Pankaj_thesis_maximum Power Point Tracking And Power Smoothing In Wind Energy Conversion System Using Fuzzy Logic Pitch Controller

Maximum power point tracking and power smoothing in wind Energy conversion system using fuzzy logic pitch controller A Thesis submitted in partial fulfillment of the requirement for the award of the degree of MASTER OF TECHNOLOGY in POWER ELECTRONICS & ASIC DESIGN by

Pankaj Shukla (Reg. No. 2008PE19)

Under the Guidance of Dr. S.R.Mohanty Asst. Professor, EED

DEPARTMENT OF ELECTRICAL ENGINEERING MOTILAL NEHRU NATIONAL INSTITUTE OF TECHNOLOGY (DEEMED UNIVERSITY) ALLAHABAD-211004 JUNE 2010 MOTILAL NEHRU NATIONAL INSTITUTE OF TECHNOLOGY

ALLAHABAD

CERTIFICATE This is to certify that the thesis entitled, “Maximum power point tracking and power smoothing in wind Energy conversion system using fuzzy logic pitch controller” submitted by Mr. Pankaj Shukla in partial fulfillment of the requirement of the award of the degree of Master of Technology in Electrical Engineering with specialization in Power Electronics & ASIC Design to the Motilal Nehru National Institute of Technology, Allahabad (Deemed University) during the academic year 2009-10. The results embodied in this thesis have not been submitted for the award of any other degree. We approve his submission for the above mentioned degree.

Date: June 2010 Place: Allahabad (U.P.)

(Dr. S.R.Mohanty) Assistant Professor, EED

CANDIDATES’S DECLARATION

I, Pankaj Shukla hereby submit the thesis, as approved by the thesis supervisors Assistant Professor, Dr.S.R.Mohanty, Assistant Professor, Electrical Engineering Department, MNNIT, Allahabad. I hereby declare that the work presented in this thesis is an authentic work carried out by me during July 2009-June- 2010. I have read and understand the Institute’s rule relating to the thesis, inventions, innovations and other work and agree to be bound by them. I also declare that, to the best of my knowledge and belief, this work has not been submitted earlier for the award of any other degree or thesis.

June, 2010 Allahabad

(Pankaj shukla) Reg.No.2008PE19

Dedicated To My parents

ACKNOWLEDGEMENTS I would like to express my sincere thanks and deepest to my honorable Thesis Supervisors Dr.S.R.Mohanthy, Assistant Professor, Department of Electrical Engineering, Motilal Nehru National Institute of Technology, Allahabad, for their invaluable guidance, motivation, support, advice and supervision during the entire period of this thesis. Their meticulous guidance, constructive and valuable suggestions, timely discussions and clarifications of my doubts increased my cognitive awareness and helped me for making a deeper analysis of the subject under study. I also express my sincere thanks to my Head of the Department, Prof. Dinesh Chandra, for his invaluable support and encouragement throughout the thesis. I also express my heartfelt gratitude to the Department of Electrical Engineering MNNIT Allahabad for giving us this opportunity, which has enriched our knowledge and experience immensely. Lastly, I wish to express thanks to my parents, family members and friends for their patient encouragement and cooperation, which has gone along way in making this report a success.

(Pankaj Shukla) Reg. No. 2008PE19

ABSTRACT In recent years, there has been a growing interest in wind energy as it is a potential source for electricity generation with minimal environmental impact. With the advancement of aerodynamic designs, wind turbines, which can capture hundreds of kilowatts of power, are readily available. When such wind energy conversion systems (WECS) are integrated to the grid, they produce a substantial amount of power, which can supplement the base power generated by thermal, nuclear, or hydropower plants. The purpose of this work is to develop a maximum power tracking control strategy for variable speed wind turbine systems. In this thesis, four different methods of tracking the peak power in a wind energy conversion system (WECS) is discussed. The algorithms search for the peak power by varying the speed in the desired direction. The generator is operated in the speed control mode with the speed reference being dynamically modified in accordance with the magnitude change of active power. The peak power points in the P–ω curve correspond to dP/d ω =0. This fact is made use of in the optimum point search algorithm. The generator considered is a wound rotor induction machine whose stator is connected directly to the grid and the rotor is fed through back-to-back pulse-width-modulation (PWM) converters. Pitch angle control is the most common means for adjusting the power output of the wind turbine when wind speed is above rated speed and various controlling variables may be chosen, such as wind speed, generator speed and generator power. As conventional pitch control usually use PI controller, the mathematical model of the system should be well known. A fuzzy logic pitch angle controller is developed in this thesis, in which it does not need well known about the system. The design of the fuzzy logic controller and the comparisons with conventional pitch angle control strategies with various controlling variables are carried out. The simulation results show that the fuzzy logic controller can achieve better control performances than other three methods of maximum power point control strategies. The output power of WECS is also effectively smoothened using the proposed method.

CHAPTER-1

INTRODUCTION

1.1 INTRODUCTION

Wind energy is one of the most available and exploitable forms of renewable energy. Wind blows from a region of higher atmospheric pressure to one of the lower atmospheric pressure. The difference in pressure is caused by: (A) The fact that earth’s surface is not uniformly heated by the sun and (B) The earth’s rotation. The global electrical energy is rising and there is a steady rise of the demand on power generation, transmission, distribution and utilization. The maximum extractable energy from the 0-100m layer of air has been estimated to be the order of 1012 KWh/annum, which is of the same order as hydroelectric potential. Wind Energy, energy contained in the force of the winds blowing across the earth’s surface.When harnessed, wind energy can be converted into mechanical energy for performing work such as pumping water, grinding grain, and milling lumber. By connecting a spinning rotor (an assembly of blades attached to a hub) to an electric generator,

modern wind turbines convert wind energy, which turns the rotor, into

electrical energy [1]. Since earliest recorded history, wind power has been used to move ships, grind grain and pump water. This is the evidence that wind energy was used to propel boats along the Nile River as early 5000 B.C. within several centuries before Christ; simple windmills were used in china to pump water. In the United States, millions of windmills were erected as the American West was developed during the late 19th century. Most of them were used to pump water for farms and ranches. By 1900, small electric wind systems were developed to generate current, but most of these units fail into disuse as inexpensive grid power was extended to rural areas during the 1930s. By 1910, wind turbine generators were producing electricity in many European countries.

Wind turbines are available in a variety of size, and therefore power ratings. The largest machine, such as the one built in Hawaii, has propellers that span the more than the length of a football field and stands 20 building stories high, and produces enough electricity to power 1400 homes. A small home-sized wind machine has rotors between 8 and 25 feet in diameter and stands upwards of 30 feet and can supply the power needs of an all-electric home or small business. All electric-generating wind turbines, no matter what size, are comprised of a few basic components: the (the part that actually rotates in the wind), the electrical generator, a speed control system, and a tower. Some wind machine have fail- safe shutdown system so that if part of the machine fails, the shutdown system turn the blades out of the wind or puts brakes.

