the low unit power factor, energy bidirectional flow and constant DC voltage, the PWM rectifier has been gradually getting attention; this is especially in the research of V2G, V2H, where it has been used in the research of application. Direct Current Control with the advantages of fast dynamic response is widely used in many control methods  [1]. At present, the methods of direct current control are: average current, hysteresis current, forecast current and so on; these control methods require using of multipliers to produce the command current signal. The three-phase PWM rectifier is a system of multi-input multioutput and time-varying strongly coupled—so nonlinear distortion generated by the multipliers will cause system instability and harmonic distortion of input current [2]. So literature [3] proposed a new type of threephase voltage-type PWM rectifier control method that is the one-cycle control. One-cycle control was a new control method proposed by American professor Keyue M Smedley and Cuk in the1990s. It is a non-linear control technology, Achieve decoupling control of three-phase PWM Rectifier use the control that resettable integrator enables the use

of the amount charged in a switching cycle track changes in a given reference can be converted into a linear non-linear switch. And because the technology of one-cycle control, both in steady-state and transient charged amount it can keep track given reference value changes, which can in switching cycle perturbations can effectively resist the power supply side, eliminate the steady-state and transient errors. Literature [3] is used showing these advantages to build a resettable integrator, RS flipflop, a comparator structure, control mode without multipliers, no power supply voltage detector, and its experimental study to prove the rightness of the above conclusions. Based on literature [3], and literature [4] it proved the One-cycle control and SPWM equivalence relation, and researched the desigin methods of the controller parameters. Literature [5] and [6] used the algorithm of space vector control and one-cycle ontrol method in the grid inverter. Using the space vector algorithmand reducing the number of turn-off switches within a cycle time, reduced losses and improved the efficiency of the system. Based on the above literature, through studying the three phase voltage PWM Rectifier of onecycle and deriving the control law, derive a spacevector mode controller of one-cycle, combining the space vector control method and one-cycle, dividing the grid voltage in six intervals according tothe

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frequency cycle, establishing a separate one-cycle control model in each interval. One-single mode controller of space-vector has the advantage of no multiplier, simple structure, constant frequency operation, simple control and its only two switch groups working in each interval, reducing switching losses and improving efficiency. Finally completing experimental research of one-single mode controller of space-vector verifies the theoretical analysis.

2

CONTROL THEORY

2.1

Mathematical model with one-cycle control

Figure  1 is the topological structure of PWM rectifier. The device is an ideal device; it ignored the phenomenon of magnetic saturation etc, the relationship between the voltage in Figure 1. ⎧uAo = uAN + uNo ⎪ ⎨ uBo = uBN + uNo ⎪u = u + u CN No ⎩ Co

(1)

The switching frequency is much larger than the frequency of supply voltage, so the inductance value is relatively small; the voltage across can be negligible, there: ⎧uAo ⎪ ⎨ uBo ⎪u ⎩ Co

usa usb usc

uNo

uAo + uBo + uCo = uAN + uBN + uCN + 3uNo = 0

(3)

⎧ ⎪usa ⎪ ⎪ ⎨ usb ⎪ ⎪ ⎪ usc ⎩

Topological structure of PWM rectifier.

1 uAN − (uAN + uBN + uCN ) 3 1 uBN − (uAN + uBN + uCN ) 3 1 uCN − (uAN A + uBN + uCN ) 3

(5)

The rectifier at the same bridge arm of the two switches are turned on in a complementaryset. PWM Rectifier switching cycles is Ts; the duty ratio is di(i = a, b, c): the switch Sa2, Sb2, Sc2 the conduction time in each clock cycle is daTs, dbTs, dcTs; the above three switches, Sa1, Sb1, Sc1 the conduction time in each clock cycle is da’ = (1 − da)Ts, db′ = (1 − db)Ts, dc′ = (1 − dc)Ts. So a, b, c with respect to the point N of the voltage is: ⎧uAN ⎪ ⎨ uBN ⎪u ⎩ CN

( ( (

da )udc db )udc dc )udc

(6)

Take the formula (6) into the formula (5), get the result of a matrix form. The equation can be obtained by the relationship of the duty cycle and power phase switching voltage: ⎡ 2 ⎢− 3 ⎢ ⎢ 1 ⎢ 3 ⎢ ⎢ 1 ⎢⎣ 3

1 3 2 − 3 1 3

1 ⎤ 3 ⎥ ⎡d ⎤ ⎡usa ⎤ ⎥ a 1 ⎥⎢ ⎥ 1 ⎢ ⎥ db = ⎢usb ⎥ 3 ⎥ ⎢ ⎥ udc d ⎢ ⎥ ⎢⎣ dc ⎥⎦ ⎣ usc ⎥⎦ 2 − ⎥ 3 ⎥⎦

(7)

The formula (7) shows that the coefficient matrix is a singular matrix, without an only solution of the equation, the general solution can be expressed as: ⎧ ⎪d a ⎪ ⎪ ⎨d b ⎪ ⎪ ⎪ dc ⎩

Figure 1.

(4)

Take formula (2), formula (4) into formula (1):

(2)

usa, usb, usc are the three phase voltage power supply. In Figure  1, Grid voltage three-phase equilibrium, so ua + ub + uc = 0, added the three equations of the Formula (1), get that:

1 (uAN + uBN + uCN ) 3

k1 + k2

usa udc

k1 + k2

usb udc

k1 + k2

usc udc

(8)

k1, k2 are the undetermined coefficients, take formula (8) into formula (7), getting the range of k1, k2:

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⎧ k2 = −1 ⎪ usi ⎨ usi ⎪ u < k1 < 1 + u (i dc ⎩ dc

2.2 a, b, c )

(9)

From the nature of voltage-type PWM Rectifier, so usi < udc (i = a, b, c), take the formula (9): ⎧ k2 = −1 ⎨ ⎩0 k1 2

(10)

Achieve unity power factor, the need to satisfy every phase inductor current to follow the corresponding phase voltage sinusoidal variation, the equation is: ⎧usa ⎪ ⎨usb ⎪u ⎩ sc

isa Re isbRe isc Re

(11)

Re is the equivalent input resistance of the circuit, isa, isb, isc is the three-phase inductor current. take formula (10), formula (11) into the formula (8) can be obtained: ⎧ da ⎛ d ⎞ = U m ⎜1 − a ⎟ ⎪Rs isa = U m U m k1 ⎠ ⎝ k 1 ⎪ ⎪⎪ db ⎛ d ⎞ = U m ⎜1 − b ⎟ ⎨Rs isb = U m U m k1 ⎠ ⎝ k 1 ⎪ ⎪ d ⎛ d ⎞ ⎪ Rs isc = U m − U m c = U m ⎜1 − c ⎟ k1 ⎠ ⎝ k ⎪⎩ 1

(12)

Um = Rsk1udc/Re, Rs is the sampling resistor of the circuit. The formula (12) is the one-cycle control method’ mathematical model of the three phase PWM voltage rectifier. It describes the relationship between each phase inductor current and the DC voltage and the corresponding switching duty ratio. The frequency Switch-off is much larger than the frequency of the grid-side power, so Um can be seen as a constant. In a switching cycle Ts, the expression form of the integral formula (12) is: ⎧ ⎪Rs isa = U m ⎪ ⎪ ⎨Rs isb = U m ⎪ ⎪ ⎪ Rs isc = U m ⎩

1 daTs U m dt τ ∫0 1 dbTs U m dt τ ∫0 1 dcTs U m dt τ ∫0

Model space-vector mode of one-cycle control

From literature [6] we know that One-Cycle Control of the space vector model has divided the grid voltage frequency voltage into six regions, Within each region, only two of the switches have been in working condition, reducing the switching losses and improving the system’s efficiency. The coefficient matrix in the formula (7) is a singular matrix, so the equation is not unique. So set the di (i = a, b, c) is 0 or 1; get to determine the value of the other two. In Figure  2, A frequency cycle of three phase voltage is divided into six regions, setting the di (i = a, b, c) is 0 or 1 in each regions; get a set of solutions of the equation; Determine the mathematical model of one-cycle control in a specific region. In the 0 ∼ 60°, Set db = 1, db′ = 0, so Sb2 is in a conducting state in this region, Sb1 is in the off state; set db = 1; take formula (11) into formula (7): ⎧ ⎡1 − da ⎤ ⎡2 1 ⎤ ⎡isa ⎤ ⎪U m ⎢ ⎥ = Rs ⎢1 2 ⎥ ⎢ i ⎥ 1 − d ⎨ ⎣ ⎣ ⎦ ⎣ sc ⎦ c⎦ ⎪ d =1 b ⎩

(14)

Um = Rsudc/Re, By controlling the duty cycle da, dc to satisfied the formula (14), So that each phase inductor current isa, isc to follow the corresponding phase voltage usa, usc, so isb follow usb, achieve unity power factor. The other five control models can be obtained in the same method; each model is substantially similar, so it can be summed up as the same form: ⎧ ⎡1 − d p ⎤ ⎡ 2 1 ⎤ ⎡i p ⎤ ⎪U m ⎢ ⎥ = Rs ⎢ ⎥⎢ ⎥ ⎨ ⎣1 − d n ⎦ ⎣1 2 ⎦ ⎣ in ⎦ ⎪ ⎩ dt = 1

(15)

(13) Figure  2. Power supply voltage power frequency division chart.

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Dp, dn, dt represent the duty cycle of different switch, to different areas, ip, in, dp, dn, dt have been given different assignments. Figure 1 shows the different areas of the inductor current and the switch to select the operating state: In Table 1 represents that the switch has always been in the conduction state, while 0 represents off state. In the combination of Table 1 with equation (15), a one-cycle control circuit based on vector mode of PWM rectifier could be achieved. Literature [7] shows that the use of a single reset integrator, compared with each phase current using an individual reset integrator, can automatically eliminate the impact of the variable integration time constant on the line side current, and thus has better adaptability of parameters, so that in this circuit a single reset integrator is used. As shown in Figure 3, the circuit structure is divided into four parts: (1) region determining circuit, which determines the region of power supply voltage vector; (2) multiplexing circuit, depending on the regions an appropriate input supply current is selected; (3) single-cycle control core circuit, including adders, two comparators, a reducible integrator and two RS triggers, and the integration time constant is set to switch cycle; (4) output logic selection circuit; the Sp, Sn are assigned to correct switch separately.

Table 1. Voltage vectors of space-vector algorithm and one-cycle control. Region

ip

in

Sa1

Sa2

Sb1

Sb2

Sc1

Sc2

0∼60° 60∼120° 120∼180° 180∼240° 240∼300° 300∼360°

ia −ib ib −ic ic −ia

ic −ic ia −ia ib −ib

!Sp 1 !Sn Sn 0 Sp

Sp 0 Sn !Sn 1 !Sp

0 Sp !Sp 1 !Sn Sn

1 !Sp Sp 0 Sn !Sn

!Sn Sn 0 Sp !Sp 1

Sn !Sn 1 !Sp Sp 0

Figure 3.

The control circuit of PWM rectifier.

3

SIMULATION AND EXPERIMENTAL ANALYSIS

Through the above theoretical analysis, a system simulation model is built in Matlab/Simulink, as shown in Figure 4. The parameters of the system simulation model are as follows: AC power supply voltage in the network side us = 220 V, filter inductance L = 3.2 mh, DC capacitor C = 2000 F, the load in the DC side is 60o, when t = 0.4 s a 120o load is added suddenly, power frequency is 50 Hz, switch frequency 10 kHz. Figure 5 is the waveform of a three-phase current in the network side and the waveform of a phase voltage and a phase current of PWM rectifier in the steady state; as shown in the Figure 5, the waveform of three-phase current is good and symmetrical. Following with a phase supply voltage, a phase current variants is sinusoidal, whose THD is 2.73%; from literature [8] we can see that the power factor could be go up to 0.99. As shown in Figure 6, we can see that the variation of current is not obvious when load is added suddenly, which is still symmetrical and could reach a stable state in a short time. A phase current could follow a phase voltage as sinusoidal variation when load is added suddenly, and a DC voltage could be restabled and reachreference value in a short time. As shown in Figure 7, we can see that compared with the traditional control of PWM rectifier, the state of high frequency switch reduces at the same

Figure 4.

System of PWM rectifier.

Figure 5.

The wave of current and voltage.

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the wastage of the switch tube and improving the efficiency of the system. In addition, experiments verify that the rectifier power factor reaches up to 0.99, the power factor closes nearly to unit 1, and low current distortion is achieved; simultaneously the DC output voltage is stable and has good dynamic response when there are load variants. So this technology has a very good application prospect. ACKNOWLEDGEMENT In this paper, the research was sponsored by the National High Technology Research and Development Program (“863” Program) of China (No. 2011AA05A109). REFERENCES

Figure 6.

The wave of current and voltage.

Figure 7.

The wave of PWM switch.

time, in each 60o interval; only two switch tubes work in high frequency conditions, which can significantly reduce the wastage of the switch tube, and improve the efficiency of the system. 4

CONCLUSION

In this paper, in the combination of the space vector algorithm and one-cycle control method together, and through theoretical derivation, a vector model one-cycle control of three-phase voltage type rectifier is designed. The values of this rectifier are that it has reliable work performance, constant switch frequency, simple engineering application, is easy to control, and a multiplier is not needed to design, and so on; According to the power frequency, the grid voltage is divided into 6 intervals at the same time, which makes only two switches in the high frequency work state in each interval, reducing

[1] Min Dong Ki, et al. Direct digital current control of a three-phase PWM converter based on a new control mode with a delay and SVPWM effects. IEEE IECON, 1998, 2: 774∼779. [2] Chen Yang. Three-phase boost-type grid-connected inverter [J]. IEEE Trans on Power Electronics, 2008, 23(5):2301–2309. [3] Zhang Chunjiang, Gu Herong, Zhao Qinglin, et al. Three-phase Voltage Source Type PWM Rectifier by One-cycle Control Without Multipliers [J]. Transactions of China Electrotechnical Society, 2003, (6): 28–32. [4] Yang Xi-jun, Yao Su-yi, Zhang Zhe-min, et al. Three phase SPWM Rectifier With One Cycle Control [J]. Proceedings of the Chinese Society of Universities for Electric Power System and its Automation, 2011, (1): 108–113. [5] Hou Shi-ying, Tuo Yuan-ke, Fang Yong, Zeng Jianxing. Three phase vector model of one cycle control of dual buck inverter [J]. Power System Technology, 2010, (5): 128–132. [6] Chong ming Qiao, Keyue M Smedly. Three-phase Gride-Connected Inverters Interface for Alternative Energy Sources with Unified Constant-frequency Integration control. IEEE, 2001, 2675–2682. [7] Du Xiong, Zhou Luo-wei, Luo Quan-ming. Effect of integration time constant in one cycle control of three phase in PFC [J]. Proceedings of the CSEE, 2006, (9): 120–125. [8] Zhang Hou-sheng. Research on High Power Factor Rectifier Based on One Cycle Control [D]. Xi’an: Northwestern Polytechnical University, 2005. [9] Cheng Qi-ming, Cheng Yin-man, XUE Yang. A summary of current control methods for threephase voltage-source PWM rectifiers [J]. Power System Protection and Control, 2012, 40(3): 145–155. [10] Ding Qi, YAN Dong-chao, Cao Qi-meng. Research on design method of control system for three-phase voltage source PWM rectifier [J]. Power System Protection and Control, 2009, 37(23): 84–87.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Joint positioning method with radar based on wavelet entropy Hui Yu, Jun Liu, Min Wang & Rong Guo State Key Laboratory of Astronautic Dynamics, Xi’an, China Xi’an Satellite Control Center, Xi’an, China

Yue Yang Xi’an Jiaotong University, Xi’an, China

ABSTRACT: In order to obtain full trajectory parameters, multiple-radar tracking and positioning technologies are often adopted in the exterior trajectory measurement system. Addressing the problem that visible positioning deviation is, in the local time segment, caused by the restriction of radar station distribution, a joint positioning method based on wavelet entropy is presented in this paper. Firstly, radar signal is expanded in multiple frequency bands by using a wavelet multi-scale transformation. Then the index weight of each single device is determined according to the wavelet entropy discriminance and variation coefficient method. Finally, the results of fusion positioning are obtained with the use of wavelet reconstruction. The effectiveness and reliability of the algorithm is verified by processing of real data. The results show that this method can improve the accuracy of trajectory calculating results. Keywords: exterior trajectory measurement; wavelet transform; joint positioning; wavelet entropy; coefficient of variation 1

INTRODUCTION

In the spacecraft flight testing task, modern radar is regarded as the main outer trajectory tracking measurement equipment, which has target limitations that directly affects the correctness and accuracy of the flight trajectory calculating. With the development of China’s space technology, the precision of exterior trajectory measurement and orbit determination needs to be higher and higher. If only a single device is used for target tracking, it is difficult to get an accurate state estimation. Therefore, the optimal estimation of the final flight trajectory parameters is obtained with the full use of different data sources. Radar joint positioning means that the target positioning measuring data from more than one radar and different stations are fused to form the newer and better target positioning information. Wavelet entropy[1–3] is a method with a new measurement sequence complexity, which is the combination of wavelet analysis and information entropy. In this paper, the wavelet entropy theory is introduced to the exterior trajectory data processing system in an explorative mode to perform target positioning solution. Later the characteristics of different equipment positioning resulting in error at different scales are analysed by using

multi-resolution of the wavelet to find the weights of the stability and precision of the positioning characterization. Then the final fusion results are obtained through using the wavelet reconstruction. The results show that this method will b on the basis of the effective absorption of useful information. It is a kind of effective and feasible multi-sensor information fusion method, which has a good practicability in the post-highprecision data processing. 2 2.1

WAVELET ANALYSES AND WAVELET ENTROPY Wavelet analysis

Wavelet analysis is a more flexible method of signal analysing and processing. Wavelet has the characteristic of good space locality, which can not only extract the signal from overall characteristics, but also reflect the instantaneous signal changes in a local time or frequency domain—especially for the processing of non-stationary and nonlinear signals[4]. Give a time series of signal x(t ), for discrete wavelet decomposition and at the decomposition scale j, the high frequency detail coefficient dj(t) and low frequency approximation coefficient aj(t) are extracted. The signal components are Dj(t) and

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Aj(t) through a single branch reconstruction, the original signal can be represented as J

x(t ) = ∑ D j (t ) + AJ (t )

(1)

j =1

short abnormal signals can be found in the Wavelet transform based on the definition of Wavelet entropy. Suppose that E {E1, E2 , , EJ } represents energy value in each frequency band after the signal x(t ) is decomposed according to formula (1), formula (3) can be obtained as follows[7]:

where J is decomposition layer. For convenience, usually make AJ (t ) = DJ +1(t ) , then get the formula

J

pi ( E ) = Ei

x(t ) = ∑ D j (t ).

where pi ( E ) is the Wavelet variance in the scale i. J ∑ pi ( E ) = 1 can be obtained clearly.

j =1

i =1

Information entropy

Information entropy[5] means the uncertainty of the random variables, or random events in the form of statistics. Assume that the system may have m different states, and the probability of each state is respectively p1 p2 , …, pm , which meets the conditions: m 0 pi 1 i = 1, 2, , m ; ∑ p i = 1. i =1

Combined with the definition of information entropy, the Wavelet energy spectrum entropy of signals can be gotten according to formula (2): J

HWE

m

(2)

i =1

That is the presence of state probability and its product of logarithm and the opposite. 2.3

Wavelet Entropy

The Wavelet Entropy[1–3,6] (WE) theory was proposed by Blanco in 1998 on the basis of the Wavelet transform, which is the combination that a signal is based on multi-scale energy spectrum. And its basic idea is to process the Wavelet transform coefficient matrix into a probability distribution sequence, and to reflect the degree of sparse coefficient matrix with the sequence of Entropy. That is to analyze the order degree of the distribution of the signal probability. Because the Wavelet entropy is a combination of the unique advantages of an irregular abnormal signal for Wavelet transform and the statistical properties of the signal complexity for information entropy, it has now become a very active research field, both at home and abroad. Its application domain is very wide. In the traditional statistical analysis method, the entropy is often directly computed according to the probability distribution of the signals. But if the abnormal signals have small amplitude and short duration, the proportion of the signal statistical distribution is small, then Wavelet transform can enlarge the local features. Therefore, small and

∑ pi ( E ) l

pi ( E )