In Fig.1.1, the data showing the present situation of installed units in different countries of the world. It shows that the maximum units is installed in U.S.A (31.62%), then in China (23.83%) and then in India (6.57%) [44].

REST OF THE WORLD, 14.88

PORTUGAL, 3.29

USA, 31.62

FRANCE, 3.59 ITALY, 0.82 SPAIN, 0.9

GERMANY, 6.3 INDIA, 6.57 CHINA, 23.83

Fig.1.1.Installed units of wind power in different countries in percentage. 1.1.1- Benefits of wind power A wind energy system can provide a cushion against electric power price increases. If you live in a remote location, a small wind energy system could help you avoid the high

costs of having utility power lines extended to your site. Although wind energy system involves a significant initial investment, they can be competitive with conventional energy sources when you account for a lifetime of reduced or altogether avoided utility costs. The length of the payback period – the time before the savings resulting from your system equal the cost of the system itself- depends on the system you choose the wind resource on your site, electricity costs in your area, and how you use your wind system. Wind energy is the world's fastest-growing energy source and will power industry, businesses and homes with clean, renewable electricity for many years to come. In India, wind power plants have been installed in Gujarat, Orissa, Maharashtra and Tamil Nadu, where wind blows at speed of 30 km/h during summer. On the whole, the wind power potential of India has been estimated to be around 20,000 MW [2]. Small wind energy systems can be used in connection with grid-connected systems, or in stand-alone application that are not connected to the utility grid. A grid-connected wind turbine can reduce consumption of utility-supplied electricity for lighting, appliances, and electric heat. If the turbine cannot deliver the amount of energy you need, the utility makes up the difference. When the wind system produces more electricity than the household requires, the excess can be returned to the grid. With the interconnection available today, switching takes place automatically. Stand-alone wind energy systems can be appropriate for homes, farms, or even entire communities that are far from the nearest utility lines. Either type of the system can be practical if the following condition exist Some few requirements of wind generation system [4] 1. Wind generation is dependent on the quality and quantity of the wind hitting the blades. The better the wind you have the more power you will generate. 2. The power available in wind increases by the cube of the wind speed – if wind speed doubles, power output increases by eight. 3. Turbulent wind (from obstruction, geographical features, etc.) will reduce the power output as the turbine swings back and forth hunting for the wind. 4. These are the few requirements of site for wind generation system: 5. The higher a turbine, the more power is generated, the better quality the wind.

6. A wind turbine should be at least 40 ft above any object within a 400 ft radius. Note there is often exception to this rule depending on your site. 7. It is often more economical to install a higher tower than purchasing a larger turbine. 8. Space: generally locations with an acre or more will be suitable. A guyed tower requires ½ the height of the tower as a radius at a minimum for location of anchor points. Space is also required for ground assembly and erection of the tower. Lattice towers require less surface area, but are more complex and expensive to install. Wind energy has been the subject of much recent research and development. In order to overcome the problems associated with fixed speed wind turbine system and to maximize the Wind energy capture, many new wind farms will employ variable speed wind turbine. DFIG is one of the components of Variable speed wind turbine system. DFIG offers several advantages when compared with fixed speed generators including speed control. These merits are primarily achieved via control of the rotor side converter. Many works have been proposed for studying the behavior of DFIG based wind turbine system connected to the grid. Most existing models widely use vector control Double Fed Induction Generator. The stator is directly connected to the grid and the rotor is fed to magnetize the machine [3]. Large wind farms have been installed or planned around the world and the power ratings of the wind turbines are increasing. Wind energy generation equipment is most often installed in remote, rural areas. These remote areas usually have weak grids, often with voltage unbalances and under voltage conditions. [5]. Many places also do not have the potential for generating hydel power. Nuclear power generation was once treated with great optimism, but with the knowledge of the environmental hazard with the possible leakage from nuclear power plants, most countries have decided not to install them anymore [42]. It is, however, only since the 1980s that the technology has become sufficiently mature to produce electricity efficiently and reliably from the wind. Over the last two decades, a variety of wind energy systems have been developed. Power extracted from wind energy contributes a

significant proportion of consumers’ electrical power demands. In recent years, many power converter techniques have been developed for integrating with electrical grid [2]. The first wind turbines were probably simple vertical-axis, such as those used in Persia as early as about 200 B.C for grinding grain. The use of these vertical-axis mills subsequently spread throughout the Islamic world Later, horizontal-axis windmills, consisting of up to ten booms, rigged with jibs sails, were developed. In middle ages, by the eleventh century A.D, windmills were in extensive use in the Middle East and were introduced to Europe in the thirteen century by returning crusaders. By the fourteen century the Dutch had taken the lead in improving the design of windmills, and used them extensively thereafter for draining the marshes and lakes of the Rhine River delta. Between 1608 and 1612, Beemster Polder, wetland area which was about 10 feet below sea level, was drained by 26 windmills of unto 50 hours power (hp) each, operating in two stages .The first oil mill was built in Holland in 1582 and paper mill 1586. By the middle of the nineteenth century, some 9000 windmills were being used in the Netherlands [1, 3]. By 1960, fewer than 1000 were still in working condition due to introduction of steam engine. The Dutch introducing many improvements in the design of windmills and particular, the rotors, large industrial mills could deliver up to 90 hp in high winds. Industrialization, first in Europe and later in America, led to a gradual decline in the use of windmills. The steam engine replaced European water-pumping windmills. In the 1930s, the Rural Electrification Administration's programs brought inexpensive electric power to most rural areas in the United States [1, 3]. Since the end of the 19th century the wind power used to generate electricity. In 1888, Charles F. Brush built the first automatically operating wind turbine of 12 KW with a rotor diameter of 17 meter and 144 rotor blades made of cedar wood for electricity generation. The Danish Poul la Cour (1846-1908), another pioneer of electricity generating wind turbines, discovered fast rotating wind turbines with few rotor blades in 1897 in Askov (Denmark). It was more efficient for electricity production than the slow moving ones.

Modern wind turbine technology has been accomplished with the help of many areas, such as material science, computer science, aerodynamics, analytical methods, testing, and power electronics. Without the help of these areas the rapid development of new technologies would not be possible. A relatively new area for wind turbines is power electronics based variable speed drives.

Power

electronic

systems

allow

synchronization between the wind turbine system and the utility grid and operate the wind turbine at variable speeds, increasing the energy production of the system. In addition, power electronics provide a means to transfer energy to and from storage units, which can allow the storage of excess energy generation for later use[2]. It is important to find an alternative form of energy before the world’s fossil fuels are depleted. It is predicted that oil and gas reserves will be depleted by 2032. Due to the combustion of fossil fuels, carbon dioxide is released into the atmosphere causing the atmosphere to trap solar radiation that then leads to global warming or the “green house effect”.

1.2 LITERATURE REVIEW A lot of research work has been carried out in the area of wind power technologies in power systems which led to the development of different methodologies and approaches. Both grids connected and stand-alone operation is feasible. A lot of research work has been reported in the area of wind energy conversion systems, Since wind availability is sporadic and unpredictable. A brief literature review of these methodologies and approaches is present below.

J.G. Slootweg et al. [41] presented dynamic model (d-q frame) of wind turbine concept namely a doubly fed (wound rotor) induction generator with a voltage source converter feeding the rotor. Thus wind turbine concept is equipped with rotor speed, pitch angle and terminal voltage controllers. The wind turbine response is simulated in this paper. In [1], the authors focused on future concepts to increase the penetration of wind

power in power system, where Offers broad coverage ranging from basic network interconnection issues to industry deregulation and future concepts for wind turbines and power system. Discusses wind turbine technology, industry standards and regulations along with power quality issue. [1] presents models for simulating wind turbines in power system. The [2, 4] are added with almost all existing machines, but they introduced new control concepts on different motors and its drives. Introduced matlab/simulink model for The doubly fed (wound rotor) induction generator control through a rotor connected bidirectional a.c., d.c., a.c. PWM converter that is used for pump storage hydro and wind energy conversion today.All the renewable sources, non renewable sources and other energy sources are discussed in [3]. F. Mei and B. Pal [5] investigated the modal analysis of a grid connected doubly fed induction generator (DFIG). The change in modal properties for different system parameters, operating points, and grid strengths are computed and observed. The results offer a better understanding of the DFIG intrinsic dynamics, which can also be useful for control design and model justification. L. Szabo et al. [1] presented simulation tool for induction generators. In this paper, a mathematical model of doubly-fed induction generator was built to control active and reactive power in wind power plants. In this model park’s transformation is used where three phased stator and rotor symmetrical windings are transformed in orthogonal axis systems to improve power quality, high energy efficiency and controllability.

The wind farm power collection system, grounding of wind farms against power system faults and transient over voltages and Wind turbine lightning protection systems are discussed in [5]. The Embedded wind generation, Electrical distribution networks and the impact of dispersed generation, the per-unit system, power flows and voltages in simple radial distribution networks, connection of embedded wind generation, power system studies, Power (voltage) quality. Voltage flicker, harmonics from variable speed wind turbines, measurement and assessment of power quality of grid connected wind turbines also be mention[5].[6]This book devoted to wind power and solar photovoltaic technologies, their engineering fundamentals, conversion characteristics, operational

considerations to maximize output, and emerging trends also includes

new and

specialized technologies and explore the large-scale energy storage technologies, overall electrical system performance[6]. A.Tapia et al. [33] described the modeling of the machine considers operating conditions below and above synchronous speed, which are actually achieved by means of a doublesided PWM converter joining the machine rotor to the grid. In order to decouple the active and reactive powers generated by the machine, stator-flux-oriented vector control is applied. The wind generator mathematical model developed in this paper is used to show how such a control strategy offers the possibility of controlling the power factor of the energy to be generated. In [6], a new control scheme implemented for the variable speed grid connected wind energy generation system, that helps a induction generator driven by an emulated wind turbine with two back to back voltages fed PWM inverters to interface the generator and grid. The machine currents are controlled using an indirect vector control technique [6]. The generator torque is controlled to drive the machine to the speed for maximum wind turbine aerodynamic efficiency [6]. In order to implement the separated positive and negative sequence controllers of DFIG, two methods to separate positive and negative sequence in real time are compared [7]. The features of each generator– converter configuration are considered in the context of wind turbine systems [8]. H. Karimi-Davijani et al. [34] presented fuzzy logic control of Doubly Fed Induction Generator (DFIG) wind turbine in a sample power system.. Fuzzy logic controller is applied to rotor side converter for active power control and voltage regulation of wind turbine. Wei Qiao et al. [35] presented an approach to use the particle swarm optimization algorithm to design the optimal PI controllers for the rotor-side converter of the DFIG. A new time-domain fitness function is defined to measure the performance of the controllers. Simulation results show that the proposed design approach is efficient to find the optimal parameters of the PI controllers and therefore improves the transient performance of the WTGS over a wide range of operating conditions. Rohin M. Hilloowala, and Adel M. [35] presented a rule-based fuzzy logic controller to control the output power of a pulse width modulated (PWM) inverter used in a standalone wind energy conversion scheme (SAWECS).

C. A. M. Amendola, and D. P. Gonzaga [36] presented the energy capture control is made applying a fuzzy-logic controller directly on the turbine pitch-angle and the speed control is made by a field-oriented fuzzy-logic controller, that acts on DFIG electromotive torque so that to follow the reference value generated by an optimum angular speed estimator. Yongchang and Z. Zhengming [38] presented a conventional PI controller, sliding mode controller (SMC) and fuzzy logic controller (FLC) for rotor field oriented controlled (RFOC) induction motor drives are studied comparatively. PI is simple but sensitive to parameter variations. SMC provides strong robustness to parameters variations, disturbance rejection and system order reduction. FLC does not need exact system mathematical model and can handle intricate nonlinearity, but its implementation is more complicated than that of PI and SMC. Comparative study of PI, SMC and FLC are carried out from four aspects: dynamic performance and steady-state accuracy, parameter robustness, and complexity of implementation. In the [10, 11] developed a 30kW electrical power conversion system for a variable speed wind turbine system.. As the voltage and frequency of generator output vary along the wind speed change, a dc-dc boosting chopper is utilized to maintain constant dc link voltage. The input dc current was regulated to follow the optimized current reference for maximum power point operation of turbine system. Line side PWM inverter supply currents into the utility line by regulating the dc link voltage. The active power was controlled by q-axis current whereas the reactive power can be controlled by d-axis current. The phase angle of utility voltage was detected using software PLL (Phased Locked Loop) in d-q synchronous reference frame[9, 10] .Proposed scheme gives a low cost and high quality power conversion solution for variable speed WECS. A switch-by-switch representation of the PWM converters with a carrier-based Sinusoidal PWM modulation for both rotor- and stator-side converters has been proposed. Stator-Flux Oriented vector control approach is deployed for both stator- and rotor-side converters to provide independent control of active and reactive power and keep the DC-link voltage constant [12]. In order to set synchronous vector controllers, decoupled design based on Internal Model Control approach is applied, where dynamics of the PWM converters is taken into account [12, 14].