(4)

i =1

3

The Shannon entropy of the system is defined as: H ( x ) = − ∑ p i ln pi

(3)

i =1

J +1

2.2

∑ Ei

RADAR DATA JOINT POSITIONING BASED ON WAVELET ENTROPY

In the task of aerospace measurement and control, due to the restriction of its own design and the external environment, the measurement results in high elevation and overhead paragraphs has clear deviation[8]; combination weighting must be performed with the use of multi radars to get the optimal estimation for final trajectory parameters. If the difference between the standard trajectory and the positioning results of a radar is smaller, the calculated results will have the higher credibility, and the data will have higher importance. According to the mentioned above, you can see that the weight shows that the characteristic plays a significant role in the judgment and decision. Wavelet entropy can well reflect the reliability of eachradar positioning result. Therefore, the Wavelet transform, entropy and weight are combined in this paper to discuss the application of Wavelet entropy in a more multi radar joint positioning. If there are m radars to measure targets respectively, the positioning data of flight vehicle can be obtained from the i radar in the period of k, that is Xi(k) ( i 1, , m; k , …, n ). The standard trajectory data is X 0 ( k ) , and X 0i ( k ) represents the difference sequence between X i ( k ) and X 0 ( k ). The joint positioning from m radars means that the optimal estimation are determined according to the contained information from X1( k ), , X m ( k ) . The steps of this algorithm are described as follows: 1. The sequence of Xi(k), X0(k), X 0i ( k ) can be decomposed by using the multi-resolution of

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wavelet. In engineering applications, a very important problem is how to select wavelet function. Because the Daubechies wavelet (dbN) is orthogonal and tight, as well as being sensitive to irregular signal, the effective analysis and comprehensive ability can be provided to the signals. Through the testing and analysing of different methods, it is considered that the Daubechies wavelet is ideal for the data processing of exterior trajectory. Therefore, Wavelet transform of the differential signal is performed by using db3. Figure  1 stands for differential signal wavelet decomposition of a radar and standard trajectory. From Figure 1, it can be seen clearly that the real and different scale fluctuations are decomposed with wavelet analysis method. And the characteristic of the signal can be retained effectively. 2. According to formula (5), the wavelet transform coefficient D j ( k ) of each measuring device is standardised in order to eliminate the influences of different units and different metric.

qj k =

min D j k ) j

0

D j ( k) k)

0

max D j k ) − D j ( k ) j

0

Dj k ) − Dj (k k)) + λ max D j (k k j

0

Dj (k )

yj k =

, ,

differential

uj =

signal

wavelet

sj

(7)

yj

The weight coefficient is determined as follows: uj θj = m (8) u ∑ j j =1

3. According to the formula (3), the Wavelet variance of different radar positioning data is calculated respectively in various scales, and the Wavelet entropy based on the corresponding energy calculation sequence distribution is calculated as follows: J

HWEi

radar

(6)

of variation[9]:

∑ pi ( E ) l

pi ( E )

(9)

i =1

4. Based on the principle of linear minimum mean square error, a weighted fusion is performed for the entropy at each level for single radar by using weight θj and a group of fusion results are obtained. θj can highlight the contribution to the joint positioning made by good tracking of single radar. 5. Wavelet entropy of each layer after data fusion should be calculated, and the Wavelet entropy weighting should be performed to the coefficient at each layer based on wavelet inverse transform formula. Meanwhile, the original frequency positioning results should be reconstructed as the final joint positioning results. 4

Figure 1. The decomposition.

J

1 n To calculate the mean value y j = n ∑ y j ,k and k =1 1 n 2 standard deviation s j = ∑ ( y j ,k y j ) , n 1 k =1 then to calculate the corresponding coefficient

, n)

where λ is distinguish coefficient. And λ = 0.5 is usually used in engineering applications. D 0j D 0j (1), D 0j D 0j ( n )} is the wavelet transform coefficients of standard trajectory data. D j {D j (1), ), D j ( ) D j ( n )} is the wavelet coefficients of the data from each radar. As to q j k normalized processing is performed as follows:

j 12

∑ qj k

k =1

(5) (

qj k n

SIMULATION AND ANALYSIS

During the exterior trajectory post-processing, data is often processed with the use of weight average algorithm. The comparison and analysis between the current method and the method adopted in this paper is performed as follows. Considering the high precision of differential GPS data positioning, the GPS results are regarded as standard trajectory. The current method and

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the new method are respectively used in fusion processing of three radars in a task. The difference curve between the standard trajectory and the trajectory of the two methods is shown as Figures 2 and 3. From the figures we can see, the trajectory determined by using weight average algorithm begins to deviate after 520  seconds. Compared with the standard trajectory, its deviation is obvious. With the use of new method, the occurrence of this situation can be effectively avoided. Through the reasonable distribution of the weights, the trajectory follows a trend-stationary compared with the standard trajectory, which greatly reduces the influence on calculating trajectory brought about by the results of the abnormal single device positioning to ensure the reliability of the trajectory. The accuracy parameters obtained from two methods are shown as Figures 4 and 5. The results have shown that the new method can effectively improve the precision of trajectory. In order to get better application effect of evaluation algorithm, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are introduced to measure the accuracy of the algorithm[10]. The specific results are presented as Table 1. MAE =

1 n ∑ ξt − ξˆt n t =1

(10)

Figure 2. Difference curve of X direction of coordinate.

Figure 4.

Position accuracy curve in X direction.

Figure 5.

Velocity accuracy curve in X direction.

Table  1. methods.

Statistical results calculated by different Current method

New method

Position

MAE (m)

RMSE (m)

MAE (m)

RMSE (m)

X direction Y direction

104.87 272.12

132.28 328.57

63.76 78.58

79.65 108.43

RMSE =

1 n ∑ (ξt − ξˆt )2 n t =1

(11)

where ξt is the estimated value, ξˆt is the standard trajectory data. From the statistical results we can see, the error indicators of weight average method is bigger than the new method, which shows that this method is superior to the traditional weight average algorithm. 5 Figure 3. Difference curve of Y direction of coordinate.

CONCLUSION

In the tasks of spacecraft flight control, the difference positioning result often appears between

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different tracking periods and different measurement devices. In order to obtain high precision of positioning, a joint positioning algorithm based on Wavelet entropy is studied in explorative mode. Through the application of the tasks, the results show that the useful information in the original signals can be separated by using the wavelet analysis method, which can avoid the loss of valid data. Wavelet entropy can increase the credibility of the results and improve the precision of data processing, which provides an effective method to data processing in the subsequent task. ACKNOWLEDGEMENTS This research was supported by the National Natural Science Foundation of China (61473222, 41274018, 61231018). All authors would like to express our appreciation for the support and direction from our colleagues Jiashong Wang, Shaolin Hu, Shuqiang Zhao and Weibing Du (State Key Laboratory of Astronautic Dynamics). REFERENCES [1] Zhang Hua-rong et al. 2012. Research on the detecting methods of singularity in deformation signal based on two kinds of wavelet entropy [J]. Journal of Coal Science & Engineering, 18(2): 213–217.

[2] Rosson O.A et al. 2001. Wavelet entropy: a new tool for analysis of short duration brain electrical signals [J]. J Neurosci Meth, 105(1): 65–75. [3] Blanco S. et al. 1998. Time-frequency analysis of electroencephalogram series (III): information transfer function and wavelets packets [J]. Physical Review E, 57(1): 932–940. [4] Yang Lei et al. 2009. Forecasting for non-stationary errors of ship-measured data based on wavelet [J]. Systems Engineering and Electronics, 31(4): 930–933. [5] Zhang Xianqi & Liu Huiqing. 2003. The Application of Entropy Weight Coefficient Method to the Evaluation of Enterprise Informatization [J]. Operations Research and Management Sience, 12(3): 76–79. [6] Ren Yafei & Ke Xizheng. 2010. Research on Noise of MEMS Gyroscopes Based on Wavelet Entropy [J]. Journal of Xi’an University of Technology, 26(2): 156–160. [7] Sun Zengshou & Fan Keju. 2009. Research on the Wavelet Entropy Index of Structural Damage Identification [J]. J. Xi an Univ. of Arch. & Teeh. (Natural Science Edition), 41(1): 18–23. [8] Yu Hui et al. 2010. The Radar Data Error-correction Technique Based on Hilbert-Huang Transform [J]. Chinese Space Science and Technology, 12(6): 57–63. [9] Fan Hao et  al. 2013. Optimization of Information Operation Plan Based on CV and T0PSIS Method [J]. Ship Electronic Engineering, 33(3): 26–28. [10] Chen H.Y. 2008. Effectiveness of combination forecasting method theory and its application [M]. Beijing: Science Press, 73–74.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Optimal design of locking pin for surface AUVs launcher under uncertainty S.Q. Yang Kunming Shipborne Equipment Research and Test Center, Kunming, China

ABSTRACT: Locking pin is one of the most important units in surface AUVs launchers; its reliability and life impact directly on the reliability of the AUVs launch. In optimisation design of locking pin, a lot of uncertainties affect its reliability. In this Paper, Reliability-Based Design Optimisation (RBDO) is proposed to handle the locking pin design under two kinds of uncertainties and the First-Order Reliability Method (FORM) is used to calculate reliability of its performance functions. Finally, an engineering example is used to illustrate the proposed model for the locking pin of the lock mechanism in surface AUVs launcher. Keywords: 1

locking pin; surface AUVs launcher; reliability-based design optimization; ROFM

INTRODUCTION

The Surface AUVs (Autonomous Underwater Vehicles) launcher is a device that can launch AUVs for surface ships. The Locking pin is one of the most important units in the surface AUVs launcherand its reliability and life impact directly on the reliability of the AUVs launch. The design of the locking pin is one of the important aspects of the surface AUVs launcher. But the complex working conditions make its design and modeling very difficult. Every parameter in the locking pin has significant influences on the locking pin, stability and tire service of the lock mechanism. At the same time, the traditional design method usually causes more redundancy than that being required to ensure the reliability. In the design and manufacturing process of the locking pin there are many uncertainties which may ultimately impact the reliability and safety of the product. Uncertainty is ubiquitous in any stage of the locking pin development, and they are considerable and cannot be controlled. Uncertainty impacts the locking pin performance significantly and the locking pin may suffer great risk and bring about a catastrophic consequence without carefully quantifying such uncertainties. Therefore, it is important to quantify and analyze the uncertainty during the design procedure of the locking pin. Uncertainties may come from many aspects, such as environment, manufacturing, incomplete knowledge and so on. They can be classified in two general types: Aleatory (stochastic or random) uncertainty and epistemic (subjective) uncertainty

[1–2]. Aleatory uncertainty is related to inherent variability and is efficiently modeled using the probability theory. Epistemic uncertainty describes subjectivity, ignorance or lack of information and it can be reduced with an increased state of knowledge or collection of more data. In the deterministic optimisation design, uncertainties are accounted for through the safety factor to ensure the quality of the end product. However, there are two disadvantages. First, since the safety factor is empirical, different engineers maybe using different safety factors. Second, the safety factor cannot provide a quantitative measure of safety margins in design and are not quantitatively linked to the influence of different design variables and their uncertainties on the product performance. In order to overcome the disadvantages of deterministic optimisation design, a new direction for design under uncertainties, Reliability-Based Design Optimisation (RBDO), is proposed. How to deal with uncertainty in engineering is one of the big challenges. So far, RBDO has been studied in many literatures and applied in engineering design [3–10]. Since there are many failure modes for a product, system reliability should be calculated. In other words, there are many reliability constraints in design optimizsation. The same design variables maybe appear in the different reliability constraint. Therefore, the relationship among the reliability constraints is not easy to determine. In addition, the calculations of the failure modes are known or can be identified, because of the large number of potential failure modes for most practical structures; there ise difficulty in obtaining the sensitivity of

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the performance function, and the mutual correlations among failure modes. The search for efficient computational procedures for estimating system reliability has resulted in several approaches such as bounding techniques, the Probabilistic Network Evaluation Technique (PNET), and direct or adaptive Monte Carlo Simulations (MCS) [11]. RBDO is a nonlinear optimisation problem with probabilistic inequality constraints. So far, many RBDO methods have been developed to deal with structural component’s design for improving computational accuracy and efficiency, such First Order Reliability Method (FORM), Sequential Optimisation and Reliability Assessment (SORA) [12–13], among others. In this paper, uncertainties in the locking pin design are analysed, and then we try to use the RBDO method to design the locking pin of the lock mechanism in the Surface AUVs launcher. At the same time, FORM is executed to calculate the locking pin reliability with multiple failure modes. The rest of this paper is organized as follows. Section 2 provides a brief introduction to RBDO. The mechanical model of the locking pin for surface AUVs launcher is presented in Section 3. Section 4 presents an engineering example to illustrate the proposed optimisation model. Finally, some conclusions are given in Section 5. 2 GENERAL KNOWLEDGE ABOUT RBDO RBDO is a typical probabilistic engineering design method and has been increasingly used in engineering applications. In other words, RBDO can be considered as a specific optimisation design where the minimum cost-type objective is sought while the reliability requirement is maintained. In structural optimisation, reliability is defined as the probability that the performance function (also called limit state function) is greater than or equal to 0, i.e. g(d,X,P) ≥ 0. RBDO ensures that the reliability of a product satisfies the required reliability. Calculation of reliability is given by Pr{gi (

)

} gi (

∫

fX,P ( p)dxdp

(1)

temperature is a random parameter for a gear design problem. The typical RBDO model can be formulated as follows: min d,μ X

s.t.

f (d,μ X ) Pr{{ i (d,X,P X P ) } [Ri ] , i = 1, 2, …, ng l d k d k d ku , k 1, 2, , m (2) μsl ≤ μs ≤ μsu , s 1, 2, , n

where dlk and duk are lower and upper bounds of deterministic design variable dk, respectively, and μlk and μuk are lower and upper bounds of the mean of Xs, respectively. It is difficult or even impossible to obtain the analytical solution to Eq. (1). In this paper, we use the First Order Reliability Method (FORM) to calculate the reliability. The computation procedure is given as follows. The central idea of FORM is to linearise the constraint function g(d,X,P) at its limit state (integration boundary) g(d,X,P) = 0 at a point that has the highest probability density. Then the probability can be easily calculated. In this paper, we assume all the random variables in (X,P) are independent. With this assumption, the three steps involved in FORM are as follows. Step 1: Transform the original random variables from X-space to U-space (standard normal variables) by Rosenblatt transformation. Step 2: Search the MPP (Most Probable Point) in U-space and calculate the reliability index β. The MPP search algorithm uses a recursive formula and is based on the linearization of the performance function. To reduce the accuracy loss, choose the expansion point which has the highest contribution to probability integration. At the MPP, the performance function has maximum probability density on g(U) = 0, which is the shortest distance point in u-space form the origin to the limit-state surface, and the shortest distance is β. Step 3: Calculate reliability R = Φ(β).

)≥0

3 where fX,P(X,P) is the joint probability density function of (X,P); d is the vector of deterministic design variables, for example, the number of teeth of a gear; X is the vector of the random design variables; whose mean values μX are to be determined. For example, the width and diameter of a gear could be random design variables. P is the vector of random parameters. Random parameters P are out of designer’s control and sometime called noise factors. For example, the random

THE MECHANICAL MODEL

The lock mechanism in surface AUVs launcher mainly consists of a lift tumbler (including a half cone hole), a right tumbler (including a half cone hole), bearing shaftand locking pin. The structure of the locking pin in the lock mechanism is given in Figure 1, including a half-moon pin cap and a cone pin rod. In Figure 1, γ is the half taper angle of the cone pin rod, d is the minor diameter of the cone pin rod, h is the height of the cone pin rod.

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Pr{ 2 ( , d , h))

[ bs ]

}

bs

[R2 ]

(6)

where [σbs] is the allowable compressive stress of the locking pin, P is the total pressure of the compressive plane, Abs is the effective area of the compressive plane (Abs = (d + h ⋅ tanγ)⋅h/2), [R2] is the required reliability of the locking pin compressive strength. c. Reliability constraint of the locking condition of self-lock Figure 1. The structure of the locking pin in the lock mechanism.

In the surface AUVs launcher, the cone pin rod’s reliability and life impact directly on the reliability and life of the locking pin. At the same time, its volume determines the volume of the whole lock mechanism. Thus, the locking pin should be less than a certain size. In order to minimise the volume of the locking pin and satisfaction of reliability, and to better utilise the information available, the reliability-based design optimisation of the locking pin should be conducted. Since the volume of the locking pin is in relation to the half taper angle of the cone pin rod (γ, continuous variable), the minor diameter of the cone pin rod (d, continuous variable), and the height of the cone pin rod (h, continuous variable), respectively, so the design variables will consist of d = [γ, d, h]. The volume of pin rod of locking pin is considered as the objective function to be minimised, and it is given min f

γ ,d , h

π

(( / )2

+ (tan γ

(d / ) (tan (t n γ

/2 )

2

(3)

/2 ) ) / 3

In this design problem, the inequality constraints are as follows: 1. Interval constraint of the design variables d = [ d h] ≤ du

dl

l

(4)

u

where d and d are lower and upper bounds of design variables d, respectively. 2 Reliability constraints a. Reliability constraint of the locking pin shear strength Pr{ 1( , d , h))

[ ]

}

[R1 ]

(5) where [τ] is allowable shear stress of the locking pin, Q is internal forces of the shear plane, A is area of the shear plane (A = (d + h ⋅ tanγ)⋅h), [R1] is required reliability of the locking pin shear strength. b. Reliability constraint of the locking pin compressive strength

{ 3 ( , d , h))

ta tan

}

[R3 ]

(7)

where μ is the steel friction coefficient, [R3] is the required reliability of the locking pin self-lock. The mechanical model of the design of the locking pin surface AUVs launcher under uncertainty is formulated in Eq. (8). min f

γ ,d , h

st. c1

π

(( / )2

(d / ) (tan (t n γ

+ (tan γ /2 ) ) / 3 d h g1(γ , d , h )} − [R1 ] ≥ 0

c2 (γ , d , h )

g2 ( d )} − [R2 ] ≥ 0

c3 ( d ) Pr{ P { 3 (γ , d , h )} − [R3 ] ≥ 0 dl ≤ d 4

/2 )

2

(8)

du

DESIGN EXAMPLE

In this paper, we design the locking pin that is one of the most important units in the surface AUVs launcher with least frequency use, design life of 20 years and locking pin material is 40Cr, conditioning and quenching (48∼55HRC), required reliability is 0.99. In the context of model based design, uncertainty is the difference between the model predication and reality, and in designing the locking pin in this paper, parameters or variables that are available to quantify uncertainty using probability distributions are given in Table 1. A mathematical optimization model of the locking pin is formulated according to the Eq. (7). By using the proposed optimisation method, the optimal results can be pursued, as described in Table 2 and Figure 2. In this case, the desired reliability is 0.99. From Table 2, one seesthat the area computed from the reliability-based design optimisation reduces by 45.31% compared to that of the original design. The smaller area of the locking pin can reduce the weight of the locking mechanism. From Figure 2, we can find that all the reliability of the locking pin performance functions in this paper meet the design requirement so appropriate;

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Table  1. Parameters distribution.

and

their

γ—half taper angle of cone pin rod (°) d—minor diameter of cone pin rod (mm) h—height of the cone pin rod (mm) Q—internal forces of the shear plane (N) P—total pressure of the compressive plan (N) [τ]—allowable shear stress (MPa) [σbs]—allowable compressive stress (MPa) μ—the steel friction coefficient

Table 2.

corresponding γ∼N(γ, 0.2) d∼N(d, 0.5) h∼N(h, 1) Q∼N(65000, 20000) P∼N(65000, 20000) [τ]∼N(400, 50) [σbs]∼N(650, 80)

and epistemic uncertainties in designing the locking pin of Surface AUVs Launcher. Furthermore, an engineering example is presented to verify the appropriate of the proposed optimisation model for designing the locking pin. REFERENCES

μ ∼ N(0.12, 0.03)

Optimization results.

Parameters

Safety factor method

Reliability-based design optimization

γ (°) d (mm) h (mm) f (mm3)

4.3 14.5 33.2 5.54 × 103

2.9 11.2 30.5 3.03 × 103

Figure  2. functions.

Reliability of locking pin performance

but in the original design, some reliability of performance functions don’t meet the design requirement, this may cause pressure shell failure, even a catastrophic failure. The reliability of the new design satisfies the requirement and utilises the uncertainties information available in designing the locking pin. 5

[1] Oberkampf W., Helton J. & Sentz K., 2001. Mathematical Representations of Uncertainty, AIAA Non-Deterministic Approaches Forum, no. AIAA 2001–1645, Seattle, WA, April 16–19. [2] Klir G.J. & Filger T.A., 1998. Fuzzy Sets, Uncertainty, and Information, Prentice–Hall, Englewood Cliffs, NJ. [3] Pu, P.K. Das & Faulkner D., 1997. A strategy for reliability-based optimization, Engineering Structures, vol. 19, no. 3, pp. 276–282. [4] Li H. & Foschi R.O., 1998. An inverse reliability method and its application, Structural Safety, vol. 20, no. 3, pp. 257–270. [5] Yu X., Chang K. & Choi K.K., 1998. Probabilistic structural durability prediction, AIAA Journal, vol. 36, no. 4, pp. 628–637. [6] Tu J., Choi K.K. & Park Y.H., 1999. A new study on reliability-based design optimization, ASME Journal of Mechanical Design, vol. 121, pp. 557–564. [7] Du X. & Chen W., 2001. A most probable point based method for uncertainty analysis, Journal of Design and Manufacturing Automation, vol. 4, no. 1, pp. 47–66. [8] Du X., Sudjianto A. & Chen W., 2004. An integrated framework for optimization under uncertainty using inverse reliability strategy, ASME Journal of Mechanical Design, vol. 126, no. 4, pp. 562–570. [9] Wang Z.L., Huang H.Z. & Du X., 2010. Optimal design accounting for reliability, maintenance, and warranty, ASME Journal of Mechanical Design, vol. 132(1), pp. 011007.1–011007.8. [10] Yang, S.Q., Zhou Y.T. & Lu, G.L. 2012. ReliabilityBased Design for Hoop Connection of Unmanned Undersea Vehicle Under Uncertainty, Information-An International Interdisciplinary Journal, vol.15, no.12(B), pp. 5777–5782. [11] Zhao Y.-G., Alfredo H.-S. & Ang H., 2003. System reliability assessment by method of moments, Engineering Structures, vol. 10, no. 3, pp. 1341–1342. [12] Du X. & Chen W., 2004. Sequential optimization and reliability assessment for probabilistic design, ASME J. Mech. Design, vol. 126, no. 2, pp. 225–233. [13] Wang, Z.L. Huang H.Z. & Liu Y., 2010. A unified framework for integrated optimization under uncertainty, ASME J. Mech. Design, vol. 132, no. 5, pp. 051008.1–051008.8.

CONCLUSIONS

In this paper, we used reliability-based design optimization method to deal with aleatory uncertainties

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A low temperature drift and heavy load bandgap reference voltage with adjustable output Xin Xu & Jun Jiang Sichuan Institute of Solid State Circuits, Chongqing, China

ABSTRACT: The development direction of bandgap voltage in low temperature drift, heavy load and can be adjusted. This paper presents a novel single-stage reference source; this source has adjustable output voltage, and reduces the effect of the offset voltageand overcome the non-adjustable of former references. This reference source can carry heavy load, has the same compensation coefficient with regular structure and has good Power Supply Rejection Ratio (PSRR). Keywords: 1

low temperature drift; bandgap reference voltage; adjustable output

INTRODUCTION

Low temperature coefficient and heavy load with adjustable output reference is important whether in ADC, DAC, DC-DC and any other integrated circuits. The bandgap reference source has performs well in all kinds of reference voltage source circuits at present and it can be low temperature coefficient. In order to get a low temperature drift and heavy load bandgap reference voltage with adjustable output, it is necessary to improve the structure of the typical bandgap voltage reference source or put forward a new bandgap structure. This paper proceeds according to the stability of temperature drift and the adjustable of output; it proposes a new structure based on the typical bandgap voltage source. 2

are the input of the amplifier, and drive the upper of R1 and R2, which can make point X and point Y stable in approximate equal voltage. The reference voltage can be obtained at the output of the amplifier. According to the analysis of Figure  2, VBE1 − VBE2 = VTlnn can be obtained, so the current flow through the right branch is VTlnn/R3; the output voltage is V

VBE 2 + = VBE 2

VT ln n (R3 R2 ) R3 ⎛ R ⎞ VT ln n ⎜1 + 2 ⎟ R3 ⎠ ⎝

(1)

BASIC PRINCIPLE

Consider the circuit in Figure 1, the assumption here is the base current can be neglected, the transistor Q2 is composed of n parallel transistor units, and Q1 is one transistor cell. Assuming VO1 and VO2 are equal, then VBE1 = RI + VBE2, RI = VBE1 − VBE2  =  VTlnn. So VO2  =  VBE2 + VTlnn, this means: if lnn  =  17.2, VO2 can be used as a temperature independent reference. Figure  1 needs to make two changes to be a practical circuit. First of all, VO1  =  VO2 must be ensured. Secondly, lnn  =  17.2 which makes the value of n too large, so the item of R = VTlnn must be increased in appropriate proportion. As shown in Figure  2, the circuit is an actual circuit which can realise these two functions. Here, VX and VY

Figure 1.

The principle of voltage generation.

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Figure 2.

3

Figure 3.

The bandgap reference structure.

Figure 4.

The principle diagram of the circuit.

The actual circuit in Figure 1.

IMPROVED BANDGAP VOLTAGE REFERENCE

The traditional bandgap reference has poor load ability and the output can’t be adjustable. This paper presents a bandgap reference with improved structure which can realise high load and adjustable output. Figure  3 is the bandgap reference structure block diagram presented in this paper. The circuit contains an internal start-up branch, constant current source, the internal bandgap reference, error amplifier, power transistors and over-current protection and so on. The circuit has maximum load capacity of 60 mA, output 10 V, and 7.5 V, 5 V and 2.5 V voltages through the external program. The main function of this circuit is to transfer the input voltage to precision reference voltage. The circuit consists of a bandgap reference with high temperature stability. Through programming the external pin to set up the ratio of resistors, this circuit can accurately output 10 V, 7.5 V, 5 V or 2.5 V reference voltage with 0.1% accuracy. Different output voltage value can be obtained by setting different partial pressure ratios. When power supplies from pin 8, the branch of J1, Q8, Q9, Q5, Q10 and R13 will be powered on, J1 is a pinch off resistance, which is equivalent to a N-JEFT, when J1 is powered on, there is current flow through Q5 and Q14, Q14 and Q15 are current mirror, so Q15 will be conducted, then the circuit will work. When the output is stable, the emitter voltage of Q5 is lower than Q4, so Q5 will be turned off. 3.1

The bandgap reference

The function of bandgap reference in this circuit is to provide a stable reference voltage. The simplified schematic diagram is shown in Figure 5.

This circuit is a typical bandgap structure; the area ratio of transistor Q1 and Q2 is 8:1, the value of ΔVBE is in proportional to the temperature, the VBE of transistor Q2 is a negative temperature coefficient, by setting the ratio of R2∼R4 and R1, the bandgap reference voltage VBG can close to zero temperature coefficient. If the temperature compensation is good, the value of VBG is about 1.223 V which is determined by the following formula: VREF

VBE +

2(R2

R3 || R 4 ) ΔVBE R1

(2)

The collector of Q1 and Q2 are respectively connected to the non-invert input and the invert input of the error amplifier, because of the virtual ground function of the error amplifier, the collector voltage of Q1 and Q2 are approximately equal. When R7 = R8, the bias current of Q1 and Q2 are equal to ΔVBE/R1.

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4

SIMULATION AND LAYOUT

In this paper, the simulation is based on 40 V, 6 inch technology. The temperature drift of adjustable voltages are as follows—the Temperature coefficient is very good at each output. The bandgap can load about 60  mA current. Figure 9 presents the layout of the circuit the size

Figure 5. The schematic diagram of the internal bandgap reference.

3.2

Error amplifier

The input stage of the differential amplifier is composed of NPN transistors Q3 and Q4; the base voltage of Q3 and Q4 is decided by the current of Q1 and Q2 and the resistances R5 and R6. When the base voltage of Q3 and Q4 is determined, the work current can be determined by R12. The lateral PNP transistor Q14, Q15 and Q16 are current mirror, Q14 and Q15 are the active loads of Q3 and Q4, Q16 provide drive current for the output power transistors. The substrate PNP transistors Q12 and Q13 provide current amplify as buffer. The pinch-off resistance J2 works as a N-JFET in pinch-off region which provide drive current for the output transistors, the output impedance of J2 has great impact on the current regulation. The open-loop gain of the error amplifier is determined by the following formula: g β β rOUT AV ≈ m 1 + r10 / r11

Figure 6.

8 V output of the bandgap.

Figure 7.

1.18 V output of the bandgap.

Figure 8.

Load characteristics of the bandgap.

(3)

The capacitance C1 and resistance R14 are used for the frequency compensation of the error amplifier; large capacitance can improve the stability and reduce the noise of the circuit, but will increase the start-up time. 3.3

Over-current protection

The over-current protection circuit is composed of a transistor Q22 and a resistor R18, If the voltage drop on R18 is larger than the threshold voltage of Q22, Q22 will turn on to take away the drive current of power transistor.

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5

CONCLUSION

This paper proposes a new bandgap structure which can achieve high current load capacity according to connect power transistor at the output of the error amplifier and at the same time, by setting different feedback resistance ratio, it can get different outputs. REFERENCES

Figure 9.

The layout of the bandgap.

of which is only 1.9*1.3 mm2; according to the test, the chip can realize the basic functions simulated before.

[1] Ma Zhuo, Tan Xiaoqing, Xie Lunguo, et  al. A Curvature Calibreted Bandgap Reference with Base-emitter Current Cmopensationg in a 0.13  um CMOS Process [J]. Journal of Semiconductors, 2010, 31(11). [2] Xin Ming, Ying-qian Ma, Ze-kun Zhou, et  al. A High-Presision Compensated CMOS Bandgap Voltage Reference Without Resistors [J]. IEEE J Solid-State Circuits, 2010, 57(10):767–771. [3] Ryan Foreman, Andrew Solitro: A Cmos Precision Voltage: Reference IC [D]: Worcester Polytechnic Instutute Bachelor Dissertation; 1999.05. [4] G. Dilimot, G. Brezeanu, F. Mitu, et al. Programmable Low Voltage Bandgap Reference CMOS Compatible [C]. [5] Behzad Razavi. Design of Analog CMOS Integrated Circuit [M]. Xi’an: Xian Jiaotong University Press, 2003: 21, 50–57, 310–316.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis of energy coupling between the computer chassis and electromagnetic pulse J. Liu, Y.F. Wang, Z.X. Chen & C.D. Yu Beijing Institute of Special Electromechanical Technology, Beijing, China

ABSTRACT: In order to study the coupling distribution of interior space for chassis and electromagnetic pulse energy coupling with key devices, the method of simulation is used to research the electromagnetic coupling and the results is analysed. The threshold of energy coupling and damage effect of key devices obtained in the situation of electromagnetic pulse, which provide a theoretical basis for the design of the internal structure of the chassis layout and key devices. Keywords: 1

electromagnetic pulse; energy coupling; damage

INTRODUCTION

Electromagnetic pulse has proven to be a serious threat for the computer. The time-domain of the electromagnetic pulse characteristics has a steep leading edge and narrower pulse width; frequency-domain features cover a wide frequency band. Strong electromagnetic pulse is generated by the electromagnetic pulse weapons aperture to electronic penetration inside the device through antennas, cables, and so on. Electromagnetic pulse is transformed into energy, in the form of spatial variation of high current or high voltage transmission to sensitive parts inside the device. Because the role of high density in a very small portion of the fragile, the high-density energy caused damage to electronic components and integrated circuits. Because electromagnetic pulse device to an electronic system can be described as all-pervasive, studying the effect of strong electromagnetic pulse damage with apertures chassis designing, not only provides the basis for information warfare combat and maintenance of electronic equipment, but also for the theoretical basis for electromagnetic protection and electromagnetic compatibility. 2

is 10000 V/m. The coupled energy to the computer through the aperture portion of the slit inside the center of the chassis and the maximum power density value of power, drive and hard disk can be obtained by simulation.

Figure 1.

Schematic diagram of the standard chassis.

Figure 2.

Chassis-section of YOZ.

SIMULATION AND ANALYSIS

This article focused on the damage effects research of electromagnetic pulse on the computer. A schematic diagram of the standard chassis is shown in Figure 1. Because the Gauss pulse belongs to UWB, it is used for simulation in this section. The maximum rise time of UWB is 0.5ns and the field strength

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Figure  4. Energy coupled into the power by hole of number 1.

Figure  3. Power density distribution of chassis in different times.

Figure  5. Energy coupled into the power by hole of number 2.

It can be seen from the chassis power density distribution shown in Figure 3, that with the increasing of time, the energy coupled into the chassis is dispersed. The main energy density is formed around the motherboard, power supply, optical drive and hard disk. 2.1 Energy coupling analysis of the power Pulse energy is mainly coupled into the power through holes 1, 2 and 3, so the total energy coupled into the power can be obtained. The energy coupled to power through hole 1, 2, 3 and the total energy coupled to power are shown from Figure 4 to Figure 7. By analysing the energy coupled into power, the following conclusions can be obtained: 1. The energy coupled into the hole is closely related to the seam area of supply; the larger the area, the greater the energy coupled into power; 2. From Figures 4-7, the energy coupling apertures can be obtained and the time from the beginning of coupling to reach the maximum value is about 2ns;

Figure  6. Energy coupled into the power by hole of number 3.

3. Energy coupling apertures from Figures 4-7 can be obtained; the energy coupled into power through hole 1 is about 4.9  ×  10−8 J, through hole 2 is about 1.8 × 10−8 J, through hole 3 is about 2.0 × 10−7 J and the total coupled energy

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Figure 9. The total power density in the center of the CD-Rom hole. Figure 7.

The total energy coupled into the power.

Figure 10.

Figure  8. The strength of the magnetic field in the center of CD-Rom hole.

is 2.7  ×  10−7 J, much larger than the energy threshold of integrated circuits, logical card and other computer electronic components. In this case, the UWB pulse will cause serious damage to the power electronic components. 2.2

Energy coupling analysis of the CD-Rom

This section describes the discipline that such electromagnetic pulse energy has coupled into the CD-Rom of the computer. The magnetic field that has big value in the center of chassis formed strong oscillation; the oscillation amplitude of the reflected is smaller than the incident. It can be seen from Figure 8; the strength of magnetic field in the center of CD-Rom hole also formed strong oscillations, but the amplitude of the oscillation is almost the same as the incident. The total power density in the center of the CD-Rom hole and coupling energy diagram are shown in Figures 9 and 10.

The energy coupled into the CD-Rom.

The energy coupled into the CD-Rom can be obtained by simulation: 1. The energy coupled into the optical drive is greater than the energy radiated outward; the total power density is not gradually smaller in the center of the hole, but the increase—cycle decreases. 2. By the variation of curve in Figure 10 it shows that the energy coupled into the CD-Rom reaching 3.0 × 10−9 J, which is much larger than the threshold value 4.0 × 10−10 J. Therefore, the electromagnetic pulse indicates a great disturbance and damage effect on integrated circuits and electronic components of the CD-Rom. 2.3

Energy coupling analysis of the chassis

The energy coupled into chassis by different holes and the total energy coupled into the chassis is shown from Figure 11 to Figure 15. By analysing the simulation results, the following conclusions can be obtained: 1. Due to polarisation properties, the energy coupled into the chassis by holes 9,10 and 11 are relatively small, which had weak interference on the logic card and the integrated circuit of computer.

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Figure 11. The energy coupled into the chassis by hole of number 9.

Figure 14. The energy coupled into the chassis by cooling holes of board.

Figure 12. The energy coupled into the chassis by hole of number 10 and 11.

Figure 15. The energy coupled into the chassis by holes.

old of the normal work of the computer, which would cause serious damage to the logic cards, the integrated circuits and the memory core. 3

Figure 13. The energy coupled into chassis by hole of number 12.

2. The energy of electromagnetic pulse coupled into the chassis is mainly by the cooling hole of board and hole 9. The value of energy is about 6.0 × 10−7 J, much larger than the energy thresh-

CONCLUSIONS

Through the research of this paper, the value of energy coupled into the chassis and key devices under the condition of electromagnetic pulse were obtained. The value of coupled energy in power is about 2.7 × 10−7 J, in CD-Rom is 3.0 × 10−9 J and in the chassis is about 6.0 × 10−7 J. Compared to the normal working value, the value of energy coupled into the chassis is much larger, which would cause serious damage to the key devices.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis of electromagnetic shielding effectiveness of the chassis with holes under different polarization directions Q.B. Deng, C.D. Yu & Z.P. Lian Beijing Institute of Special Electromechanical Technology, Beijing, China

ABSTRACT: In order to study the shielding effectiveness of the chassis with holes under different polarization directions of electromagnetic pulse, the discipline of shielding effectiveness with opening size of the chassis under condition of polarization directions were obtained; the research can provide a theoretical basis for the design of the chassis structural. Keywords: 1

electromagnetic pulse; polarization direction; shielding effectiveness

INTRODUCTION

The electromagnetic shielding has proven to be a useful way to protect electronic equipment from attacking of the electromagnetic pulse. Electromagnetic shielding effectiveness is usually expressed as the shielding effect method. The bigger the electromagnetic shielding value, the better the shielding effectiveness. Electromagnetic shielding is one of the most effective protection methods to prevent electrical equipments and systems from the attacking of the electromagnetic pulse. 2

SIMULATION AND ANALYSIS

In this section, after a statement of the basic problem, various situations involving possibility knowledge are investigated: first, an entire chassis model is proposed; then the relationship between shielding effectiveness and polarization direction is studied; lastly, simulation results are analysed. 2.1

selected as the excitation source; the polarization direction along the positive Z-axis is parallel to the long side of the chassis. The model of the chassis is shown in Figure 1. For the current status of the development of electromagnetic pulse weapons, such as the frequency generated by electromagnetic pulse bombs is mainly less than 2 GHZ. Take the electromagnetic pulse spectrum as the carrier band (step 1 KHZ), radio frequency section (step 1  MHZ), microwave section (step 0.02 GHZ), in which the frequency of microwave segment is selected between 300  MHZ∼2  GHZ. The frequency of the microwave segment is chosen for simulation, the curve of shielding effectiveness in center of the chassis is shown in Figure 2. It can be seen from the figure, that the electromagnetic shielding is oscillated with the frequency of the microwave section.

Model of the chassis

The following situations are defined for simulation. The size of the chassis is: 485  mm  ×  190  mm  ×  430  mm. Apertures are located in the rear center of the chassis. Ideal conductor material is selected to make the chassis, so transmission of electromagnetic waves impact on shielding effectiveness can be ignored. Only the energy coupled from the aperture should be considered impacting on the shielding effectiveness. Air is selected as material between the chassis and cabinet; radiation boundary is set in order to simulate an infinite occupied space. Plane wave is

Figure 1.

Chassis model and boundary conditions.

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Figure 4. The relationship between shielding effectiveness and side length under frequency of 1.22 G. Figure 2. The curve of shielding effectiveness in center of chassis.

Figure 3. The relationship between shielding effectiveness and radius under frequency of 1.22 G.

2.2

Figure 5.

Angle of incident schematic diagram.

Apertures sizes impact on the shielding effectiveness of chassis

Firstly, the radius of holes impacting on the chassis shielding effectiveness is studied. The frequency of 1.22  GHZ is chosen for example to research the relationship between shielding effectiveness of chassis and the radius of holes. The minimum radius value of holes is 1  mm, the maximum radius value of holes is 40 mm, 0.5 mm is chosen as the step length. Simulation results are shown in Figure 3. Then, the length of square holes impacting on the chassis shielding effectiveness is studied. The frequency of 1.22  GHZ is chosen for example to research the relationship between shielding effectiveness of chassis and the length of holes. The minimum length value of holes is 1 mm; the maximum length value of holes is 10 m, 1 mm is chosen as the step length. Simulation results are shown in Figure 4. By analysing the relationship between shielding effectiveness of the chassis and the size of holes

Figure 6. Schematic of different polarization directions.

under a certain frequency, the following conclusions can be obtained: 1. It can be seen from the above curves, under the resonant frequency, the radius of holes from 1 mm to 40 mm, the maximum shielding effectiveness of the chassis is less than 10 dB; the length of holes from 1 mm to 100 mm, the maximum shielding effectiveness of chassis is less than 15 dB.

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Figure 7. The curve of shielding effectiveness in center of chassis under angle of incidence (0, 0).

Figure 10. The curve of shielding effectiveness in center of the chassis under angle of incidence (0, 30).

Figure 11. The curve of shielding effectiveness in center of the chassis under angle of incidence (0, 60).

Figure 8. The curve of shielding effectiveness in center of the chassis under angle of incidence (30, 0).

Figure 12. The curve of shielding effectiveness in center of the chassis under angle of incidence (0, 90).

2.3 Figure 9. The curve of shielding effectiveness in center of the chassis under angle of incidence (90, 0).

2. At a fixed frequency, the shielding effectiveness is not linearly related to the pore size, shielding effectiveness is poor due to coupling apertures in the situation where the side length is small.