After controlling method for the power of variable speed DFIG a method of tracking the peak power proposed which is independent of turbine parameters and air density is proposed. The algorithm searches for peak power points by varying the speed in desired direction. The generated is operated in speed control mode with the speed reference being dynamically modified in accordance with the magnitude and direction of change of active power [14, 15, 16]. But this method is rotor speed dependent, then a method proposed that doesn’t depend on wind generator speed and rotor speed ratings nor the dc/dc power converter rating [17, 19]. The two methods utilize the turbine characteristics (torque,

power and power

coefficient curves) to determine the

operating point that results in maximum power capture [20, 22]. The only difference between the two methods presented is that one requires an anemometer so that the wind speed is physically measured while and the second method calculates the wind speed using electrical parameters [ 22].

These methods are advantageous for fast optimum point determination and easy implementation since all the physical characteristics of the turbine are programmed directly and optimum operation point is determined by simply examining the characteristics. A disadvantage of these strategies however, is that they are customized for a particular turbine.. Another drawback of this algorithm is that it cannot take into account the atmospheric changes in air density, since for all its calculations, it assumes a certain value. But for overall efficiency improvement and to reduce the cost PWM converters were used with reduced switch count power converters [23].

In [41] The complete system is modeled and simulated in the MATLAB Simulink environment in such a way that it can be suited for modeling of all types of induction generator configurations. The model makes use of rotor reference frame using dynamic vector approach.

1.3 OBJECTIVE AND OGANIZATION OF THESIS Objective The objective is to develop a model and control methodology for a Doubly Fed Induction Generator and maximum power point tracking for this model that can be achieved. Thesis Outline Chapter -2 deals with the types of wind energy conversion system and configuration. This chapter also deals with wind energy back ground and wind turbine characteristics. Chapter-3 deals with induction machine with basic dynamic d-q model, axes transformation and also describe dc drive analogy and vector control of induction machine in brief. Chapter-4 deals with modeling

of

wind turbine, pitch angle control, rotor

side

controller, grid side controller of DFIG and also deals with detail modeling of wind turbine coupled with DFIG. Chapter-5 deals with DFIG under Maximum Power Point Tracking (MPPT) and power smoothing using fuzzy pitch controller. Chapter-6 deals with simulation model and parameter initializations. Chapter-7 deals with simulation results and discussion between different results. Chapter-8 deals with conclusion and future work related to DFIG.

CHAPTER-2 WIND

TYPES AND CONFIGURATIONS OF ENERGY CONVERSION SYSTEMS

In this chapter various types and configurations of wind energy conversion systems are discussed i.e. the fixed speed wind energy conversion systems

and

variable-speed wind energy conversion systems. Also wind turbine characteristic which are specific to each turbine and depends on the aerodynamic design of the turbine and the site location of wind power plant are discussed .But in this thesis only variable speed wind turbines will be considered [26]. 2.1 General A special type of induction generator, called a doubly fed induction generator (DFIG), is used extensively for high-power wind applications. DFIG’s ability to control rotor currents allows for reactive power control and variable speed operation, so it can operate at maximum efficiency over a wide range of wind speeds. The Doubly-Fed Induction Generator (DFIG) is widely used for variable-speed generation, and it is one of the most important generators for Wind Energy Conversion Systems (WECS). Both grid connected and stand-alone operation is feasible. For variable speed operation, the standard power electronics interface consists of a rotor and stator side PWM inverters that are connected back-to-back. These inverters are rated, for restricted speed range operation, to a fraction of the machine rated power. Applying vector control techniques yields current control with high dynamic response. In grid-connected applications, the DFIG may be installed in remote, rural areas where weak grids with unbalanced voltages are not uncommon. As reported in induction machines are particularly sensitive to unbalanced operation since localized heating can occur in the stator and the lifetime of

the machine can be severely affected. Furthermore, negative-sequence currents in the machine produce pulsations in the electrical torque, increasing the acoustic noise and reducing the life span of the gearbox, blade assembly and other components of a typical WECS. To protect the machine, in some applications, DFIGs are disconnected from the grid when the phase-to-phase voltage unbalance is above 6%.

Controller design parameters for the operation of induction generators in unbalanced grids have been reported in, where it is proposed to inject compensating current in the DFIG rotor to eliminate or reduce torque pulsations. The main disadvantage of this method is that the stator current unbalance is not eliminated. Therefore, even when the torque pulsations are reduced, the induction machine power output is rerated, because the machine current limit is reached by only one of the stator phase. Compensation of unbalanced voltages and currents in power systems are addressed in where a STATCOM is used to compensate voltage unbalances. However, the application of the control method to DFIGs is not discussed. No formal methodology for the design of the control systems is presented and only simulation results are discussed in. In this thesis, a controller design is specified, which compensates the stator current unbalance in grid-connected and stand-alone DFIG operation. The strategy uses two revolving axes theory (rotating synchronously at ± to obtain the d– q components of the negative and positive-sequence currents in the stator and grid/load. The unbalance is compensated by the rotor side converter. The positive-sequence current is conventionally controlled to regulate the dc link voltage, whereas negative-sequence current is regulated to reduce or eliminate the grid voltage unbalance. 2.2 Type of Wind turbines Wind turbine converts mechanical energy into generator torque and the generator converts this torque into electricity and feeds it into the grid as other generation processes does. The only difference from other generation processes is that the mechanical energy is from wind. There are currently three main types of wind turbines available as shown in Fig.2.1.[20]

Gear Box

IG

Grid

(a)Fixed speed wind turbine with an induction generator

Gear Box

Grid

IG

GSC

RSC

(b)Variable-speed wind turbine with a doubly-fed induction generator

Gear Box

PM

RSC

GSC

Grid

converters Blades (c)Variable-speed wind turbine with a permanent magnet synchronous generator

Fig. 2.1 General structures of three different types of wind turbines

Fig.2.1 shows the structures of three different types of wind turbines in Fig.2.1 (a), (b) and (c) shows as: (a) Fixed speed wind turbine with an asynchronous squirrel cage induction generator (IG) directly connected to the grid via a transformer. (b) Variable speed wind turbine with a doubly fed induction generator (DFIG) and blade pitch control. (c) Variable speed wind turbine using a permanent magnet synchronous generator that is connected to the grid through a full-scale frequency converter. This is called direct drive (DD) wind turbine.

However, indirect grid connected wind turbines still need many improvements to compete with other conventional electricity generation technologies. Firstly, as Fig. 2.1 shows, the indirect grid connected wind turbines will need a rectifier and two inverters, one to control the stator current, and another to generate the output current, but it may change as the cost of power electronics decreases. Secondly, there are energy losses associated with AC/DC/AC conversion process, and harmonic distortions of the alternating current may be introduced in the electrical grid by power electronics devices, thus reducing power quality.