Polarization direction impact on the shielding effectiveness of chassis

When the angle of incidence (θ, φ) in different combinations chassis, the shield effectiveness of different shielding effectiveness is shown as the following: The following conclusions can be obtained through the above simulation analysis: 1. In the situation of θ  =  0, frequency less than 1.25 GHZ, the chassis has a relatively good

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Figure 13. The curve of shielding effectiveness in center of the chassis under condition of φ = 0.

Figure 16. The curve of shielding effectiveness in center of the chassis under condition of φ = 90.

2. At different θ, the chassis has the worst shielding effectiveness when θ = 90; the value of shielding effectiveness with the increase of the maximum peak angle becomes larger. 3. At different φ, the chassis has the worst shielding effectiveness when φ = 0; the value of shielding effectiveness with the decrease of the maximum peak angle becomes larger.

Figure 14. The curve of shielding effectiveness in center of the chassis under condition of φ = 30.

Figure 15. The curve of shielding effectiveness in center of the chassis under condition of φ = 60.

3

CONCLUSIONS

By studying the shielding effectiveness of chassis openings at different polarization directions, the shielding effectiveness in the center of the chassis under different conditions of angle of incidence is obtained. In the situation of same frequency, the shielding effectiveness is not linearly related to the pore size; shielding effectiveness is poor due to coupling apertures when the side length is small. When θ is changing, the chassis has the worst shielding effectiveness in the situation of θ  =  90; the value of shielding effectiveness with the increase of the maximum peak angle becomes larger. When θ is changing, the chassis has the worst shielding effectiveness in the situation of φ  =  0; the value of shielding effectiveness with the decrease of the maximum peak angle becomes larger.

shielding efficiency in situation of any φ of the pulse; frequency more than 1.25 GHZ, the chassis shielding effectiveness becomes relatively poor, with overall shielding effectiveness of the chassis is downward trend.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis of characteristic parameters of pulse impact on the electromagnetic coupling of the chassis C.D. Yu, Z.X. Chen, Y.F. Wang & J. Liu Beijing Institute of Special Electromechanical Technology, Beijing, China

ABSTRACT: In order to study the different characteristic parameters of the impact of the electromagnetic pulse on energy coupling space inside the chassis, the method of simulation is used to research the discipline of electromagnetic coupling in the condition of different pulse characteristics. The coupling energy variation with time is obtained under different characteristic parameters of electromagnetic pulse. Keywords: 1

electromagnetic pulse; energy coupling; characteristic parameter 2.1

INTRODUCTION

The electromagnetic pulse can be coupled through a variety of ways to make electronic components and equipment subject to interference and damage. The protection technology of the electromagnetic pulse has gradually become a hot issue in today’s research. The characteristic parameter is the concrete manifestation of the electromagnetic pulse, which can cause varying degrees of damage to electronic information equipment with different characteristic parameters. Therefore, the study is of variation characteristic parameters changed in different conditions on energy coupling of information equipment, not only by the modern information technology of electronic warfare equipment for the assessment, but also for the electromagnetic protection of electronic information equipment theory. 2

Strength of magnetic field in the center of chassis with different pulse width

Detail on pulse width is discussed in later sections. The pulse width such as 0.2 ns, 0.5 ns, 1 ns, 2 ns, 4 ns are chosen for analysing. The results of simulation are shown from Figure 2 to Figure 6.

SIMULATION AND ANALYSIS

By adjusting characteristic parameters such as pulse width, pulse rise and frequency, the simulation of different pulses can be done. This paper has taken pulse width as an example to research the characteristic parameters influencing the magnetic field of the chassis. The criteria of the chassis chosen for research are, size of the hole is 20 mm × 30 mm and polarization direction of pulse parallel to the short side of the hole. The amplitude of pulse is 10000 v/m, direction is vertical incidence. Chassis model and boundary conditions are shown in Figure 1.

Figure 1.

Chassis model and boundary conditions.

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Figure  2. Relationship between strength of the magnetic field and time in the center of chassis with pulse width is 0.2 ns.

Figure  5. Relationship between the strength of the magnetic field and time in the center of chassis with pulse width is 2 ns.

Figure  6. Relationship between the strength of the magnetic field and time in the center of chassis with pulse width is 4 ns. Figure  3. Relationship between the strength of the magnetic field and time in the center of chassis with pulse width is 0.5 ns.

2. The narrower the pulse width is, the shorter the time attain the first maximum peak of the energy coupled into the chassis. If taking the pulse width 0.2  ns for example, the first great peak time is about 1.32 ns. 2.2

Strength of the magnetic field in the center of the holes with different pulse width

In this part, the strength of the magnetic field in center of the holes with different pulse width is discussed. The results of simulation are shown from Figure 7 to Figure 10. Results of simulation are given and following conclusions are obtained: Figure  4. Relationship between the strength of the magnetic field and time in the center of chassis with pulse width is 1 ns.

Through the mentioned simulation results above, the following conclusions can be obtained: 1. The wider the pulse width is, the harder the pulse coupled into the chassis through holes;

1. The narrower the pulse width, the stronger the energy coupled into the center of chassis. The narrower the pulse width is, the more likely to be caused is the intensity enhancement of the field. If taking the pulse width 0.2 ns as example, the strength of the magnetic field coupled into the chassis is much larger than the incident; the stronger the magnetic field coupled into the center of holes, the greater the intensity of the field coupled into the chassis.

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Figure  7. Relationship between the strength of the magnetic field and time in the center of the hole with pulse width is 0.2 ns.

Figure  10. Relationship between the strength of the magnetic field and time in the center of the hole with pulse width is 2 ns.

Figure  8. Relationship between the strength of the magnetic field and time in the center of the with pulse width is 0.5 ns.

Figure  11. Schematic diagram of energy coupled into hole and chassis.

time to reach minimum is 0.62 ns, the interval is half width 0.1 ns.

Figure  9. Relationship between the strength of the magnetic field and time in the center of the hole with pulse width is 1 ns.

2. The narrower the pulse width, the energy coupled into the center of hole between the first maximum value and the first minimum value close to half of the width. If taking the pulse width 0.2 ns as example, the value of first time to reach maximum is 0.52 ns, the value of first

If taking the pulse width of 0.2 ns for example, Schematic diagram of energy coupled into hole and chassis are shown in Figure 11. Through the Fast Fourier Transform, Spectrogram of energy coupled into the center of the chassis with different pulse width is the following: Spectrogram in the center of the chassis with different pulse width is shown from Figure  12 to Figure 16. Through the mentioned simulation results above, the following conclusions can be obtained: 1. The narrower the pulse width, the richer the high-frequency component of the spectrum.

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Figure 12. Spectrogram in the center of the chassis with pulse width is 0.2 ns.

Figure  13. Spectrogram in the center of chassis with pulse width is 0.5 ns.

Figure 15. Spectrogram in the center of the chassis with pulse width is 2 ns.

Figure 16. Spectrogram in the center of the chassis with pulse width is 4 ns.

obtain the maximum value of the strength of the magnetic field. With a pulse width of 0.2 ns, the frequency at the maximum strength of magnetic field is about 7 GHZ; with a pulse width of 0.5 ns, the frequency of the maximum strength of magnetic field is about 3.5 GHZ; with a pulse width of 1 ns, the maximum value of strength of magnetic field is approximately 1.76 GHZ. 3 Figure 14. Spectrogram in the center of the chassis with pulse width is 1 ns.

The pulse width becomes twice as much as the original; the high-frequency component becomes about ½ of the original. 2. The wider the pulse width, the lower the frequency of the pulse coupled into the chassis to

CONCLUSIONS

By analysing the variation of different characteristic parameters such as pulse width of the electromagnetic pulse, the influence of energy coupled into chassis is deduced. The narrower the pulse width, the stronger the energy coupled into the center of chassis and the richer the high-frequency component in the spectrum. The wider the pulse width, the lower frequency of the coupling energy reaches the maximum value.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A new type of control method for the variable-speed wind turbines based on the PID neural network T. Li School of Electrical Engineering, Southeast University, Nanjing, China School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China

X.Y. Hou School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China

H.Y. Lin School of Electrical Engineering, Southeast University, Nanjing, China

Q.Y. Liu Hulun Buir College, Hulun Buir, Hailar, China

L. Zhao Shan Dong Water Polytechnic, Rizhao, Shandong, China

H.J. Liu School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China

ABSTRACT: An effective maximum power point tracking control method can improve the wind energy conversion efficiency; however, as the control tasks become more complex, the traditional MPPT control technology can not satisfy the complex control task of wind turbines. Addressing the shortcomings of the traditional PID control method, a new kind of PID neural network controller for the maximum power point tracking control of variable speed wind turbine is designed. Firstly, the model of the wind turbine is set up, and the structure of the PID neural network controller is given. Secondly, the wind generator torque of the wind turbine is regulated by the PID neural network controller by training the weights of the neural network. Finally, the simulation platform for the control system is built up, and results show that the method can track the maximum wind energy and improve the work efficiency of the wind turbine. Keywords: 1

NN; PID; PID neural network; MPPT; wind turbine

INTRODUCTION

With the increasing amount of wind energy utilisation, maximum power point tracking control technology has gradually become the focus of attention[1–3]. Variable-speed wind turbine is one of the most import wind turbines in which the generator speed can follow the change of the wind speed[4–5]. Since the uncertainty of the wind, causes a strong nonlinear impact on the wind turbine, it is hard to improve the dynamic characteristics of the system. In order to solve the problem the MPPT control method are being studied closely by the experts at home and abroad, and a series of effective methods[6–10] have been put forward—one of which is the PID control. However, tuning the parameters of the PID controller needs the use of

the trial method; when the running of the system is affected by wind speedor is impacted by the influence of other interferences, then the PID regulator loses its control flexibility. Neural network control technology is widely used because of its advantages in dealing with nonlinear systems/conditions and uncertainty: neural network is now gradually being applied to the field of wind power control[11–13]. The neural network control is one of ANN’s common control method. It is often difficult to establish an accurate plant model in the industry; a unique choice is provided by the neural network control to deal with the unknown dynamic of nonlinear systems, and it has provided an effective solution to solve maximum power point tracking problems. The PID neural network and the variable speed wind

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power generation control system is combined in this paper; an intelligent torque control method for variable speed wind power generation system is designed. Take the rotor speed of the wind power generator and the speed reference values of PID neural network as the input values, and use the output value of the PID neural network to regulate the torque of wind power generator, which make wind turbines runon the optimal power curve and by which the maximum wind energy capture is achieved. Finally, the verification test of designed controller is carried out on the platform of Dspace; the results show that the method can capture the maximum wind energy, and the efficiency in the utilization of the wind is improved. 2

PID NEURAL NETWORK CONTROL FOR THE VARIABLE SPEED WIND TURBINE

Figure 1.

Power coefficient and tip speed ratio.

Figure 2.

Structure of PID neural network.

The structure of DFIG-based wind turbine includes wind wheel, gear box, wind generator, AC/DC convertor, grid, etc. The model of DFIG-based wind turbine can be expressed as equation (1–4): Pwt Γ wt

1 ρπ R 2v3C p ( 2 1 = ρπ R3v 2CΓ ( 2

C p (λ β ) λ=

R ⋅ Ωl v

λCΓ ( λ , β )

)

(1)

)

(2) (3) (4)

where, Pwt is power of wind wheel, Γwt is torque of wind wheel, CΓ is torque coefficient, λ is tip speed ratio, β is blade pitch angle, R is radius of wind wheel, ρ is density of air, v is wind speed. As is shown in Figure 1, when the wind speed is below the rated value, the control goal of wind energy conversion system is to get the optimal value of tip velocity ratio λ and the maximum value of power coefficient Cp. The structure of PID neural network controller[14–16] is shown in Figure 2. PID neural network controller includes input layer, hidden layer and output layer. The input layer consists of two neurons. The hidden layer consists of three neurons. The output layer consists of one neuron, where, Input layer: two inputs of neural network x11, x21, where, x11 is the speed reference value Ωhref of wind turbines. x21 is the actual rotational speed Ωh of wind turbines. The output of the input layer is: y j1 = f 1 nnet1 j ) , j = 1, 2. The state function of neurons is u1 j ( k ) = net1 j .

Hidden layer: hidden layer includes three neurons, proportional element, integral element, differential element. The sum of input weight which in 2 hidden layer neurons is neti 2 ∑ y j1 ⋅ wiij 2 , i = 1, 2, 3, j =1 wij2 is the input weights of the i-th neurons which in the hidden layer. The output function neuron is: The state of proportional element is: u 21( k ) = net 21

(5)

The state of integral element is: u 2 2 ( k ) = u 2 2 ( k 1) + net 2 2

(6)

The state of differential element is: u 23 ( k ) = net 23 ( k ) − net 23 ( k 1)

(7)

Output layer: the output layer includes one neuron. The sum of neurons input weight is netl 3 yi 2 ⋅ wli 3 , wli 3 is the input weights of the i =1

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Figure  3. Structure of PID neural network controller for the generator torque.

l-th neurons which in the output layer, y and l = 1. The output of the output layer is yl 3 f 3 ( net 3l ), the yl 3 is the torque reference value of wind generator ΓGref. g The state function of neurons is u3l k ) = net 3l. The output functions of input layer and hidden layer are f1, f2, which are Tansig function. The output function of output layer is f3, which is purelin function. The choice of initial weights: the initial weights from input layer g y to hidden layer are: 2 + 1, w12 2 1, w212 + 0.1, w22 2 = − 0.1, w312 = 11 −1, w32 2 −1. The initial weights from hidden layer to output layer are: the output ratio of PID controller kP, integration kI, differentiation kD. The error back propagation algorithm of system: the weight value of PID neural network can be adjusted by error back propagation algorithm, the network training target:

Figure 4. Dspace platform of PID NN control system for variable-speed wind turbines.

m

Et (p ) = 1/m / ∑ [ytp

yop ]2

(8)

p=1

where, ytp is desired output of neural network, which is the speed reference value of wind turbines Ωhref. yop is actual output of neural network, which is the rotating speed of wind turbines Ωh, m is the number of samples. The weights of the backpropagation process are adjusted by the negative gradient algorithm with momentum factors. As is shown in Figure 3, the structure of PID neural network controller for the variable speed wind turbine is given, the inputs of the PID neural network are reference of wind generator speed Ωhref and wind generator speed Ωh, output of the PID neural network is the reference output of the wind generator torque ΓGref, which is taken as the input of the variable-speed wind turbine. 3

Figure 5.

Wind speed.

Figure 6.

Generator power (Kw).

SIMULATION RESULTS

As is shown in Figure 4, the Dspace platform of PID neural network control system for variable-speed wind turbines is given, the platform mainly include wind turbine module, the Dspace neural network module, computer monitoring module, etc. The wind speed output is shown in Figure 5. From the output results of the experiment, as shown in Figure 6 and Figure 7, we can see that

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REFERENCES

Figure 7.

Generator speed (rad/s).

when the wind speed changes, the output power of wind turbines can track the optimal power curve, and the generator output speed of wind turbines can track the optimal speed curve. 4

CONCLUSION

In order to overcome the drawbacks of the conventional control method, this paper introduces a PID neural network control method for WECS; application of PID neural network in WECS mainly includes three sections in this paper. Firstly, the model of the wind turbines is set up; secondly, the PID neural network control method for the wind turbines is designed; thirdly, the data from the work farm is collected and the PID neural network control system is built up, based on the Dspace platform. The results show that the method proposed can make the power output and torque output of wind turbines track the optimal curve. The maximum wind energy capture is achieved, and the method also provides a valuable reference for the control of wind power system. ACKNOWLEDGMENT This work has been supported by A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), A Project Funded by the Dr Foundation of Jiangsu University of Science and Technology under Grant NO. 635031303 and A Project Funded by Jiangsu University of Science and Technology under Grant NO. 633031304, A Project Funded by the National Natural Science Fund under Grant NO. 51307074, A Project Funded by the Postdoctoral Key Research Fund of Southeast University under Grant NO. 1116000185, A Project Funded by the Postdoctoral Science Foundation of Jiangsu Province under Grant NO. 1301005B.

[1] Sargolzaei, J. & Kianifar, A. 2009. Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque. Simulation Modelling Practice and Theory 17(7): 1290–1298. [2] Li, T. & Shen, Y.X. & Ji, Z.C. 2012. Research on A Novel MPPT Control Method for Variable-Speed Wind Power Systems. Research Journal of Applied Sciences, Engineering and Technology 4(16): 2712–2715. [3] Nguyen, H.M. & Naidu, D.S. 2010. Advanced control strategies for wind energy systems: An overview. Proc. Power Systems Conference and Exposition, Phoenix, 20–23 March 2011. [4] Li, T. & Ji, Z.C. 2012. Data Driven Control for Wind Energy Conversion System Output Power based on Neural Network Compensator. Journal of Control Science and Engineering 2012: 1–8. [5] Li, T. & Ji, Z.C. 2011. Intelligent Inverse Control to Maximum Power Point Tracking Control Strategy of Wind Energy Conversion System. Proc. 2011 Control and Decision Conference, Mianyang, 23–25 May 2011. [6] Vincenzo, G. & Antonio, P. & Pierluigi, S. 2008. Designing an adaptive fuzzy controller for maximum wind energy extraction. IEEE Transactions on Energy Conversion 23(2): 559–569. [7] Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand M. 2007. Multivariable control strategies for variable speed, variable pitch wind turbines. Renewable Energy 32(8): 1273–1287. [8] Muntean, I. & Cutululis, N.A. & Bratcu, A.I. & CeangĂ, E. 2005. Optimization of variable speed wind power systems based on a LQG approach. Control Engineering Practice 13(7): 903–912. [9] Nichita, C. & Luca, D. & Dakyo, B. & Ceanga, E. 2002. Large band simulation of the wind speed for real time wind turbine simulations. IEEE Transactions on Energy Conversion 17(4): 523–529. [10] Muntean, I. & Cutululis, N.A. & Bratcu, A.I. & CeangĂ, E. 2008. Optimal control of wind energy systems. London: Springer. [11] Kanellos, F.D. & Hatziargurios, N.D. 2002. A new control scheme for variable speed wind turbines using neural networks. Proc. Power Engineering Society Winter Meeting, New York, 27–31 January 2002. [12] Huang, & C.H. Hong, C.M. & Su, Y.F. & Lee, S.M. et al. 2013. Elman Neural Network for Dynamic Control of Wind Power Systems. Applied Mechanics and Materials 479–480(2014): 570–574. [13] Hui, L. & Shi, K.L. & McLaren, P.G. 2005. Neuralnetwork-based sensorless maximum wind energy capture with compensated power coefficient. IEEE Transactions on Industry Applications 41(6): 1548–1556. [14] Li, M. & Yang, C.W. 2006. A modified PSO learning algorithm for PID neural network. Proc. 2006 Chinese Control Conference. Beijing, 7–11 August 2006. [15] Shu, H.L. & Shu, H. 2007. Simulation study of PID neural network temperature control system in plastic injecting-moulding machine. Proc. 2007 International Conference on Machine Learning and Cybernetics, Hong Kong, 19–22 August 2007. [16] Shu, H.L. 2006. PID Neural Network and Its Control System. Beijing: Beijing National defense industry Press.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Research on the control method for the torque of wind generator based on data-driven T. Li School of Electrical Engineering, Southeast University, Nanjing, China School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China

X.Y. Hou School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China

H.Y. Lin School of Electrical Engineering, Southeast University, Nanjing, China

L. Zhao Shan Dong Water Polytechnic, Rizhao, Shandong, China

H.J. Liu School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, China

Q.Y. Liu Hulun Buir College, Hulun Buir, Hailar, China

ABSTRACT: Looking at the actual wind turbines in the wind farm, wind turbines are typically timevarying, strong-coupling and too complex to build a precise mathematical model; a method is proposed to improve the capture efficiency of wind energy. On the basis of related data for the wind energy conversion system, including power, wind power generator, wind speed, wind generator torque, rotor speed, data driven control does not need to establish a model of the controlled object, by the advantage of which the data drive controller for maximum wind energy capture of the wind turbine is designed, tip speed ratio is regulated by controlling the torque of the generator, and the wind energy utilization coefficient is improved. Keywords: 1

torque; data driven; DFIG; generator; MPPT; wind turbine

INTRODUCTION

With the increasing amount of the use of wind energy, wind turbine control technology is becoming a hotspot of attention[1–4]. In recentyears, the experts at home and abroad have conducted indepth research on the wind turbine control system, and put forward a series of effective methods. However, when the actual operation of wind turbines in the wind farm are often influenced by the uncertain factors, the conversion efficiency of wind energy will be greatly reduced. If offline or online data generated by the wind turbines in the wind farm are fully utilized, it will greatly improve the efficiency of conversion of wind energy, and bring new challenges to improve the dynamic characteristic of wind turbines[5–8].

The Data driven control theory[9–15] has gradually become a hot subject for of experts at home and abroad; its advantage is for the complex nonlinear system, whose model is unknown, and the related control law is taken from the process data of the controlled object: the method has been successfully applied to many fields, such as flight control, pattern recognition, robot control. A model of a wind turbine, based on DFIG, is built in this paper, as is the design of the data driven controller with the neural network compensation for wind turbines; also designed is the data drive controller for maximum wind energy capture of the wind turbine, the tip speed ratio is regulated by controlling the torque of the generator, and the wind energy utilization coefficient is improved.

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2

⎧y (N )T Qy (N ) ⎪ J = ε ⎨ N −1 T (k ) + (k )T ⎪+ ∑ (k ) ⎩ k =0

MPPT CONTROLLER WITH NEURAL NETWORK

2.1

(

Model of DFIG-based wind turbines

A model of DFIG-based wind turbines is shown in Figure 1, which includes the wind wheel, drivenchain, DFIG, grid, etc. Model of DFIG-based wind turbines can be expressed as: Pwt Γ wt

1 ρπ R 2v3C p ( 2 1 = ρπ R3v 2CΓ ( 2

C p (λ β )

λ=

)

(1)

)

(2)

λCΓ ( λ , β )

Y

)

⎡ P1 Tp ⎣

p) L

yL ( k

p + L ) ⎤⎦

(9)

(5)

f ( x( k ), u( ))

N u( k ) = f ⎡{Mi }1 , Q, R, u( k ⎣

)),, y k 1) ⎤ ⎦

(6)

where, Mi, = CA(t−1) B, i = 1, 2, …, N, A, B, C is the unknown parameters vector, the performance index function can be expressed as:

Structure of DFIG-based wind turbines.

(

P2 ⎤⎦ YV T VV T

)

+

(10)

where, P1 = Op ((Bp + MTp), P2 = −OpM, M = −Ap Op+, B p ⎡⎣ A p −1B L A B B ⎤⎦ . 4. Substituting Mi into the equation (11) to calculate the data-based control gain GW(k), i = 1, 2, …, N.

where, x(k) is the n-dimensional state quantity, u(k) is n-dimensional input quantity, y(k) is n-dimensional measured output, by solving the control function u(k):

Figure 1.

⎡⎣ y1 ( k

(8)

3. According to the equation (10) to get P1, P2 and the matrix Tp, let p = N + 1, the Markov parameters Mi, i = 1, 2, …, N of wind energy conversion system can be obtained from the Tp.

Data-driven control based on neural network

The DFIG wind energy conversion system can be expressed as: y( k

L u( k L ) ⎤ ⎡ u( k ) ⎢ ⎥ V = ⎢u p k + p ) L u p ( k + p L )⎥ ⎢ ⎥ L yL ( k L ) ⎥⎦ ⎢⎣ y k )

(4)

where, Pwt is power of wind wheel, Γwt is torque of wind wheel, CΓ is torque coefficient, λ is tip speed ratio, β is blade pitch angle, R is radius of wind wheel, ρ is density of air, v is wind speed. 2.2

(7)

)

1. The relevant data of wind energy conversion system is deduced by the SCADA system: 2. Output and input data of the control system can be expressed as (8) and (9):

(3)

R ⋅ Ωl v

⎫ ⎪ ⎬ (k ) ⎪ ⎭

(

GW ( k ) = − R θ ( k + )T (k (k + ) −1

× g ( k + )) θ ( k

)T

(k + )

(11)

5. According the equation (12) to get the xˆ Wc ( k ) , taking the column vector from second row to the (N − k + 1)l row as the state vector xWc(k) of controller.

Figure 2.

Structure of neural network compensator.

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⎡ 0l xˆ Wc ( k ) = ⎢⎢ ⎢⎣

Il (N − k )

⎤ ⎥ O x( k ) ⎥ p 0 l ⎥⎦

⎡ 0l = ⎢⎢ ⎢⎣

Il (N − k )

⎤ ⎥ (P u ( k − p) ⎥ 1 p 0 l ⎥⎦ + P2 y p ( k − p ))

(12)

6. According the equation (13) to calculate the control input u: U( )

W ( k ))xWcc ( k )

(13) Figure 5.

Wind speed.

Figure 6.

Output results.

7. The system output y(k) can be derived from the control quantity u(k − 1), and the next control input u(k + 1) can be calculated by using the control input of the next time u(k) and the system output y(k).

Figure 3. Structure of data-driven controller based on neural network.

Figure 4. Platform of Xilinx System Generator for NN controller of DFIG-based wind turbine.

8. As is shown in Figure 2, by taking e(k), e(k – 1), y(k − 1) as the input of neural network compensation controller, the control input of next time Δunn(k + 1) can be obtained, circulating into the step (2).

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3

REFERENCES

SIMULATION RESULT

As is shown in Figure 3, structure of data driven controller based on neural network is given, λr is the reference input, λ is actual input, the error between them is e(t), which is the input of the data driven controller. As shown in Figure 4, the platform of the Xilinx System Generator for NN controller of DFIGbased wind turbine is given; the platform includes wind turbine module, signal processing module, Xilinx virtex-5 platform module, Matlab/system generator module, etc. The wind speed output is shown in Figure 5. As is shown in Figure 6, from the output results of the experiment, from (a)–(b), we can see that tip speed ratio and wind energy utilisation coefficient are more stable with the control method proposed than that with traditional one. 4

CONCLUSION

In order to overcome the shortcomings of the data drive control method, one can use the data driven control method based on neural network compensation, to enhance the performance of the MPPT control system for the WECS, by determining the Markoff parameter of the wind energy conversion system. Secondly, the system—, the MPPT control system for the DFIG-based WECS, whose output is adjusted by controller gain. Finally, neural networks of different training rulers are used to train the compensator, and thenthe results obtained are compared. The method is relatively simple; it’s easy industrial implementation can effectively reduce the wind energy conversion system speed fluctuation, increase the security of wind turbine operationand capture the maximum wind energy. A good guide in the application of doubly fed wind energy conversion system is provided. ACKNOWLEDGMENT This work has been supported by A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), A Project Funded by the Dr Foundation of Jiangsu University of Science and Technology under Grant NO. 635031303 and A Project Funded by Jiangsu University of Science and Technology under Grant NO. 633031304, A Project Funded by the National Natural Science Fund under Grant NO. 51307074, A Project Funded by the Postdoctoral Key Research Fund of Southeast University under Grant NO. 1116000185, A Project Funded by the Postdoctoral Science Foundation of Jiangsu Province under Grant NO. 1301005B.

[1] Li, T. & Shen, Y.X. & Ji, Z.C. 2012. Research on A Novel MPPT Control Method for VariableSpeed Wind Power Systems. Research Journal of Applied Sciences, Engineering and Technology 4(16): 2712–2715. [2] Nguyen, H.M. & Naidu, D.S. 2010. Advanced control strategies for wind energy systems: An overview. Proc. Power Systems Conference and Exposition, Phoenix, 20–23 March 2011. [3] Li, T. & Ji, Z.C. 2012. Data Driven Control for Wind Energy Conversion System Output Power based on Neural Network Compensator. Journal of Control Science and Engineering 2012: 1–8. [4] Li, T. & Ji, Z.C. 2011. Intelligent Inverse Control to Maximum Power Point Tracking Control Strategy of Wind Energy Conversion System. Proc. 2011 Control and Decision Conference, Mianyang, 23–25 May 2011. [5] Andrew, K. & Zhang, Z.J. & Li, M.Y. 2010. Optimization of Wind Turbine Performance with Data-Driven Models. IEEE Transactions on Sustainable Energy 1(2): 66–76. [6] Andrew, K. & Zhang, Z.J. 2011. Adaptive Control of a Wind Turbine with Data Mining and Swarm Intelligence. IEEE Transactions on Sustainable Energy 2(1): 28–36. [7] Andrew, K. & Zheng, H.Y. 2010. Optimization of Wind Turbine Energy and Power Factor with an Evolutionary Computation Algorithm. Energy 35(3): 1324–1332. [8] Andrew, K. & Zheng, H.Y. & Zhe, S. 2010. Power Optimization of Wind Turbines with Data Mining and Evolutionary Computation. Rnewable Energy 35(3): 695–702. [9] Tian, X.M. & Chen, G.Q. & Chen, S. 2011. A DataBased Approach Formulti Variate Model Predictive Control Performance Monitoring. Neurocomputing 74(4): 588–597. [10] Van, H.J, & De, J.B. & Steinbuch, M. 2007. Direct Data-Driven Recursive Controller Unfalsication with Analytic Update. Automatica 43(12): 2034–2046. [11] Guardabassi, G.O. & Savaresi, S.M. 2000. Virtual Reference Direct Design Method: An off-line Approach to Data-based Control System Design. IEEE Transactions on Automatic Control 45(5): 954–959. [12] Hou, Z.S. & Huang, W.H. 1997. The Model-Free Learning Adaptive Control of a Class of SISO Nonlinear Systems. Proc. 1997 American Control Conference, Albuquerque, 4–6 June 1997. [13] Lee, C.M. & Narayanan, S. 2003. Emotion Recognition Using a Data-Driven Fuzzy Inference System. Proc. European Conference on Speech Communication and Technology, Geneva 31 August 2003. [14] Spall, J.C. & Cristion, J.A. 1998. Model-Free Control of Nonlinear Stochastic Systems with Discrete-Time Measurements. IEEE Transactions on Automatic Control 43(9): 1198–1210. [15] Hou, Z.S. & Yan, J.W. 2009. Model Free Adaptive Control based Freeway Ramp Metering with Feed Forward Iterative Learning Controller. Acta Automatica Sinica 35(5): 588–595.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Construction of robustly stable interval polynomial T.A. Ezangina, S.A. Gayvoronskiy & S.V. Efimov National Research Tomsk Polytechnic University, Tomsk, Russia

ABSTRACT: The paper presents an original algorithm to determine the intervals of characteristic polynomial coefficients, which ensure its robust stability. This algorithm is built in conditions that provide linking indexes of stability and oscillation with interval coefficients of the characteristic polynomial. The paper reviews application of the algorithm for determination of the interval settings in a robust PID-controller. Performance of the algorithm was tested by construction of localization regions of the roots of the interval polynomial upon the determined intervals of the controller parameters. Keywords: interval characteristic polynomial; coefficient parameters of quality; interval-parametric synthesis, robust controller 1

INTRODUCTION

A great importance to the theory of robust control presents the Kharitonov’s theorem, which allows to assess stability of polynomials with coefficients varying within the known intervals. To analyze the robust stability, testing an infinite number of polynomials is not required but, instead, we check the stability of only four Kharitonov’s polynomials composed of the extreme values of coefficients, in alternating pairs (two minimum and two maximum values) [1]: P1( s ) = p0 + p1s + p2 s 2 + p3s3 + ..., P2 ( s ) = p0 + p1s + p2 s 2 + p3s3 + ..., P3 ( s ) = p0 + p1s + p2 s 2 + p3s3 + ..., P4 ( ) = p0 + p1s + p2 s 2 + p3s3 + ...,

The inverse problem—determining intervals of coefficients of characteristic polynomial—is also important to the development of automatic control systems with interval parameters. Here, we are interested in robustly stable polynomials, i.e., their roots lie in the left half-plane under any changes in the coefficients of polynomials. Figure 1 shows mapping of variations in the coefficients of the characteristic polynomial onto the root plane for the case of a robustly stable polynomial. Determination of admissible variation range for coefficients of the characteristic polynomial can solve the problem of interval-parametric synthesis of robust controllers, whose configurable settings are included linearly in the coefficients of the characteristic polynomial. The objective of this study is to develop a method for determining the interval coefficients of the characteristic polynomial, which provide its robust stability.

2

ALGEBRAIC CONDITIONS FOR STABILITY AND OSCILLABILITY OF THE INTERVAL POLYNOMIAL

Let there be given an interval polynomial: P ( s ) = [ pn ]s n + [ pn − 1 ]s n −1 + ...[ p0 ], Figure 1.

Mapping the polyhedron of coefficients.

The reported study was partially supported by RFBR, research project No 14-08-31031.

(1)

were pi ≤ pi ≤ pi ( pi is the lower limit, pi —the upper limit). Sufficient conditions for robust stability and robust oscillability can be obtained on the basis of coefficients pi [2–3]. Let us introduce the

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coefficient parameters of stability λi [2–3] formed by the quadruples of the adjacent polynomial coefficients (1): [ λi ] =

[

][ [ i ][ i

i +2 ] i +1 ]

, i 1, n 2.

Definition 1. The interval automatic control system is stable if the following conditions are satisfied

λi

pi pi + 2 pi pi +1

(2)

Stability domains constructed upon these conditions will be located inside the exact stability region found by any necessary and sufficient condition. Restriction of this field is insignificant and occurs through discarding points, which refer to systems with a minor stability factor. Let us consider coefficient parameters δz [2–3] that characterize the tendency of a system to oscillate. Parameters δz are dimensionless and are called oscillation indexes. Same as λi, these parameters are linked in a very simple way with coefficients of the transfer function of a linear system. Parameter δz is determined by coefficients of the characteristic polynomial (1), as follows [δ z ] =

[ pz2 ] , z = 1,n , − 1. [ z 1 ][ z +1 ]

pz 1 pz +1

δd , z

1, n − 1,

(3)

where δd is an admissible oscillation index determined from Figure 2. Robust stability and robust oscillability of the interval characteristic polynomial can be estimated upon the conditions (2) and (3). 3

λ * , i 1,nn 2, λ * ≈ 0, 465

pz2

δz

THE ALGORITHM FOR DETERMINING INTERVALS OF POLYNOMIAL COEFFICIENTS

Suppose that at least two leading coefficients of the characteristic polynomial (1) are known. By applying conditions (2) and (3), we construct a system of inequalities to determine the unknown intervals for the characteristic polynomial coefficients ⎧ pi pi + 2 λ * , i n 2, n 3, ...1 λ * ≈ 0, 465; ⎪ λi pi pi +1 ⎪ ⎨ pz2 ⎪δ = ⎪ z p p ≥ δ d , z n 1, n , ...1. z −1 z +1 ⎩ (4) Through algebraic transformations conducted on the basis of the system (4), we obtain expressions for the limits of coefficients pi pi =

Sufficient condition for a given degree of oscillability is formulated on the basis of oscillation indexes δz [2–3]. To locate the roots of the interval characteristic polynomial (1) in a given angular sector ±ϕ, we must select controller settings, which would enable the following conditions

Pz = pg =

λ * pi pi + 2 ,i pi + 3 pz +1 2

δ d pz + 2

,z

g, z

λ *δ d pi + pi +1 pi + 2

g − 1, g 2

,i

0 g=n k

g − 1, ...0, g g, g 1

n−k

0 g=n k

(5)

(6)

(7)

Relying on the expressions (5)–(7), an algorithm for determining the interval coefficients that ensure robust stability of the interval polynomial is generated. The block diagram of this algorithm is demonstrated in Figure 3. The algorithm includes the following steps:

Figure 2. Dependence between the sector of root location and δd value.

1. Setting the constant coefficients of the polynomial and the admissible oscillation index δd. 2. Verification of the fulfilled conditions (2) and (3) for the known coefficients of the interval characteristic polynomial. 3. Solutions for (5), (6), finding the maximum values of pi and selecting the minimum pi value from this set of values.

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P ( s ) = [ p6 ]s 6 + [ p5 ]s5 + [ p4 ]s 4 + [ p3 ]s3 + [ p2 k2 )]s 2 + [ p1( k1 )]s1 + [ p0 (

0 )],

where [p6] = [a5], [p5] = [a4], [p4] = [a3], [p3] = [a2], [p2] = [a1] + b[k2], [p1] = [a0] + b[k1], [p0] = b[k0]. To solve this task, we elaborate the system in the form (4): ⎧ p0 k0 ) p3 ≤ 0, 465; ⎪λ1 = p1 k1 ) p2 k2 ) ⎪ ⎪ p1 k1 ) p4 ⎪ ⎪λ2 = p k ) p ≤ 0, 465; 2 2 3 ⎪ ⎪ ⎪λ = p2 k2 ) p5 ≤ 0, 465; ⎪ 3 p3 p4 ⎪ ⎪ ⎪λ = p3 p6 ≤ 0, 465; ⎪ 4 p4 p5 ⎪ ⎪ p12 ( k1 ) ⎪ δ = ≥ 1.45; ⎨ 1 p0 ( k0 ) 2 ((k k2 ) ⎪ ⎪ p22 ( 2 ) ⎪ ≥ 1.45; ⎪δ 2 = p1( 1 ) p3 ⎪ ⎪ p32 ⎪ δ = ⎪ 3 p ( ) p ≥ 1.45; 2 2 4 ⎪ ⎪ 2 ⎪δ = p4 ≥ 1.45; 4 ⎪ p3 p5 ⎪ ⎪ 2 ⎪δ = p5 ≥ 1.45; 5 ⎪ p4 p6 ⎩

Figure 3. Block diagram of the algorithm for determining the interval coefficients.

4. Solution for (7) and determination of the minimum limit for pi. limit pi. 4

APPLICATION OF THE ALGORITHM IN THE INTERVAL-PARAMETRIC SYNTHESIS OF THE ROBUST CONTROLLER

Assume that the automatic control system has a certain controlled object Wco =

According to the developed algorithm, we check that conditions (2), (3) for λ4, δ4, δ5 are fulfilled:

b = 0, [ a5 ]s5 + [ a4 ]s4 + [ a3 ]s3 + [ a2 ]s2 + [ a1 ]s1 + [ a0 ]

were[a5] = [0.5; 1], [a4] = [11.5; 12], [a3] = [46.5; 47], [a2] = [107.5; 108], [a1] = [121; 122], [a0] = [25; 90], b = 3.2 and PID-controller Wc ( ) ( k2 s 2 k1s1 k0 )/ss . We need to determine the setting intervals of PID-controller: k0, k1, k2, which provide that the automatic control system is robustly stable and conditions for oscillability and accuracy at given δd = 1.45, Dw = 1, where Dw—is quality of the system are satisfied. We write the interval characteristic polynomial of this system, as follows

⎧ ⎪λ4 = ⎪ ⎪ ⎪ ⎨δ 4 = ⎪ ⎪ ⎪ ⎪δ 5 = ⎩

p3 p6 = 0.101 ≤ 0, 465; p4 p5 p42 p3 p5 p52 p4 p6

≥ 1.66; ≥ 5.62;

Since these conditions are satisfied, we proceed to determining the limits of unknown coefficients p2(k2), p1(k1), p0(k0), aided by the conditions (5)–(7).

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For this purpose, we start with defining the upper limit of the coefficient p2(k2) 0.465 p3 p4 ⎧ = 193.7; ⎪ p2 k2 ) = p5 ⎪ ⎨ p32 ⎪ = 169.57 ⎪ p2 k2 ) = δ d p4 ⎩ From the set of obtained values, we select the minimum value p2(k2) = 169.57. Then, we calculate the lower limit of p2(k2), according to the algorithm p2 k2 ) =

0.465δ d p3 p3 p4

= 166.5 Figure 4.

Similarly to finding the upper limit of the coefficient p2(k2), we calculate the maximum limit p1(k1) 0.465 p2 k2 ) p3 ⎧ = 177.41; ⎪ p1 k1 ) = p4 ⎪ ⎨ 2 ⎪ p k ) = p2 k2 ) = 177.41 1 1 ⎪ δ d p3 ⎩

p1 k1 ) =

0.465δ d p2 k2 ) p2 k2 ) p3

= 176.3

Further, we determine the maximum limit p0(k0) 0.465 p1k1 p2 ⎧ = 126.441; ⎪ p0 k0 ) = p3 ⎪ ⎨ p12 k1 ⎪ p k ) = = 126.441 ⎪ 0 0 δ d p3 ⎩

Da0 b

CONCLUSION

This research paper presents an algorithm for determining the coefficient intervals of the characteristic polynomial that ensure its robust stability, developed on the basis of robust expansion of the coefficient method. This algorithm can be applied to determine the interval settings of robust controllers. The operating performance of this algorithm is verified on the example of intervalparametric synthesis of robust PID-controller. REFERENCES

The lower limit of p0(k0) is found from the value of quality factor [3] p0 k0 ) = k0b = 25 k0 =

Thus, the system under discussion will remain robustly stable at any PID parameter values within the found ranges. This result is checked by constructing the localization regions of the roots and the interval polynomial (Fig. 4). 5

Then we calculate the minimum limit p1(k1)

Regions of root localization.