To improve the performance of wind turbines, different technologies are being applied to them. Now two types of indirect grid connected wind turbines dominate the market. The DD type of wind turbines is mainly built by Enercon (Germany). This type of wind turbines is combined with synchronous permanent magnet generator and AC/DC/AC converter with a rating of 100% of the rated wind turbine power. Since it does not need the gear box, the weight at the hub height can be lowered a lot, and the operation and maintenance of the gear box are not needed. But because the capacity of the converter has to match the maximum output power of the generator, its cost is highest among all types of wind turbines. Also the generator is bigger than other types of wind turbines. In the long term, the operation and maintenance costs of the gear box can be saved.

The other type of indirect grid connected wind turbine is a variable speed wind turbine with DFIG, which dominates the market with their total share to be around 84.5%-86%. The wind turbine with DFIG is combined with gear box, induction generator, and AC/DC/AC converter with a rating of only 20%–30% of the rated wind turbine power.. The cost of DFIG system is lower than the direct drive system because its power converter is approximately one-third the size of the direct drive system. But the control system of a DFIG is more complex than that of a DD.

2. 3 TYPES OF WIND ENERGY CONVERTION SYSTEMS Wind electric conversion systems can be broadly classified as; 

Constant speed constant frequency (CSCF);



Variable speed constant frequency (VSCF);



Variable speed variable frequency (VSVF);

2.3.1 Constant speed constant frequency (CSCF)

In the CSCF scheme, the rotor is held constant by continuously adjusting the blade pitch and/or generator characteristics. For synchronous generators, the requirement of constant speed is very rigid and only minor fluctuations of about 1% for short duration could be allowed [5].As the wind fluctuates, a control mechanism becomes necessary to vary the pitch of the rotor so that the power derived from the wind system is held fairly constant. Such a control system is necessary since wind power varies with the cube of wind velocity. During gusty periods, the machine is subjected to rapid changes in the input power. The control mechanism must be sensitive enough to damp out these transient so that the machine output does not become unstable. Such a mechanism is expensive and adds complexity to the system [21].

2.3.2 Variable speed constant frequency (VSCF)

The variable speed operation of wind electric system yields higher output for both low and high wind speeds. This results in higher annual energy per rated installed

capacity. Both horizontal and vertical axis wind turbines exhibit this gain under variable speed operation [17]. In this scheme, the need for a costly blade control mechanism is avoided. Generation schemes involving speed rotors are more complicated than constant speed systems. Variable frequency power must be converted to constant frequency power, and this can be done by using power electronics [21].

2.3.3 Variable speed variable frequency (VSVF)

General, resistive heating loads are less frequency sensitive. Synchronous generators can be affected at variable speed, corresponding to the changing drive speed. For this purpose, self-excited induction generator can be conveniently used. This scheme is gaining importance for standalone wind power applications [5, 18, 21]. 2.4 WIND GENERATORS

According to the turbine position the wind generators are divided into two axes that are horizontal axis and vertical axis generators.

2.4.1 Horizontal axis wind generators Horizontal axis wind generators have the main rotor shaft and electrical generator at the top of a tower, and must be pointed into the wind. Small generators are pointed by a simple wind vane or tail. Large generators often use a wind sensor coupled with a servomotor. Most large wind generators use a gearbox, which turns the slow rotation of the blades into a quicker rotation that is more suitable for generating electricity [4, 5]. 2.4.2 Vertical axis wind generators Vertical axis wind generators have the main rotor shaft running vertically. The advantages of this configuration are that the generator and/or gearbox can be placed at the bottom, near the ground, so the tower doesn't need to support the additional weight, and that the generator doesn't need to be pointed into the wind. They generally also operate at

lower wind speeds. However, they are not as efficient at extracting energy from the wind [4, 5]. 2.5 CHOICE OF GENERATORS

Basically, a wind turbine can be equipped with any type of 3 phase generator. Today, the demand for grid-compatible electric current can be met by connecting frequency converters, even if generator supplies AC of variable frequency or DC. Several general types of generators may be used in WT [4, 5, 21]. 1. Permanent magnet generators, 2. Caged rotor induction generators, 3. Synchronous generators, 4. Doubly fed induction generators. 2.5.1 Permanent magnet synchronous generators Permanent magnet excitation is generally favored in newer smaller scale turbine designs, since it allows for higher efficiency and smaller wind turbine blade diameter. While recent research has considered larger scale designs, the economics of large volumes of permanent magnet material has limited their practical application. The primary advantage of permanent magnet synchronous generators (PMSG) is that they do not require any external excitation current. A major cost benefit in using the PMSG is the fact that a diode bridge rectifier may be used at the generator terminals since no external excitation current is needed. Flexibility in design allows for smaller and lighter designs and higher output level may be achieved without the need to increase generator size. Lower maintenance cost and operating costs, bearings last longer, there is no significant losses generated in the rotor and the Generator speed can be regulated without the need for gears or gearbox .Very high torque can be achieved at low speeds and Eliminates the need for separate excitation or cooling systems[5]. But some disadvantages are there in PMSG that Higher initial cost due to high price of magnets used and Permanent magnet costs restricts production of such generators

for large scale grid connected turbine designs. High temperatures and sever overloading and short circuit conditions can demagnetize permanent magnets. Use of diode rectifier in initial stage of power conversion reduces the controllability of overall system [5, 17].

2.5.2 Induction generators The use of induction generators (IG) is advantageous since they are relatively inexpensive, robust and they require low maintenance. The nature of IG is unlike that of PMSG, Lower capital cost for construction of the generator and Known as rugged machines that have a very simple design. Higher availability especially for large scale grid connected designs and Excellent damping of torque pulsation caused by sudden wind gusts, relatively low contribution to system fault levels [5]. Disadvantages of this generator is Increased converter cost since converter must be rated at the full system power then Results in increased losses through converter due to large converter size needed for IG Generator requires reactive power and therefore increases cost of initial AC–DC conversion stage of converter and May experience a large in-rush current when first connected to the grid .it also increased control complexity due to increased number of switches in converter [5, 17]. 2.5.3 Synchronous generators The major advantages of synchronous generator is that its reactive power characteristics can be controlled, and therefore such machine can be used to supply reactive power to systems that require reactive power. The application of synchronous generators (SG) in wind power generation has also been researched. A brief description of one possible converter-control scheme is given for a small wind energy conversion system. The use of a diode rectifier along with a DC/DC boosts stage and inverter as a power electronic interface for grid connection. It possesses Minimum mechanical wear due to slow machine rotation. Due to direct drive applicable further reducing cost since gearbox not needed. it allow for reactive power control as they are self excited machines

that do not require reactive power injection and Readily accepted by electrically isolated systems for grid connection. It Allow for independent control of both real and reactive power [5]. Disadvantages are typically having higher maintenance costs again in comparison to that of an IG and magnet used which is necessary for synchronization is expensive. But magnet tends to become demagnetized while working in the powerful magnetic fields inside the generator. It requires synchronizing relay in order to properly synchronize with the grid [5]. 2.5.4 Doubly fed induction generators As the PMSG has received much attention in wind energy conversion, the doubly fed induction generator has received just as much consideration, if not more. If a wound rotor induction machine is used, it is possible to control the generator by accessing the rotor circuits. A significant advantage in using doubly fed induction generators (DFIG) is the ability to output more than its rated power without becoming overheated. It is able to transfer maximum power over a wide speed range in both sub- and super-synchronous modes. The DFIG along with induction generators are excellent for high power applications in the MW range. More importantly, converter power rating is reduced since it .is connected to the rotor, while the majority of the power flows through the stator [5].

Fig. 2.2: Typical wind generators.

2.6 MODELING OF DFIG SYSTEM 2.6.1 Blade modeling (wind modeling) An aerodynamic model of the wind turbines is a common part of the dynamic models of the electricity-producing wind turbines. The captured aerodynamic power is given by: 1

𝑃𝑀 = 2 𝜌𝑎𝑖𝑟 𝜗 2 𝐴𝐶𝑃 𝜆, 𝜃

(2.1)

where 𝑃𝑀 is the captured power from wind, 𝜌𝑎𝑖𝑟 is the air density, v is the wind speed, A is the swept area of the blade, 𝐶𝑃 ( 𝜆) is the power coefficient, λ is the ratio between blade tip speed and wind speed at hub height, θ is the pitch angle. 𝐶𝑃 (𝜆, 𝜃) can be obtained from wind turbine manufacturers.

2.6.2 Drive train modeling The mechanical construction of the wind turbines is simply modeled as a lumped-mass system with the lumped combined inertia constant of the turbine rotor and the generator rotor. The shaft dynamic equation is [15]:

2𝐽𝑇

2𝐽𝐺

𝑑𝜔 𝑇

𝑑𝜔𝐺

𝑑𝜃𝑇𝐺

𝑑𝑡 = 𝑇𝑇 − 𝐾𝑠 𝜃𝑇𝐺 − 𝐷𝑠 (𝜔 𝑇 − 𝜔𝐺 )

(2.2)

𝑑𝑡 = 𝐾𝑠 𝜃𝑇𝐺 − 𝑇𝐸 − 𝐷𝑠 (𝜔 𝑇 − 𝜔𝐺 )

(2.3)

𝑑𝑡 = 𝜔𝑜 (𝜔 𝑇 − 𝜔𝐺 )

(2.4)

where JT and JG are the inertia constant of the turbine rotor and the generator rotor, respectively, Ks and Ds are the shaft stiffness and damping constant respectively, θTG is the electrical twist angle of the shaft, ωo is the base value of angular speed, ωT and ωG are the angular speeds of shaft at the ends of turbine and generator, respectively, TT and TE are the mechanical and electrical torque, respectively. 2.6.3Generator modeling As mentioned earlier, there are three types of generators used in wind turbines: one is induction generator, the second one is doubly fed induction generator, and the other is permanent magnetic synchronous generator. 1) Induction generator (IG) The equivalent circuit of the induction generator is shown in Fig.2.3, and the electric and magnetic equations of the model are described by equations (2.5)-(2.10) [20].

Rs

Ls

Lr

Rr V+rdq

+

Lm Vsdq -

jws ψ+rdq

J(ωs-ωr)ψ+rdq

Fig.2.3 Equivalent circuit of the induction generator

Stator Voltage is given by: dΨds

𝑉𝑑𝑠 = 𝑖𝑑𝑠 𝑅𝑠 − 𝜔𝑠 Ψ𝑞𝑠 + 𝑉𝑞𝑠 = 𝑖𝑞𝑠 𝑅𝑠 + 𝜔𝑠 Ψ𝑑𝑠 +

dt dΨqs dt

(2.5) (2.6)

Rotor Voltage is given by: 𝑉𝑑𝑟 = 𝑖𝑑𝑟 𝑅𝑟 − 𝑠𝜔𝑠 Ψ𝑞𝑟 + 𝑉𝑞𝑟 = 𝑖𝑞𝑟 𝑅𝑠 + 𝑠𝜔𝑠 Ψ𝑑𝑟 +

dΨdr dt dΨqr dt

(2.7) (2.8)

Flux Linkage is given by: Ψ𝑑𝑠 = 𝐿𝑚 𝑖𝑑𝑟 − 𝐿𝑠𝑙 𝑖𝑑𝑠 Ψ𝑞𝑠 = 𝐿𝑚 𝑖𝑞𝑟 − 𝐿𝑠𝑙 𝑖𝑞𝑠 Ψ𝑑𝑟 = −𝐿𝑚 𝑖𝑑𝑠 − 𝐿𝑟𝑙 𝑖𝑑𝑟 Ψ𝑞𝑟 = 𝐿𝑚 𝑖𝑞𝑠 − 𝐿𝑟𝑙 𝑖𝑞𝑟

(2.9)

Electronicmagnetic Toque is: 𝑇𝑒𝑙 = Ψ𝑞𝑟 𝑖𝑑𝑟 − Ψ𝑑𝑟 𝑖𝑞𝑟

(2.10)

where vs, is and Ψs are stator voltage, current and flux respectively; vr, ir and Ψr are rotor voltage, current and flux respectively; ωs is the angular velocity of the chosen frame of reference; d and q represent d and q axis, respectively. Lm is the mutual inductance; Lsl and Lrl are the stator and rotor leakage inductances, respectively. 2) Doubly fed induction generator (DFIG)

Doubly fed induction generator is a modified version of IG where two rotor windings receive electrical excitation from external sources. As a result, the rotor equations are modified as presented in section D below. Rests of the equations are same as IG.

3) Permanent magnet synchronous generator

The generator for a direct drive wind turbine is different from the other types. It is a permanent magnet synchronous generator, and using Park’s transformation, can be expressed by the following equations [20] and [18]. 𝑉𝑑𝑠 = −𝑖𝑑𝑠 𝑅𝑠 − 𝜔𝑠 Ψ𝑞𝑠 + 𝑉𝑞𝑠 = −𝑖𝑞𝑠 𝑅𝑠 − 𝜔𝑠 Ψ𝑑𝑠 +

dΨds dt

dΨqs dt

(2.11)

Ψ𝑑𝑠 = 𝐿𝑚 + 𝐿𝑠𝑙 𝑖𝑑𝑠 + Ψ𝑚 Ψ𝑞𝑠 = 𝐿𝑚 + 𝐿𝑠𝑙 𝑖𝑞𝑠

(2.12)

where ωr is the mechanical angular velocity of the rotor at any instant, d and q represent d and q axis respectively, ψm is the flux produced by the permanent magnets. Electronicmagnetic Toque is given by:

𝑝 𝑇𝑒𝑙 = (3 2)( 2)(Ψ𝑑𝑠 𝑖𝑞𝑠 − Ψ𝑞𝑠 𝑖𝑑𝑠 )

(2.13)

Where p is the number of poles.