= 7.813.

From the expressions of coefficients p2(k2), p1(k1), p0(k0) and by applying the rules of interval arithmetic we find the setting limits for PIDcontroller

[1] Polyak B.T. & Scherbakov P.S. Robust stability and control. Moscow: Science, 2002, p. 303. [2] S.A. Gayvoronskiy & T.A Ezangina. Bundled Software for the Desing of Interval Dynamic Systems. Applied Mechanics and Materials. Vols. 446–447 (2013), pp. 1217–1221. [3] S.A. Gayvoronskiy & T.A Ezangina. Robust control of complex dynamic units with interval parameters. Systems and Computer Science (ICSCS), 2013 2nd International Conference on 26–27  Aug. 2013, Villeneuve d’Ascq, France, PP. 201–204.

k0 = [7.813; 39.51]; k1 = [ ]; k2 = [13 92 15 17 ].

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A MEMS digital seismometer with new structure J. Guo & S.H. Xu Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

ABSTRACT: A MEMS (Micro Electro Mechanical Systems) digital seismometer with a new structure is designed and developed; this includes the seismic sensor, weak signal detecting and feedback circuit, digitalized unit, controlling circuit, data communication unit and power supply circuit. As the force feedback circuit of MEMS sensor is introduced, the dynamic range and the small signal recognition ability is significantly improved, which makes such seismometers a substitution for traditional products; it is especially suitable for single seismometer receiving techniques. With no orientation and limited axial disturbance, MEMS digital seismometers are easy to constitute as a mutually orthogonal three-component digital seismometer. Keywords: 1

MEMS; seismometer; ASIC

INTRODUCTION

MEMS, the abbreviation of Micro Electro Mechanical Systems, is a super micro mechanical component with the ability of electronic induction or reaction. MEMS can be mass-produced at a comparatively low cost with a process similar to semiconductor production. Currently, MEMS components are widely used in automation equipment (such as airbag sensors and many types of pressure sensors of engines) and computer components (such as the nozzle of ink-jet printers and the hard disk pickup heads). As the MEMS technique is developed and is maturing, it is now used in seismometers for seismic exploration. Out of the inherent mechanical characteristics, the traditional moving-coil geophones presently widely used domestically and abroad, have low sensitivity, limited dynamic range (around 60 dB), narrow frequency band (10∼200  Hz), poor antiinterference ability and huge weight and volume. However, with the dynamic range up to 120 dB, the latest seismic instruments cannot be fully utilized. Compared to the regular moving-coil simulation geophones, MEMS digital seismometers show advantages which include low weight, broad frequency band, wide dynamic range, little aberration, strong anti-interference ability and so on: they will definitely substitute the moving-coil simulation geophones most widely used currently. MEMS digital seismometers are especially suitable for single seismometer high density sampling techniques. In view of the great technical advantages of MEMS digital seismometers, relative research has

been conducted and products have been released both in China and other countries. For example, ION, a US company, developed the VectorSeis three-component digital seismic sensor in 1999, and Sercel, a French company, introduced the MEMS digital seismic sensor—DSU unit. In this article, a new seismometer with MEMS + AD type will be introduced; it is different from the existing MEMS seismometer and has certain technical advantages. 2

THE INTEGRAL STRUCTURE DESIGN OF THE SEISMOMETER

Figure  1 is the block diagram showing the principle of the new MEMS digital seismometer. There are 6  main units: MEMS seismic sensor (MEMS), weak signal detecting and feedback circuit (ASIC), digitalized unit (AtoD), controlling circuit (CPU), data Communication unit (C) and Power supply circuit (P). The integral structure (see Figure  2 for reference) is constituted by the up cover, the outgoing cable, the circuit board, the sensor core, the shell and the tail cone, the digitalized unit (AtoD), the controlling circuit (CPU), the data Communication unit (C) and the Power supply circuit (P) are incorporated onto one circuit board. The MEMS seismic sensor (MEMS) and weak signal detecting and feedback circuit (ASIC) are integrated on one Aluminum alloy core, which has the same size with and can be interchanged with the regular moving-coil core. The difference is that the regular moving-coil core does not need power supply, but both MEMS seismic sensor and

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ASIC circuit need power supply. That’s why a pair of power supply circuit, besides a pair of signal circuits, is needed between the MEMS seismic sensor core and the circuit board. The circuit board and MEMS seismic sensor core are encapsulated in the plastic shell by the up cover, and the sensor core extraction electrode is connected to the circuit board. The 48 V power supply is adopted, and the digital seismometer should work regularly when the voltage is decreased to 27 V. There are 2 pairs of outgoing cables from the circuit board, one of which is connected with the outside power source to provide power for the circuit board, and the other will be the data line. The tail cone will be installed at the bottom of the shell as the grounding component. 3

Figure  1. Principle block diagram of MEMS digital seismometer.

Figure  2. Integral structure sketch of MEMS digital seismometer.

THE STRUCTURE AND FUNCTION OF EACH MODULE

MEMS digital seismometers adopt the sandwich structure of 3 layers of silicon wafers with a special bonding process. Compared with the sandwich structure of 4 layers of silicon wafers adopted internationally, the process is simple and the yield rate is high. The double-sided beam mass structure with 8 beam supporting and up-to-bottom symmetry for monolithic fabrication, as well as the once forming technology, not only simplified the fabrication process, but also greatly decreased the thermal noise and the cross axis sensitivity. The reliability of the MEMS sensors produced with the said design and process will definitely improve, and the rate of loss will obviously reduce. The weak signal detecting and feedback circuit (ASIC) adopts low noise capacitance signal enlargement technology, especially the simulated force feedback circuit to increase the signal noise ratio, which helps detect the weak signal and enlarges the dynamic range of the sensor to 110 dB, satisfying the requirement of high-accuracy seismic exploration. The introduction of a feedback circuit greatly improves the small signal recognition ability, the technical indexes of which reach international advanced level. MEMS digital seismometers of both ION and Sercel adopt digital force feedback pattern. The advantage is that controlling and recording is available and flexible. Domestic MEMS digital seismometers at earlier stage do not have feedback circuits, which leads to the result of limited dynamic range (less than 90 dB) and poor detecting ability of weak signals. The characteristics of the adopted simulated force feedback pattern include quick responding and simple circuit structure. This is the major difference with other MEMS digital seismometers in the world. The MEMS seismic sensor is directly connected and integrated with the weak signal detecting and

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feedback circuit (ASIC). Since the output signal of the MEMS seismic sensor is very weak, the sensor should be closely connected to the weak signal detecting circuit to avoid loss. So The MEMS seismic sensor is directly connected with the weak signal detecting and feedback circuit (ASIC), and both parts are integrated into one. Such a connection can avoid the need for transmission via cable of the simulated signal, keep the effective content of the weak signal and improve the anti-interference ability. The digitalized unit (AtoD) provides preamplification, Δ-Σ modulation, digital filtering and other functions. It also provides 24 digits A/D conversion accuracy. Programmatic configuration of the pre-amplification gain at 0 dB, 6 dB, 12 dB, 18 dB, 24 dB, 30 dB or 36 dB, as well as 4, 2, 1, 0.5 or 0.25  sampling rate can be realized. AtoD can employ either the A/D conversion suite CS3301 A, CS5373 A and CS5378 produced by Cirrus Logic or AD1282 chip by TI. Both products are specified chips for seismic exploration and widely used in seismic exploring instruments. MEMS digital seismometers of both ION and Sercel adopt digitalisation scheme with ASIC circuits. In this invention, ASIC circuits only complete the low noise capacitance signal enlargement and the simulated force feedback, which has wider pre-amplification gain and higher conversion accuracy and is suitable for the utilisation in different areas. The controlling circuit (CPU) is embedded CPU. It controls the digitalised unit to complete the analogue-digital conversion and controls the data communication unit to realise the communication and transmission of data with the seismic instrument system; it also controls the power supply circuit (P) to realise the power supply for the MEMS seismic sensor, the weak signal detecting and feedback circuit (ASIC), the digitalised unit (AtoD), the controlling circuit (CPU) and the data Communication unit (C). The controlling circuit (CPU) adopts a C51 or ARM series embedded CPU, the most energy saving chip; provided that it can fulfill the function of controlling the digitalized unit, the data communication unit and the power supply circuit. The data communication unit (C) provides the data communication function with the seismic

instrument mainframe system, receiving the controlling command of the mainframe and conducting the uploading of sampling data. It should be consistent with the data communication protocol of the mainframe system. 4

CONCLUSION

This MEMS seismometer has the dynamic range of up to 110dB and largely improved small signal recognition ability; i is especially suitable for single seismometer receiving techniques. After mass production, the cost will be effectively controlled, and it will surely be the substitution of traditional seismometers. With no orientation and limited axial disturbance, the digital seismometers described in this article can easily constitute a mutually orthogonal three-component digital seismometer. It will be obtained when three MEMS seismic sensors are orthogonally integrated on one Aluminum alloy core. ACKNOWLEDGEMENTS This work is supported by the Chinese National 863 Program (2012  AA061102) and by R&D of Key Instruments and Technologies for Deep Resources Prospecting (the National R&D Projects for Key Scientific Instruments), Grant No. ZDYZ2012-1-06-03. REFERENCES [1] Llobera A., Plaza J.A., and Salinas I., etc. Characterization and passivation effects of an optical accelerometer based on antiresonant waveguides IEEE Photonics Technology Letters. 2004, 16(1): 233∼235. [2] J. Dakin and B. Culshaw, Optical fiber sensors II: Systems and applications, Artech house, Boston and London, 1996. [3] J. Guo, G.Q. Ma, etc. MEMS-based digital geophone and its application. Petroleum Geophysical Exploration, 2005, 44(4): 348∼351.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

A type of real-time vibration monitoring system based on Ethernet and RS-232 S.H. Xu & J. Guo Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

P.P. Li CNOOC Research Institute, Dongcheng, Beijing, China

ABSTRACT: This article describes a method to implement the vibration monitoring system by the Ethernet and RS-232 serial port communication working together. Through real-time analysis of the first arrive and energy information from the sensor vibration data, the monitoring system can give us the source location and energy intensity. The system comprises a central station and a number of monitoring stations which are spread out in the monitored area and constituted LAN, using Ethernet. In the system, RS-232 will be responsible for the real-time transmitting of the second pulse signal from the GPS module, to each of the distributed real-time vibration monitoring stations. Then by Ethernet the monitoring stations provide and send sampling data of the vibration signal and GPS pulse signal to the central station. Then the central station will synchronise the vibration data from all the monitoring stations, based on pulse signals, and finally locate the vibration source and calculate the vibration energy. The system uses a simple apparatus to meet the application requirements. The system is reliable and has good applicability. Keywords: 1

vibration monitoring; RS-232; GPS second pulse

INTRODUCTION

Vibration monitoring system is a kind of widely used digital system with a combination of digital communications, sensors and other embedded systems technology. In some applications, the realtime calculation of vibration location and energy is required. The general method is to work within the monitored area to be dispersed, in a certain number of laid (at least 4) vibration monitoring stations (or monitor), with each monitoring station being responsible for the analog to digital conversion of the vibration sensor and real-time transmission to the central station, which is responsible for storing and processing data. The pre-requisites of data solver are: vibration data from each distributed collection stations is acquired in strict time synchronisation, the error should be less than half the sampling rate, such as when the sampling rate is 4 K and synchronisation accuracy should be more than 125 us. The cause for data collection not synchronized generally includes two things: time is not synchronised between different distributed systems and Ethernet is not fixed for time delay. A variety of methods can be used to synchronise data collection, such as the use of a high-precision clock and time server; in addition GPS synchronisation is a frequently used synchronisation method. GPS synchronisation is fantastic in

some areas such as inside a mine or seriously covered ground. By considering the system cost and it’s flexibility in use, we want to design a new kind of monitoring system in which the connection is between the division vibration monitoring stations and the central station is LAN—and the collection data of every vibration monitoring station is strictly synchronised. The traditional LAN network synchronisation method is generally by 1588 agreement, which will need higher requirements for the system hardware and software, and the implementation is more complex. Compared with Ethernet, RS-232 is a kind of low grade serial communication with a small and fixed delay. In this system a TCP/IP is used for the communication between the central station and the distributed monitoring, which completes the entire control and vibration data backhaul. At the same time, due to the current embedded systems which are usually backed with perfect Ethernet supportability, this system is easy to develop. 2 2.1

THE STRUCTURAL DESIGN OF MONITORING SYSTEM The topological structure

The schematic structure diagram of the vibration monitoring system is shown in Figure1. Part is

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from the GPS module is not present in any network node in the system. All distributed monitoring stations and the host system use the original RTC time, only high-precision GPS module second pulse signal is used to synchronise vibration signals between the various distributed monitoring stations. According to the working principle of the monitoring system, the exact time of occurrence of the vibration is not required. For the solution of the vibration position information calculation, as long as the vibration data in the data solver using various monitoring stations are relatively synchronised (synchronisation accuracy less than the sampling rate) to each other can be calculated by the exact spatial position of a vibration point. The time of vibration is defined by the system time in the central station. Figure  1. diagram.

Vibration monitoring system topology

2.2

a central station which is responsible for controlling the entire system, including the recovery and storage of data, and data demodulation computing work. Part is a fiber optical Ethernet switch, responsible for the Ethernet connection between the central station and the distributed monitoring stations. Part is the GPS timing module, which comprise a GPS antenna and a GPS receiver. The timing module can output second pulse signals and timing information through a serial port. When you first start the time module, four or more satellites signals are needed to receive to complete positioning and timing; after the positioning is completed, even if only one GPS satellite signals can be received, the timing can work properly. Part is a serial port to fiber conversion module, which is responsible for translating the GPS signals from the electrical type to optical type. Part is a light switch. When the distributed monitoring station and central station need to be connected in parallel, part is needed to divide the second pulse optical signal from part into multiple access for each monitoring station. Part and part represent the distributed monitoring stations and three-component geophone connected which are connected to the monitoring stations. The optical cable which is used to connect each monitoring station should have at least three cores, one pair for Ethernet, one for RS232 transmission. It should be noted that, unlike the conventional optical communication port, in the present system, RS-232 communication is only responsible for sending the PPS signal from the GPS module to each monitoring station. Each monitoring station is without signal returns, so a one-way transmission fiber optic cable is sufficient. Although GPS is used in the monitoring system described herein, the timing information output

Design of distributed monitoring station

Figure 2 is a schematic diagram of the structure of distributed monitoring station; CM is the control unit of distributed monitoring station, using AM9 as the core control chip, with a high frequency, high processing speed, low power consumption, and perfect Ethernet supportability. The AD module is an analog-digital conversion collection module with a four-channel data acquisition, each station using four Cirrus Logic’s CS5373 chip; with high collecting precision, maximum sampling rate can be up to 4 K. FPGA is also used to be responsible logic timing control and data processing functions of AD conversion. When the monitoring stations work, the first three AD channels are connected with three-component sensors responsible for collecting the vibration signals on the Z, X, Y three directions. The fourth channel is used to connect the serial port signal output via FOM2  module; its output signal is a 1PPS pulse signal of seconds. In every serial-fiber conversion module FOM2 of each distributed monitoring station the input and output terminals were short-circuited; the output

Figure 2.

Distributed monitoring station structure.

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distribution of fiber access the next station, used as an input. The power module supply power management for other modules provides digital 3.3 V, 5 V, and 2.5 V analog power supply and is with a 12 V DC power input terminal. During the test, the GPS module pulse signals, simultaneously, access to two channels in one distributed monitoring station, a direct input, and another channel, through two serial photoelectric conversion modules and cable transmission. Two pulse rise times of the signal to start at the acquisition beginning have a difference of one sample point (2.5  K sampling). After being collected for 24  hours, the error will generally accumulate to three sampling points, but the delay and the cumulative is all consistent for every distribution station. So, after we complete the data aligning according to the PPS signal’s pulse rising edge the collected three components vibration data are time synchronised. It should be noted that the delay of Ethernet is far less than 1 s, that is, it can be controlled within a second pulse signal. Therefore, the second pulse signal can be used to time corrected. After powering on each distribution station the host system releases the Ethernet command to detect the connection status with the distribution stations and after that determine the connection is normal, and then start sending commands to start AD and return collecting data. This gives an additional advantage of using the methods described herein RS232 assistant pulse signal synchronised with the vibration data for all the distributed monitoring station does not need to add additional time information. Of course, this requires two premises as a guarantee. First, the sample data of four channels in every distributed monitoring station is strictly synchronized. Secondly, each distributed monitoring station will send sampling data packets to the central station in the period of the same second pulse. 3

Figure 3.

The actual collection data.

excited vibration using a hammer around station one, is shown in Figure 3. A total of 16 channels, which 4, 8, 12, 16 channels are for GPS pulse signals. Figure 3 is the data conducted after the synchronisation correction according to the second pulse. From the three-component vibration sensor sampling data of the four distribution stations, the vibration reaches chronological order in line with the position of the distribution relationship. The experimental data were repeatedly hammering processed, after system solver, the vibration point position and measured position have an error less than 2 meters. This shows that when performed the real time vibration data acquisition and synchronization methods described herein method proved to be accurate and reliable.

PRACTICAL EFFECT ANALYSIS

We did an actual data acquisition experiment using a central station and four distribution stations. Four distribution stations are serially connected using a fiber optic cable which has four cores, two for Ethernet connections with the central station through the fiber optic switch. One fiber core which is used to receive GPS pulse signal from RS-232 serial is connected to the GPS timing module through the serial-fiber converter. The monitoring area is a cement ground with 40 multiplied by 40 meters. Four monitoring stations are located in the four corners of the monitored area. Surface vibration propagation velocity is about 300 meters per second. The acquired sample data from the

4

CONCLUSIONS

By using low-cost and simple Ethernet equipment and method we can complete the real-time vibration data transmission and high-precision time synchronisation. Unlike ordinary GPS timing in distributed systems, in our system the GPS signal is only given by the central station. That is as long as the central station has a normal function of GPS timing, the entire system can complete time synchronisation. Compared with other timing synchronisation method such as the time server and other highprecision clock, data synchronisation method described herein has the most simple structure and

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is the least expensive. It is quicker in some local area network system, particularly with the use of fiber-optic connections in a LAN system with a complete layout of the monitoring system. Optical cable with more than three cores can be used to complete the connection of the system. Next we can try to add the second pulse signal to the normal vibration acquisition channel, and then only three channels are necessary in every monitoring station. Equipment cost and the power consumption can be further reduced. ACKNOWLEDGEMENTS This work is supported by the Chinese National 863 Program (2012AA061102) and by R&D of Key Instruments and Technologies for Deep Resources Prospecting (the National R&D

Projects for Key Scientific Instruments), Grant No. ZDYZ2012-1-06-03. REFERENCES [1] B.M. Gurbuz. Upsweep Signals With high Frequency Attenuation and Their use in the Construction of Vibroseis Synthetic Seismograms. Geophysical Prospecting 1982, Vol. 30: p432–443. [2] Lee, Kyung Chang, Lee, Suk. Performance evaluation of switched Ethernet for real-time industrial communications. Computer Standards and Interfaces, 2002, 24(5): p411–423. [3] Decotignie, Jean-Dominique. Ethernet-based realtime and industrial communications. Proceedings of the IEEE, Industrial Communication Systems 2005, 93(6): p1102–1117. [4] Hopler, Robert B. A history of the development of instruments for measuring vibrations of the earth part 4. Journal of Explosives Engineering, 2006, (1): p38.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Study on the transient voltage stability of distribution systems considering large-scaled dispersed EV charging Y.X. Chen & Z.J. Chi Beijing Power Test Research Institute, Beijing, China

P.W. Zheng & X.N. Lin Huazhong University of Science and Technology, Wuhan, China

ABSTRACT: As an increasing amount of Electric Vehicles (EVs) are connecting to a power distribution system via charging devices, the load of power system will become heavier and more unpredictable. This may lead to serious risk for the security and stability of the system’s operation. In this paper, the quantitative influence that EV charging loads may bring to the transient voltage stability of distribution system is studied. It also analyses the security and stability status of power system integrated with largescaled EV loads. In order to indicate the margin of transient voltage stability of distribution network, an index is proposed. This index is represented by a relative value of slip, considering the torque-slip property of induction motor load. Furthermore, simulation analysis and calculations are carried out to study the transient voltage instability condition, focused on the dispersed charging scenario of large-scaled EVs. The result shows that the index proposed is capable of reflecting the profound effect of large-scaled EV loads on the transient voltage security of distribution network. Keywords: 1

electric vehicle; dispersed charging; transient voltage stability margin; voltage slip

INTRODUCTION

With the reducing of primary energy sources such as fossil energy, expending the usage scope of a secondary energy such as electric power will be put on agenda. Thus, as a significant element of it, the popularisation of the electric vehicle is attracting great attention. Using new energy automobiles to replace the fuel consumption and traditional vehicles, and replacing fossil fuels by electrical energy, are the only long-term solution to the future traffic situation. Large-scaled charging stations are needed in order to meet the wide spread of electric vehicles. However, the result of [1–2] shows that an EV charging station is a typical power electronic device of a large power base and high current, which could bring profound influence to the stability of the transient voltage of the power system. For the distribution network which contains much induction motor load, the connection of largescaled EVs will lead to different degrees of effects to the transient voltage stability of distribution network based on the location, the capacity and the pattern of grid-connecting. Current studies of the security and stability of the power system with which electric vehicles are connected, mainly focus on the influence on the generating capacity configuration due to massive

growth in charging loads and the influence on the voltage, loss, harmonic waves and load unbalance of power grid. Hotspots also lie in the assistant services that EVs can offer to the operation of a power system such as reactive compensation, load support, spinning reserve and frequency regulation etc. [3–5] In view of this, considering dynamic loads represented by different connecting proportions of induction motors, slip margin of induction motor load is used to reflect the transient stability margin. Then transient voltage stability of distribution network under different scenarios can be studied. By constructing the typical model of distribution network and the charging model of EV loads, the quantitative effect on the transient voltage stability of distribution network brought by dispersed EV charging loads of different penetrations, are thoroughly examined. 2

2.1

ANALYSIS OF ELECTRIC VEHICLE CHARGING SCENARIO AND CHARGING MODEL Electric vehicle charging scenario analysis

There are three types of electric vehicle charging modes, i.e. conventional charging mode, quick charging mode and battery swapping mode.

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Relatively low; charging at valley time is achievable; difficult to manage and maintain Relatively high; difficult to charge at valley time; beneficial for management and maintenance Relatively low; charging at valley time is achievable; reducing car cost; facilitate unified battery management, maintenance and recycling 5–10 minutes Battery swap station

Large-scaled centralized charging Large-scaled centralized charging Charging station

Traffic-intensive areas; along highways City center; along highways

About 30 minutes

Buses, taxis and private cars

Relatively low; about ¥20,000 for one charging pile (5 kw) Relatively high; determined by the construction scale Very high, about ten million RMB for one station Private cars; company and institution vehicles Buses, taxis and private cars 6–10 hours Dispersed charging

Parking lots; residential areas

Construction costs Target user Charging duration Suitable construction occasions

Charging spot

Main circuit structure and control strategy of the charger With the development of PWM rectifier technology, the bidirectional converter which utilizes IGBT as

Type of service

2.2.2

Charging infrastructure

2.2.1 Battery model Research shows that the most commonly adopted vehicle battery models are the internal resistance model, Thevenin model, fourth-order model, PNGV model, GNL model and so on. The Thevenin model concerned both the capacitive and resistance characteristic of the battery. It’s the most representative battery model which can describe the complete transient characteristics [6]. The Thevenin model is presented in Figure  1, where the ideal voltage source Uoc is the open circuit voltage of battery and RTo is the internal resistance. The parallel capacitor CTp and resistance RTp externalize the transient characteristics of battery.

Comparison of charging spot, charging station and battery swap station.

EV charging model analysis

As with the specific battery charging process, the strategy generally adopted nowadays is the socalled constant current-constant voltage two-stage charging. In the first stage when battery State of Charge (SOC) is relatively low, a constant current charging method is selected. Later on, when the battery voltage reaches the maximum allowable value, it changes to constant voltage charging method to avoid damage to the battery due to over-current. The charging power and charging duration required for the first stage was far greater than the second one. Therefore, the constant current charging stage is the only consideration in this paper when analysing the effect of charging facilities on the transient voltage stability of distribution network.

Table 1.

2.2

Charging and operating costs

According to their usages and requirements, different modes are adopted for the corresponding vehicles. For private cars, company and institution vehicles, conventional charging mode is generally adopted. As to buses and taxis, quick charging mode or battery swapping mode are more appropriate. The corresponding equipment for the charging modes mentioned above are a household DC charging spot, charging stations and battery swap station respectively. Comparison for the three kinds of infrastructures are shown in Table 1. The focus of this research is on the analysis of transient voltage instability of distribution network under dispersed charging scenario. In this scenario, a penetration rate ρ is used to produce EV charging load value for each load bus by multiplying ρ with the active power of the original load buses and allocating the product to each bus of the distribution network.

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the Total Harmonic Distortion (THD) of gridconnected current for charging facility should be less than 1%, which is fully considered in the simulation afterwards. 3

Figure 1.

Existing research [7–10] determines the short-term large disturbance voltage stability of power system by analysing the dynamic stabilisation of induction motor loads after the disturbance. According to literature [9] in which an analytical assessment approach for the transient voltage stability of a load bus is proposed, the critical clearing time tc is adopted to reflect the transient voltage stability degree of the load bus containing induction motors. The analytical calculation formula is as follows:

Thevenin model of battery.

tcm

Figure 2.

tc −

Sdec 2 H ( sc − s = Tm Tm

) − Sdec Tm

(1)

where 2H is the inertia time constant of the induction motor; s0 and sc means the initial slip and the critical slip respectively; Sdec represents the deceleration area after fault is cleared and Tm indicates the load torque. Taking into account the indication role of the critical slip sc, i.e. the unstable slip under small disturbance, on transient voltage stability degree, a margin index η for transient voltage stability is proposed in this paper:

Electrical schematic of the EV charger.

η= Figure 3.

TRANSIENT VOLTAGE STABILITY MARGIN INDEX OF DISTRIBUTION NETWORK CONSIDERING THE EFFECT OF INDUCTION MOTORS

scm − smax scm

(2)

Schematic diagram of charger control.

the main power device can not only achieve conversion under unity power factor but also has excellent control of the waveform of grid-connected current. Meanwhile, four-quadrant operating is achievable as well. So recently it has gradually started taking the place of traditional phase-control rectifiers. The electrical schematic of the EV charger adopted in this paper is shown in Figure 2. A coordinated control strategy by combining AC-DC unit control with DC-DC unit control, which ensures the constant DC bus voltage and the constant battery current separately, is selected to achieve a stable recharging process for EV batteries, as shown in Figure 3. According to the code requirements by national standard GB 19939-2005,

Figure 4. Torque-slip curves of the general motor model.

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where scm is the corresponding critical slip at the time epoch when terminal voltage reaches its minimum [11] after the fault is removed. smax is the maximum slip after the clearance of the fault and it can be obtained by time-domain simulation. 4

SIMULATION AND ANALYSIS OF THE IMPACT OF LARGE-SCALED EV DISPERSED CHARGING ON TRANSIENT VOLTAGE STABILITY IN DISTRIBUTION SYSTEM

4.1

Simulation configuration

As is shown in Figure 5, a 14 node distribution network system model [12] is built based on the platform of Simpower system in Matlab/Simulink. For the full consideration of the potential influences of different connection penetration of EVs on the transient voltage stability of distribution network, a large disturbance of three-phase short circuit fault is set to occur at point A1 in this paper. First of all, EV charging penetration ρ is defined as:

ρ=

Pev PL

(3)

where PL is the active power of each load bus and Pev is the active power of EV load connected.

So, based on the inherent load of each node PL, the active and reactive power of EV load Pev and Qev can be calculated according to the power factor PF. The charging power of each EV is set as 180  kW, and the power factor of charger is 0.95 for simulation. Then, based on the aforementioned EV charging model, an assembly charging model is established and connected to the distribution power system. Meanwhile, by utilizing the transient voltage stability margin index η considering induction motor load, the quantified influence of EV load connection on transient voltage stability margin of the distribution system can be calculated. 4.2 Simulation results When EVs are not connected and the fault is set to occur at 2.0 s, it is discovered by time domain simulation that node N8 of the distribution network is the first load bus to lose stability at 2.1035 s. Therefore, node N8 is the most stability-vulnerable load bus. In the following discussion, node N8 will be regarded as the reference node which can measure the transient voltage stability of the entire distribution network. When considering a dispersed charging scenario where EV load penetration ρ = 10%, the simulation is carried out with the fault occurring at 2  s and cleared at 2.08 s. Results are listed in Table 3 where the maximum slip Smax of each node of the distribution system, the calculated unstable slip greater value Scm and the transient voltage stability margin index η are included. It can be seen that node N8 and N9 are more likely to lose stability. Comparing it to the situation

Table 2.

Load parameters of distribution network.

Branch number

I

II

III

Load type

Commercial load

Industrial load

Residential load

Proportion of comprehensive load Static load 35% 25% Induction 65% 75% motor load

Figure 5.

14 node distribution network.

Parameters of induction motor loads H 1.5 3.2 s0 0.0113 0.0050 0.035 0.029 Rs Xs 0.094 0.067 Xm 2.8 3.8 Rr 0.048 0.009 Xr 0.163 0.17

50% 50%

4.7 0.0076 0.064 0.137 3.0 0.059 0.167

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EV charging scenarios, the evolution of transient voltage stability margin is shown in Figure 7.

Table  3. Transient voltage stability margin index of each node at 2.08  s when EV load penetration ρ = 10%. Node number

Smax

Scm

η

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14

– 0.0470 0.0628 0.0236 0.0453 0.0685 0.0352 0.0717 0.0677 0.0527 0.0335 0.0233 0.0235 0.0281

– 0.237 0.0893 0.310 0.253 0.293 0.464 0.0797 0.0825 0.103 0.381 0.316 0.297 0.225

– 0.802 0.297 0.923 0.821 0.766 0.924 0.101 0.179 0.488 0.912 0.926 0.921 0.875

As can be seen from the simulation results, with the increase of EV load capacity connected, the change of transient voltage stability margin index of industrial load branch II which contains node 3, 8, 9, 10 and 11 is the most obvious. The access of large-scaled EV load will bring challenges to the transient voltage security of the dynamic load which is of high power value and load rate in industrial loads. The change of commercial load branch I which contains node 2, 5, 6 and 7 is relatively insignificant compared to branch II and the change of residential load branch III which contains node 4, 12, 13 and 14 is the smallest. Further simulation reveals that when 30% penetration rate of EV dispersed charging load is connected to the test distribution system, after the large disturbance of fault occurring in 2  s and cleared at 2.08 s, node N9 will lose its stability. This paper analyzes different EVs charging scenarios and simulates focusing on dispersed charging mode. According to the transient voltage stability margin index considering the effect of induction motor in distribution network, the impact of various penetrations large-scaled EV dispersed charging on transient voltage stability of distribution system is quantified. Result shows that the access of large-scaled EV loads will bring profound impact and challenge to the transient voltage security of the power distribution system, especially to the feeder with high dynamic load components.

Figure  6. Slip change tendency of node N8 with EV load connected.

CONCLUSION

REFERENCES

Figure 7. Comparison of transient voltage stability margin index under different dispersed charging scenarios.

without EV load, transient voltage stability margin of node N8 and N9 is calculated to decrease by 49% and 58% respectively. The slip s change of the induction motor load for node N8 with and without EV load connection are shown in Figure 6. Moreover, when considering the dispersed charging scenario where EV load penetration ρ  =  20%, transient voltage stability margin index of each node can be calculated similarly. By comparing different

[1] Schneider K., Gerkensmeyer C., Kintner-Meyer M., et  al. Impact assessment of plug-in hybrid vehicles on pacific northwest distribution systems [C]//Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE. IEEE, 2008:1–6. [2] H.U. Zechun, Song Yonghua, X.U. Zhiwei, et  al. Impacts and Utilization of Electric Vehicles Integration Into Power Systems [J]. Proceedings of the CSEE. 2012. 32(4):1–10. [3] Tomić J., Kempton W. Using fleets of electric-drive vehicles for grid support [J]. Journal of Power Sources. 2007. 168(2): 459–468. [4] Ota Y., Taniguchi H., Nakajima T., et  al. Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging [J]. Smart Grid. IEEE Transactions on. 2012. 3(1):559–564. [5] Kempton W., Tomić J. Vehicle-to-grid power fundamentals: calculating capacity and net revenue [J]. Journal of Power Sources. 2005. 144(1):268–279.

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[6] Chen M., Rincon-Mora G.A. Accurate electrical battery model capable of predicting runtime and IV performance [J]. Energy conversion, IEEE Trans on, 2006. 21(2):504–511. [7] Sun Huadong, Zhou Xiaoxin, L.I. Ruomei. Influence of Induction Motor Load Parameters on Power System Transient Voltage Stability [J]. Power System Technology, 2005. 29(23):1–5. [8] Zhao Bing, Tang Yong. Dynamic Characteristics Analysis of Induction Motor Loads [J]. Proceedings of the CSEE, 2009. 29(7):1–5. [9] L.I. Lili, L.U. Chao, C.K. Wong. Analytical Assessment of Transient Voltage Stability of Load Bus Considering Induction Motors. Automation of Electric Power Systems [J], 2009. 33(7):1–5.

[10] Xu Taishan, Xue Yusheng, Han Zhenxiang. Quantitative Analysis for Transient Voltage Instability Caused by Induction Motors. Automation of Electric Power Systems [J], 1996. 20(6):12–15. [11] Yunus K., De La Parra H.Z., Reza M. Distribution grid impact of Plug-In Electric Vehicles charging at fast charging stations using stochastic charging model [C]//Power Electronics and Applications (EPE 2011), Proceedings of the 2011-14th European Conference on. IEEE, 2011:1–11. [12] S. Civanlar, J.J. Grainger, H. Yin, et  al. Distribution Feeder Reconfiguration for Loss Reduction [J]. IEEE Transactions on Power Delivery. 1988. 3(3): 1217–1223.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Fault diagnosis and failure rate analysis of power transformer based on cloud relation space model Li-Juan Guo & Song-Mei Tao Electric Power Research Institute, Guangxi Power Grid Corporation, Nanning, China

ABSTRACT: In order to solve the problem of randomness and fuzziness in the sample of power transformer fault diagnosis and failure rate analysis, a new fault diagnosis and failure rate analysis method for power transformer, based on a cloud relation space model, is proposed. By using a cloud generator to analyse total dissolved gas content and percentage of dissolved gas in transformer oil, building cloud combination, and getting a new cloud fault diagnosis model by establishing the relation space between the fault type and cloud combination. Failure rate can be deduced by analysis of the relation space. The result of the research proves the effectiveness and reliability of this method. Keywords: 1

power transformer; fault diagnosis; failure rate; cloud model; relation space done by establishing relation space between the fault type and cloud combination.

INTRODUCTION

Power transformers are important equipment in power systems. Any fault in the power transformer may lead to the interruption of the power supply. So it is of vital importance to detect the incipient fault of the transformer as early as possible. Diagnosis of potential faults concealed inside power transformers is the key of ensuring stable electrical power supply to consumers. Conditioned monitoring and software-based diagnosis tools are central to the implementation of efficient maintenance management strategies for many engineering applications, including power transformers. Dissolved Gas Analysis (DGA) has been widely recognized as an effective diagnostic technique for power transformer faults detection. In the past years, various fault diagnosis techniques have been proposed. But these methods only consider the fuzziness of date, ignoring the randomness of date. The traditional cloud model can be better for taking the fuzziness and randomness into account, but is lacking the diagnostic capacity to normal samples and the study about the relationship among samples. This paper presents a new transformer fault diagnosis method based on the cloud model. The sample data of dissolved gas in transformer (H2, CH4, C2H6, C2H4 and C2H2) can be transformed into percentage of each gas and total dissolved gas content. Then, the sample was transformed into multiple qualitative cloud concepts through the cloud model. Simplifying the database by determining the dissolved gas sample data belongs to their corresponding cloud concepts. Building cloud combination, and getting the cloud fault diagnosis and failure rate analysis model can be

2

DATA ANALYSIS BASED UPON CLOUD THEORY

2.1

Cloud theory

Cloud theory merges fuzziness and randomness values expressed by natural language, and constitutes the relationship between the qualitative and quantitative. The Cloud model is based on two theories—probability theory and fuzzy set theory. And it portrays a unified concept of randomness, fuzziness and relevance, through a specific algorithm for constructing. Cloud theory is an uncertainty transformation model between qualitative concepts and quantitative values. The Cloud model consists of three digital characteristics: expectations, entropy, super entropy. Expectations (Ex) indicates the cloud distribution center; Entropy (En) describes the fuzziness of the concept. Super entropy (He) reflects the uncertainty of the Cloud. 2.2

Data analysis

At first, pre-process the date of fault transformer oil dissolved gas. Hi = Gij =

hi hmax

gij hi

(1) (2)

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where Hi is value of total dissolved gas after normalized, hi is value of total dissolved gas before normalized, hmax is the maximum value of each gas. Gij is percentage of gas j in sample i, gij is the exact value. Then the sample i can be expressed as (Hi, Gi1, ……Gi5). Analysis of the frequency distribution of five kinds of gases and total dissolved gas content. Figure 1 is frequency distribution of total dissolved gas, Figure 2 is frequency distribution of C2H4%. Then, this paper uses the cloud transformation algorithm based on the peak of the cloud to processing the frequency curve. The algorithm can extract the cloud concept distribution form the sample data. n ⎧ ⎪ f x ) → ∑ (ai * c( Exi , Ennii , H ei )); ⎪ i =1 ⎨ n ⎪0 < f x x)) − ∑ (ai * c( Exi , Eni , H eiei )) < ε ⎪ i =1 ⎩

(3)

where f(x) is frequency distribution function, ai is amplitude, c(Exi, Eni, Eri) is a cloud after

Figure 1.

Frequency distribution of the dissolved gas.

Figure 2.

Frequency distribution of C2H4%.

transformation, n is the number of the cloud, ε is the maximum error in the transformation process. The local maximum points of the frequency distribution are center of date, and their position are the expectations in this paper. Using this method to analyse C2H4% and total gas. The specific algorithm is as follows: 1. Find each peak point in Figure 1 and Figure 2, and define the coordinate values as Exi. 2. Calculate Eni of each cloud model, and calculate probability density function of each cloud y functi ( Exi )2 / ( Eni )2 (x model fi x ) = e −(x . The results are shown in Figure 3 and Figure 4. 3. Calculate Hei of each cloud model by reverse cloud algorithm. Then, the cloud model is represented as C(Exi, Eni, Hei). The cloud model of H2%, CH4%, C2H2% and C2H6% can bededucedin the same way. According to the Figure  3 and Figure  4, parts of the cloud model are too close, and some cloud models are covered by each other. The duplication and unnecessary of cloud model can cause much

Figure  3. Superposition of gas curves of the cloud models after cloud transformation.

Figure 4. Superposition of C2H4% curves of the cloud models after cloud transformation.

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hardship in data partitioning. So, simplifying the state space is necessary. Considering the peak of cloud model and human cognitive characteristics, this paper uses the cloud merging algorithm to merge some cloud models when the distance is too close. For C2H4%, the number of cloud models becomes 4 from 9 by using the cloud merging algorithm. Analysis of the cloud model of H2%, CH4%, C2H2%, C2H6% and total gas by the same algorithm. The final cloud model is show in Table 1. 2.3

Relation space

Use 500 samples (x1, y1), (x2, y2)……(x500, y500) as training samples, and yi = Y = {1,……,6} is diagnostic results, Y1—Healthy, Y2—Low energy discharge, Y3—High energy discharge, Y4—Low temperature overheat, Y5—Middle temperature overheat, Y6—High temperature overheat. The training samples can be used to construct a relation space between cloud combination and diagnostic result.

μkj

∑

yi

i ( μik )

(4)

j

where μkj is correlation of the cloud combination k (CX1, CX2, CX3, CX4, CX5, CX6) and fault type j.

μik = {μiCCX μiCX

μiCCX

μiCX

μiCCX

μiCX

}

(5)

where μik is correlation of each elements of training sample i and the cloud combination k. For example, μiCx2 is correlation of H2% in sample i and cloud Cx2.

μiCCX 2 = e

−

(

i1 − ExCx 2 ) 2 2 EnCx 2

2

(6)

where gi1 is H2%, ExCx2 is expectation of cloud of Cx2, EnCx2 is entropy of Cx2. Partial relation space of cloud combination to fault diagnosis is shown in Table  2. Such as the cloud combination k(1,2,2,2,5,3) means total gas cloud C1, H2% cloud C2, CH4% cloud C2, C2H6% cloud C2, C2H4% cloud C5, C2H2% cloud C3. 3

FAULT DIAGNOSIS AND FAILURE RATE ANALYSIS MODEL

Get the fault diagnosis and failure rate analysis model by building the relation space. The correlation of test sample and diagnostic results can be deduced by the relation space from the cloud combination to fault diagnosis.