2.6.4 Converter modeling and control

With the assumption that the converters are lossless, the equations of converters are as follows: 1) DFIG converter The power at the rotor side (also called slip power) is given by: 𝑃𝑟 = 𝑣𝑑𝑟 𝑖𝑑𝑟 + 𝑣𝑞𝑟 𝑖𝑞𝑟 𝑄𝑟 = 𝑣𝑞𝑟 𝑖𝑑𝑟 − 𝑣𝑑𝑟 𝑖𝑞𝑟

(2.14)

And the power at the stator side is given by: 𝑃𝑠 = 𝑣𝑑𝑠 𝑖𝑑𝑠 + 𝑣𝑞𝑠 𝑖𝑞𝑠 𝑄𝑠 = 𝑣𝑞𝑠 𝑖𝑑𝑠 − 𝑣𝑑𝑠 𝑖𝑞𝑠

(2.15)

So the total output power is: 𝑃 = 𝑃𝑠 + 𝑃𝑟 = 𝑣𝑑𝑟 𝑖𝑑𝑟 + 𝑣𝑞𝑟 𝑖𝑞𝑟 + 𝑣𝑑𝑠 𝑖𝑑𝑠 + 𝑣𝑞𝑠 𝑖𝑞𝑠 𝑄 = 𝑄𝑠 + 𝑄𝑟 = 𝑣𝑞𝑟 𝑖𝑑𝑟 − 𝑣𝑑𝑟 𝑖𝑞𝑟 + 𝑣𝑞𝑠 𝑖𝑑𝑠 − 𝑣𝑑𝑠 𝑖𝑞𝑠

(2.16)

2) Frequency converter For a direct drive system, all the power produced by the generator goes from the stator and pass through the converter. 𝑃 = 𝑃𝑠 = 𝑣𝑑𝑠 𝑖𝑑𝑠 + 𝑣𝑞𝑠 𝑖𝑞𝑠 𝑄 = 𝑄𝑠 = 𝑣𝑞𝑠 𝑖𝑑𝑠 − 𝑣𝑑𝑠 𝑖𝑞𝑠

2.7 WIND ENERGY BACKGROUND

The amount of power captured from a wind turbine is specific to each turbine and is governed by [1]. Pt 

1 AC p vw3 2

(2.17) Where: Pt = the turbine power(W), ρ = the air density (kg/m), A= the swept turbine area (m^3), CP = the coefficient of performance vw = is the wind speed(m/s). The coefficient of performance of a wind turbine is influenced by the tip-speed to wind speed ratio or TSR given by TSR 

wr , vw

(2.18)

Where w is the turbine rotational speed and r is the turbine radius. A typical relationship, as shown in Fig. 2.4, indicates that there is one specific TSR at which the turbine is most efficient. In order to achieve maximum power, the TSR should be kept at the optimal operating point for all wind speeds. The turbine power output can be plotted versus the turbine rotational speed for different wind speeds, an example of which is shown in Fig. 2.4. The curves indicate that the maximum power point increases and decreases as wind speed rises and falls [4, 22, 23],

0.4 Cp 0.3 0.2 0.1 0.0 0

2

4

6 8 Tip Speed Ratio

10

12

Fig. 2.4: Typical coefficient of power curve

P (W)

V2 > V1 P2 max

P1 max (rad/s) Fig. 2.5: Turbine output power characteristic

CHAPTER -3 PRNCIPLE OF DOUBLY FED INDUCTION GENERATOR

3.1 INTRODUCTION Variable speed ac drives have been used in the past to perform relatively undemanding roles in application which preclude the use of dc motors, either because of the working environment limits. Because of the high cost efficient, fast switching frequency static inverter. The lower cost of ac motors has also been a decisive economic factor in multi motor systems. However as a result of the progress in the field of power electronics, the continuing trend is towards cheaper and more effective power converters, and a single motor ac drives complete favorably on a purely economic basis with a dc drives. Among the various ac drive systems, those which contain the cage induction motor have a particular cost advantage. The cage motor is simple and rugged and is one of the cheapest machines available at all power ratings. Owing to their excellent control capabilities, the variable speed drives incorporating ac motors and employing modern static converters and torque control can well complete with high performance four quadrant dc drives [27]. The induction motors were evolved from being a constant speed motors to a variable speed. In addition, the most famous method for controlling induction motor is by varying the stator voltage or frequency. To use this method, the ratio of the motor voltage and frequency should be approximately constant. With the invention of Field Orientated Control, the complex induction motor can be modeled as a DC motor by performing simple transformations. In a similar manner to a dc machine, in induction motor the armature winding is also on the rotor, while the field is generated by currents in the stator winding. However the rotor current is not directly derived from an external source but results from the emf induced in the winding as a result of the relative motion of the rotor conductors with respect to the stator field. In other words, the stator current is the source of both the magnetic field and armature current. In the most commonly used, squirrel cage motor, only the stator current can directly be controlled, since the rotor winding is not accessible. Optimal torque production condition are not inherent due to the absence of a fixed physical disposition between the stator and rotor fields, and the torque equation is non linear. In effect, independent and efficient control of the field and torque is not as

simple and straightforward as in the dc motor [27, 28]. The concept of the steady state torque control of an induction motor is extended to transient states of operation in the high performance, vector control ac drive system based on the field operation principle defines condition for decoupling the field control

from the torque control. A field oriented induction motor emulates a separately exited dc motor in two aspects [27].

I - Both the magnetic field and torque developed in the motor can be controlled independently. II - Optimal condition for the torque production, resulting in the maximum torque per unit

ampere, occurs in the motor both in steady state and in transient condition of operation. 3.2 DC MOTOR ANALOGY Ia

If Ia

If

Fig-3.1 DC motor analogy

Where torque (T)  Ia.If And where Ia represents the torque component and If the field. The orthogonal or perpendicular relationship between flux and mmf axes is independent of the speed of rotation and so the electromagnetic torque of the dc motor is proportional to the product of the field flux and armature current. Assuming negligible magnetic saturation, field flux is proportional to field current and is unaffected by armature current because of the orthogonal orientation of the stator and rotor field. Thus in a separately excited dc motor with constant value of field flux, torque is directly proportional to armature current [27, 28].

Fig. 3.2: Separately excited

The principle behind the field oriented control or the vector control is that the machine flux and torque are controlled independently, in a similar fashion to a separately excited DC machine. Instantaneous stator currents are transformed to a reference frame rotating at synchronous speed aligned with the rotor stator or air gap flux vectors, to produce a d-axis component current and a q-axis component current. (SRRF).In this work, SRRF is aligned with rotor mmf space vector, the stator current space vector is split into two decoupled components, one controls the flux and the other controls the torque respectively [27, 28].