Table 1. Ex and En of cloud models. H

H2%

CH4%

C2H6%

C2H4%

C2H2%

Ex

En

Ex

En

Ex

En

Ex

En

Ex

En

Ex

En

0.01316 0.15000 0.30535 0.5000 0.77091

0.06580 0.03197 0.08271 0.02866 0.14079

0.05170 0.28100 0.53487 0.93803 \

0.06400 0.08180 0.06814 0.06525 \

0.02190 0.15000 0.25979 0.3500 0.47229

0.043015 0.054600 0.035105 0.03199 0.09358

0.01360 0.06365 0.12648 0.19480 \ \

0.0169 0.01604 0.01732 0.02646 \ \

0 0.08000 0.26365 0.52212 0.71798 \

0.02105 0.04881 0.07854 0.06510 0.04859 \

0 0.01200 0.02188 0.03382 0.04956 0.08656

0.004564 0.002960 0.003044 0.005068 0.005754 0.007279

Table 2.

Partly relation space of cloud combination to fault diagnosis.

Cloud combination

Healthy

Low energy discharge

High energy discharge

Low temperature overheat

Middle temperature overheat

High temperature overheat

(1,2,2,2,5,3) (1,2,2,1,3,1) (2,3,1,2,3,4) (5,2,1,4,1,3) (3,3,5,3,3,1) (5,1,5,2,5,3) (5,1,3,3,5,4)

5.13E-06 1.44E-09 2.58E-08 4.21E-09 2.85E-08 1.36E-09 1.13E-10

4.13E-12 9.52E-07 5.72E-11 8.08E-09 4.55E-09 4.13E-12 2.73E-10

1.61E-13 3.86E-10 2.22E-04 3.86E-10 3.83E-10 1.61E-13 5.91E-08

9.64E-12 1.13E-08 7.83E-09 2.42E-06 6.83E-08 9.64E-12 1.51E-06

1.19E-13 4.88E-09 6.17E-10 6.10E-07 2.50E-07 1.19E-13 3.85E-06

4.24E-12 1.20E-09 1.61E-16 9.39E-07 2.58E-10 1.17E-05 2.81E-04

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Table 3.

Test samples.

Gas contents

Probability

H2

CH4

C2H6

C2H4

C2H2

Y1%

Y2%

Y3%

Y4%

Y5%

Y6%

True state

80.6 23.7 14.67 85 44.09 57 293 20.9 150 49.2 247.7 147.2 45.3

8.9 1.9 3.68 26 13.64 13 50 10.5 130 33.2 148.7 20.7 32.4

5.3 0 10.54 17 2.64 0.1 13 11.5 35 15 78.7 10.9 14.8

4.7 1.5 2.71 17 20.89 11 115 18.1 95 49.9 192.7 76 97.1

0.3 0 0.2 48 20.21 12 120 1.4 0 1.4 0.9 6 6

89.9528 99.3332 94.4253 0.03157 2.06768 0.09754 1.20438 2.73044 0.45383 0.74336 0.32329 0.00073 0.00099

0.32150 0.00232 0.74125 73.0326 68.9153 2.88564 1.01794 0.02466 0.05263 2.05240 0.46642 0.00036 0.00087

8.30375 0.06833 3.55581 26.6473 17.5222 78.5044 93.0368 5.70288 0.42264 0.04778 0.28596 0.05918 0.08338

0.00332 0.00018 0.83350 0 4.95468 0.00577 0.17571 55.0442 65.9439 25.1438 2.46449 0.64174 0.58703

0.00958 0.00178 0.01025 0.28838 1.47632 0.01009 0.00664 32.2722 19.9351 66.4104 65.9537 1.53037 1.18005

1.40899 0.59414 0.43387 0.00005 5.06377 18.4964 4.55849 4.22556 13.1918 5.60215 30.5061 97.7675 98.1476

1 1 1 2 2 3 3 4 4 5 5 6 6

f( )=

∑ μk μkkj

(7)

y yj

Then, normalized all the correlation. pi =

fi x ) 6

∑ fi x )

i = 1, 2, ..., 6

REFERENCES (8)

i =1

where Pi is the probability to state i (Healthy, Low energy discharge, High energy discharge, Low temperature overheat, Middle temperature overheat, High temperature overheat). The possibility of healthy is P1. 4

EXAMPLE

This paper analysed 500 training samples, and uses 13 samples to test the cloud relation space model. The test results are shown in Table  3. For most of samples, diagnostic accuracy rate was close to 100%. Partly results are ambiguous, but the probability to true state is more than 50%. 5

This model have a high accuracy, and intuitive result representation.

CONCLUSIONS

The data of dissolved gas in transformer is transformed into multiple qualitative cloud concepts through the cloud model, which is consistent with human’s cognition better. And the problem of data partitioning at the boundary is effectively solved. Cloud model can keep the multiplicity of data.

Sun Caixin, Chen Weigen, LI Jian. 2004. The fault diagnosis technology and electrical equipment online monitoring gas in the oil. BEI JING: Science Press. Zheng Hanbo. 2012. Study on condition assessment and intelligent fault analysis for power transformers. Chongqing, China: Chongqing University. Guo Yinchen, Song Qiong, FAN Xiuling. 2013. Transformer fault diagnosis based on semi-supervised classifying method. High Voltage Engineering. 39(5): 1096–1100. He Zheng, He Biao. 2012. The application of cloud model in fault diagnosis in power transformers. Journal of Anhui Electrical Engineering Professional Technique College, 17(1): 4–9. Zhang Yiyi, Liao Ruijin, Yang Lijun. 2012. An assessment method for insulation condition of power transformer based upon cloud mode. Transactions of China Electrotechnical, 27(5): 13–20. Zhou Quan, Xu Zhi, Liao Ruijin, et al. 2013. Insulation condition assessment of power transformer bushing based on cloud model an kernel vector space model. High Voltage Engineering, 39(05): 1101–1106. Li Deyi. 2005. Uncertainty artificial intelligence. BEI JING: China national defense press. Zhou Quan, Sun Chao, Liao Ruijin, et  al. 2013. Multiple Fault Diagnosis and Short-term Forecast of Transformer Based on Cloud Theory. High Voltage Engineering, 40(05): 1453–1460. Li Xingsheng, Li Deyi. 2003. A new method based on cloud model for discretization of continuous attributes in rough sets. Pattern Recognition and Artificial Intelligence, 16(1): 33–37.

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Mechatronics Engineering and Electrical Engineering – Sheng (Ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02719-0

Analysis on technical innovation and regional disparities growth of China Jun Xiong Jiangsu Institute of Commerce, Nanjing, China

Wen-Wu Zhang Nanjing University of Finance, Nanjing, China

ABSTRACT: Optimising distribution of innovative capitals and promoting collaborative innovation are important ways to achieve regional coordinated development in China. Using panel data of 30 China’s provinces during the period of 1990∼2010, this paper studies effects of innovation agglomeration on the industrial agglomeration and economic growth. Keywords: innovation agglomeration; local knowledge spillovers; Chinese regional disparities; regional collaborative innovation 1

INTRODUCTION

The uneven regional development of China has increasingly become a prominent contradiction. How to effectively reduce the development gap between less-developed areas and developed areas has become a pressing issue for a coordinated economic development for China. The policy makers of less-developed regions, are more concerned about how to achieve the regional economic growth. Economic theorists have offered a lot of research and many interpretations on “Economic Growth”. From the neoclassical growth theory, which focuses on exogenous factors of improvement, to the endogenous growth theory which emphasises on the technological progress in the economic system, economic growth has always been an important topic amongst economic theorists. With the fourth revolution of monopolistic competition and increasing returns, and a new perspective, spatial economics has provided a theoretical tool and empirical method for people to study the differences in economic growth and spatial economy. Compared with previous studies, this paper tries to make some extensions from the following aspects: 1) We used Chinese economic statistics and science & technology statistics during the period of from 1990–2010; the related indicators, as degrees of innovation agglomeration of China’s provincial regions and regional knowledge spillover effects, have been built and measured. Unlike the previous studies, which chose a single indicator to measure the innovation capacity, we have

referenced the knowledge production function of Duranton and Puga (2003) and Zvi (1979). We synthetically measured both the degrees of innovation agglomeration and regional knowledge spillover effects, from three aspects, including production, transfer and accumulation of knowledge. Both existing innovations that were represented by patents and knowledge diffusion factors like technology exchange and patents transfer etc. have been considered, while the future innovation elements like human capital etc. have also been introduced; 2) In order to comply with China’s development reality, this paper has taken into consideration the cost differences of regional knowledge diffusion and empirically analysing the impacts that both innovation agglomeration and regional knowledge spillover effects, have on regional economic growth; moreover, evidence of China’s economy has been provided as an empirical study of the spatial economics theory from the perspectives of innovation agglomeration and its spatial effects. Based on the above discussion, the structure of this paper is organised as follows: In the second part, the econometric model will be set with a data description. The resulting analysis of the econometric model will be carried out in the third part; and a conclusion will be given in the final. 2 2.1

METHODOLOGY Empirical model and variables

The differences between the spatial structure of industry and regional economic growth are the

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regional innovation central city in this paper. The logarithm of initial level of GDP per capita has been introduced into explanatory variable as it was treated in the traditional literature, i.e. ln gdp0 in the above expression, for observing if “conditional convergence” exists in regional economic growth. For controlling the impact that the government intervention has on economic growth, the government spending as a share of GDP gov has been introduced. In this model, the subscript i also stands for regions, Z is regional dummy variable for innovative zoning, and ε is error term.

results of interaction among multiple variables, including innovation agglomeration, local knowledge spillovers, etc. The result cannot be achieved by using an ordinary linear model or through verifying the impact of a single variable. Therefore, we use the ideas and methods of Kesidou and Romijn (2008) for reference in examining the relations between local knowledge spillovers and the innovation output of enterprises, and run the empirical test from the industrial clusters’ aspect as well as the economic growth aspect. a. The econometric models of the spatial structure of industry We have used the model of Ellison, Glaeser and Kerr (2008); the dependent variable is the variable that reflects the characteristic of industrial clusters, and the independent variables are the factors with influences on industrial space that have been focused on in this paper, which include innovation agglomeration and local knowledge spillovers, etc. The concrete model is: Aggloi = α + α1kpi + α2lksi + α3iksi + θZj + ε In the above expression, the explained variable Agglo is the degree of spatial concentration of the manufacturing industry; the explanatory variable kp is the degree of innovation agglomeration; lks is the effects of local knowledge spillovers, to measure the relevance of learning and transfer degree within the region. In order to observe the impact of effects of knowledge spillovers more comprehensively, we introduce the variables of effects of international knowledge spillovers for reference and comparisons, iks represents international knowledge spillovers, the subscript i stands for regions, and ε is error item. b. The extended model of economic growth The second focus of this paper is observing the impact that the factors like innovation agglomeration, local knowledge spillovers, etc. have on the regional economy. Based on the econometric model of Barro (2000), which uses crosssectional data to study the determinants of economic growth, our model is as follows: dgdpi = β + β1kpi + β2lksi + β3geoim + β4lngdpi0 + β5govi + γZj + ε

2.2

3 3.1

The dependent variable dgdp is the annual growth rate of GDP per capita; the meanings of independent variable kp and lks are the same as in the model (a); as the impact of spatial factors has been taken into consideration, geo represents the geographic locations of innovation where the area is, which has been presented by the distance from this area to the nearest

Data source

In the light of data availability, the time span in this paper is 1990–2010; the required data for innovation agglomeration, local knowledge spillovers and economic growth are mainly retrieved from “China Statistical Yearbook on Science and Technology” (1991–2011), “China Statistical Yearbook” (1991–2011) and “China Population & Employment Statistical Yearbook” (1991–2011); the data of instrumental variables is mainly from “China Compendium Of Statistics 1949–2008”, which takes 30 provinces and relevant items as observation samples; the range of data is 27 provinces and 3  municipalities in total. In order to ensure the consistency and accuracy of data, we have adjusted the inconsistent statistical data, and use the online database of the China Economic Information Network to supplement the missing data, for making the data complete and reliable as possible. Considering the impact of spatial factors, the data such as geographic distance and areas of some provinces and cities have been used in this paper as well; innovation geographical distance data has been mainly obtained through using the distance measurement function of Google Earth 7.0  software database, and the areas of provinces and cities are from the published land areas in the Central People’s Government website as standards. RESULT ANALYSIS Measurement strategy

We have an empirical regression on the relations among innovation agglomeration, knowledge spillovers and regional economic growth. Equation (a) and (b) have set the basic regression models; now the regression has been set as follows: 1. According to the data in posession, in order to examine the robustness of the estimation results, we use the method gradually and add control variables into the regression equation. When

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lks have on manufacturing industrial agglomeration and regional economic growth of China are all positive, and at 5% level of significance. As we gradually introduce control variables, though the coefficient values vary within a certain range, there’s no substantial change in impact symbols and significance, which show a high robustness of the model. Moreover, after adding the control variables, the R-squared has increased, which has further enhanced the explanatory result of the equation. In the equation of industrial agglomeration, the impact coefficients of innovation agglomeration kp and local knowledge spillovers lks are significantly positive, which indicate that technology innovation and knowledge spillovers are the two important factors for promoting the spatial structures of China’s industry to agglomerate, and this also coincided with the general rules of spatial economic theories and economies of agglomeration. In the equation of economic growth, innovation agglomeration kp and local knowledge spillovers lks also show positive effects on promoting regional

control variables was added, if the symbol of regression coefficient stays unchanged, even if the statistical significance is reduced, it will still be regarded as there’s no fundamental change in statistical significance, and the robustness of the results can be verified. 2. For testing the validity of instrumental variables, we separately carry out “under-identification test”, “weak identification test” and “over-identification test”, and report the corresponding statistics of Anderson canonical correlations LM statistic, Cragg-Donald Wald F statistic and Sargan statistics with concomitant probabilities, to indicate the effectiveness of selected instrumental variables. 3.2

Results of estimation and measurement

Table  1  shows the estimation results of equation (a) and (b) that were obtained by using ordinary least squares with fixed effects. As can be seen from the table, the coefficients of impact that innovation agglomeration kp, local knowledge spillovers

Table 1.

Fixed effects OLS estimation results. Agglo

Var kp lks iks

(1)

dgdp (2)

**

1.063 (0.092) 0.314** (0.044) 0.437** (0.056)

(3) **

(4) **

(1) **

1.041 (0.086) 0.262** (0.036) 0.381* (0.042)

0.922 (0.021) 0.247** (0.013) 0.322 (0.055)

0.917 (0.049) 0.201** (0.027) 0.274* (0.012)

0.146** (0.011)

0.126*** (0.024) 0.213 (0.141) 0.102* (0.019)

−0.157** (0.029) 0.5228 609

−0.116** (0.019) 0.5479 609

0.248** (0.044) 0.241 (0.096) 0.259** (0.046) 0.214* (0.047) 0.101* (0.018) −0.093** (0.012) 0.6022 609

gov geo ln gdp ln FDI K MARK φ ZD C R2 Obs

−0.164* (0.031) 0.5163 651

(2) **

(3) **

(4) **

2.137 (0.106) 1.141** (0.031) −0.536* (0.011) 0.209*** (0.017) −0.113** (0.023) 0.264* (0.039)

1.932 (0.094) 1.066** (0.067) −0.481 (0.108) 0.197*** (0.032) −0.104** (0.011) 0.203* (0.041) 0.332 (0.121)

1.761 (0.083) 0.936** (0.054) −0.358* (0.050) 0.189** (0.011) −0.097** (0.007) 0.182* (0.022) 0.316 (0214) 0.193** (0.041) 0.135** (0.028)

0.176* (0.062) 0.4974 651

0.113** (0.037) 0.5247 609

0.0.107** (0.015) 0.5369 609

1.819** (0.152) 0.901** (0.038) −0.377** (0.022) 0.186** (0.014) −0.088** (0.009) 0.177* (0.017) 0.348 (0.137) 0.213** (0.034) 0.149** (0.015) 0.105* (0.036) −0.137* (0.013) 0.089** (0.021) 0.5578 609

Notes: Values in brackets are standard deviations. ***, **, * separately represent that levels of statistical significance are 1%, 5% and 10%. Under the regression coefficients, data in parentheses is the standard deviation of the regression coefficients.

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economic growth. To some extent, this has proved that technology innovation and knowledge spillovers play important roles in local economic growth. As local knowledge spillovers involve inter-regional innovation diffusion and technology dissemination, which indirectly reflect the necessity that inter-regional technology sharing and innovation collaborating have for optimising spatial economic structure. It is noteworthy that, as a comparative variable, the impact that international knowledge spillovers has on industrial agglomeration and regional economic growth show reverse effects. The import of international technology has promoted industrial agglomeration, but also has divided the speed of inter-regional economic growth. But the significance of this coefficient is not stable in the results, and we still need to introduce the instrumental variables in order to confirm the significance of results. Besides, the impact that geographic innovation variable geo has on regional economic growth is significantly negative, which shows strong spatial differences. The coefficient of variable ln gdp0 is positive which measure conditional convergence by introducing initial GDP per capita has passed the test at 10% level of significance. This indicates that China’s regional economic growth had no “conditional convergence” characteristics (at least during the past years of 1990–2010). The variable of government spending gov has a significant effect on promoting the economic growth, which indicates that regional economic growth largely relies on local government investment and fiscal expenditures. To be sure, the above coefficient symbols and significance need to be carefully re-checked after introducing instrumental variables and overcoming the endogeneity. 4

CONCLUSION

While considering the characteristics of geographical limitations of China’s innovation spatial agglomeration and knowledge spillovers, we focus on the impact that innovation agglomeration and local knowledge spillovers has on China’s economic space disparities. This paper has applied cross-year provincial panel data of China, and investigated the impact of innovation

agglomeration and knowledge spillover effect from the two aspects of industrial agglomeration and economic growth. The results can be summarized as: firstly, during the year 1990∼2010, the significant characteristics of innovation agglomeration existed in 31 provinces and cities of China, though agglomeration disparities between regions are more obvious, and show the “Matthew Effect” cycle varying in eastern, central and western provinces; knowledge spillover effect show obvious locality characteristics, there are strong knowledge spillovers between adjacent provinces. Secondly, after using instrumental variables to control the endogeneity, innovation agglomeration and local knowledge spillovers have shown a significant and steady effect on promoting the spatial concentration of industry and regional economic growth, which means the higher the degree of innovation agglomeration is, the stronger the knowledge spillover effect is, and the higher the degree of regional industrial agglomeration will be, the faster the speed of economic development will be. However, the geographical agglomeration gaps and the distancedecay of knowledge spillover effect also imply a tendency of the increase in spatial economic disparities. In addition, geographic locations of innovation, government spending, foreign direct investment, regional trade freedom and market competition environment are the important influencing factors for spatial economic structure and regional economic growth too.

REFERENCES [1] Duranton, G. & Puga, D. (2003). Microfoundations of Urban Agglomeration Economies: C.E.P.R. Discussion Papers. [2] Zvi, G. (1979). Issues in Assessing the Contribution of Research and Development to Productivity Growth. Bell Journal of Economics, 10(1), 92–116. [3] Kesidou, E. & Romijn, H. (2008). Do Local Knowledge Spillovers Matter for Development? An Empirical Study of Uruguay’s Software Cluster. World Development, 36(10), 2004–2028. [4] Barro, R.J. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, 5(1), 5–32. doi: 10.1023/a:1009850119329.

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Editor Sheng

MECHATRONICS ENGINEERING AND ELECTRICAL ENGINEERING

an informa business

Mechatronics Engineering and Electrical Engineering

The 2014 International Conference on Mechatronics Engineering and Electrical Engineering (CMEEE2014) was held October 18-19, 2014 in Sanya, Hainan, China. CMEEE2014 provided a valuable opportunity for researchers, scholars and scientists to exchange their new ideas and application experiences face to face together, to establish business or research relations and to find global partners for future collaboration. The papers in this book are selected from more than 500 papers submitted to the 2014 International Conference on Mechatronics Engineering and Electrical Engineering (CMEEE2014). The book is divided into 4 sections, covering the topics of Mechatronics, Electrical Engineering, Control and Automation and Other Engineering. The conference will promote the development of Mechatronics Engineering and Electrical Engineering, strengthening international academic cooperation and communications.

Editor Ai Sheng