3.3 INDUCTION MOTOR ANALOGY

An induction motor is said to be in vector control mode, if the decoupled components of the stator current space vector and he reference decoupled components defined by the vector controller in the SRRF match each other respectively. Alternatively, instead of matching the two phase currents (reference and actual) in the SRRF, the close match can also be made in the three phase currents (reference and actual) in the stationary reference frame. Hence in spite of induction machines non linear and highly interacting multivariable control structure [28].its control has becomes easy with the help of FOC. Therefore FOC technique operates the induction motor like a separately excitedly DC motor.

The transformation from the stationary reference frame to the rotating reference frame is done and controlled by with reference to specific flux vector (stator flux linkage,

rotor flux linkage) or magnetizing flux linkage). In general, there exits three possibilities for such selection and hence, three vector controls. They are stator flux oriented control, rotor flux oriented control and magnetizing flux oriented control. As the torque producing component in this type of control is controlled only after transformation is done and is not the main input reference, such control is known as indirect torque control. The most challenging and ultimately, the limiting feature of field orientation is the method whereby the flux angle is measured or estimated. Depending on the method of measurement, the vector control is sub divided into two sub categories: direct vector and indirect vector control. In direct vector control, the flux measurement is done by using flux sensing coils or the hall devices [27, 28]. FOC uses a d-q coordinates having the d-axis aligned with rotor flux vector that rotates at the stator frequency. The particular solution allows the flux and torque to be separately controlled by the stator current d-q components. The rotor flux is a flux of the daxis component stator current ids .The developed torque is controlled by the q – axis component of the stator current iqs. The decoupling between torque and flux is achieved only if the rotor flux position is accurately known. This can be done using direct flux sensors or by using a flux estimator [28].

3.3.1 Vector control techniques of induction motor

The synchronously rotating reference frame (SRRF) can be aligned with the stator flux or rotor flux or magnetizing flux (field flux) space vectors respectively. Accordingly, vector control is also known as stator flux oriented control or rotor flux oriented control or magnetizing flux oriented control. Generally in induction motors, the rotor flux oriented control is preferred. This is due to the fact that by aligning the SRRF with the rotor flux, the vector control structure becomes simpler and dynamic response of the drive is observed to be better than any other alignment of the SRRF. The vector control can be classified into (i) Direct vector control and (ii) indirect vector control [28].

Fig. 3.3: Vector controlled induction motor

In vector control the dynamic performance of the induction motor improves to a great extent. The squirrel cage induction motor behaves similar to a separately excited dc motor with control of field and torque being independent of each other. Therefore the drive exhibits quick starting response, fat reversal response and quick change over from one operating point to another. With proper choice of speed controller, the drive can be further improved in terms of performance indices such as starting time, reversal time, and dip in speed on load application, overshoot in speed on load removal, steady state speed error on load etc [27, 28].

3.3.2 DYNAMIC DQ MODEL R.H. Park in 1920's proposed a model for synchronous machine with respect to stationary reference frame. H.C. Stanley in 1930's proposed a model for induction machine with respect to stationary reference frame. Later G. Bryons proposed a transformation of both stator and rotor variables to a synchronously rotating reference frame that moves with the rotating magnetic field. Lastly Krause and Thomas proposed a model for induction machine with respect to stationary reference frame.

Transformation: - the stator winding axes as-bs-cs with voltage vas , vbs & vcs with respect to stationary reference frame, the voltages are referred as vds & vqs [29].

Fig. 3.4: Stationary frame a-b-c to dq.

Fig. 3.5: Stationary frame to synchronous rotating frame

3.3.2.1 Synchronously rotating reference frame-Dynamic model (Kron's equation)

The dynamic model of DFIG is derived from the two-phase synchronous reference frame in which the q-axis is 90° ahead of the d-axis with respect to the direction of rotation. The electrical model of DFIG in the synchronous reference frame, here the quantities on the rotor side have been referred to the stator side. The model is composed of two groups, i.e. the first one is the voltage equations and the other is the flux ones. The general model for wound rotor induction machine is similar to any fixed-speed induction generator [12]. The DFIG system consists of stator, rotor and turbine. So the model design according these, the followings parameters are used to modeling the DFIG:

3.3.2.2 Voltage equations Stator Voltage Equations:

Vqs  Pqs  ds  Rs iqs

(1)

Vds  Pqs  qs  Rs ids

(2)

Fig. 3.6: d-axes transform Rotor Voltage Equations:

Vqr  Pqr  (  r )dr  Rr iqr

(3)

Vdr  Pdr  (w  wr )qr  Rr idr

(4)

Fig. 3.7: q-axes transform 3.3.2.3 Power Equations:

Ps  3 / 2(Vdsids  Vqsiqs )

(5)

Qs  3 / 2(Vqsids  Vdsiqs )

(6)

3.3.2.4 Torque Equation:

e  

3 p (dsiqs  qsids ) 22

(7) 3.3.2.5 Flux Linkage Equations: Stator Flux Equations:

qs  ( Lls  Lm )iqs  Lm iqr

(8)

ds  ( Lls  Lm )ids  Lm idr

(9)

Rotor Flux Equations:

qr  ( Llr  Lm )iqr  Lm iqs

(10)

dr  ( Llr  Lm )idr  Lm ids

Then, the d-axis of reference frame to be along the stator flux linkage (stator flux oriented control) will be

eqs  0

(11)

And hence from stator flux equation:

iqse  

Lm iqre Lls  Lm

(12)

Substituting for i qse in torque equation will result in:

e  

3 p Lm edsiqre 2 2 Lls  Lm

(13)

For eds to remain unchanged at zero, pds must be zero. Substituting for peds using e

stator voltage equation we get e Vdse  rs ids

(14) e

Neglecting stator resistance will lead to Vds simplified as

 0 ; substituting this, the power equation

3 se  (Vqse iqse ) 2 3 Qse  (Vqse idse ) 2 (15)

Therefore, the above equations show that active and reactive powers of the stator can be controlled independently. In terms of rotor current component

se  (Vme

Lm e iqr ) Ls (16)

Lm idre  s Q  (V ) Ls e s

e m

Where Ls = Lls  Lm

CHAPTER-4

CONTROLLER FOR DOUBLY FED INDUCTION GENERATOR

4.1 DFIG WITH BACK TO BACK CONVERTER A double fed induction generator is a standard, wound rotor induction machine

with its stator windings

is directly connected to grid and its rotor windings is

connected to the grid through an AC/DC/AC converter. AC/DC converter connected to rotor winding is called rotor side converter and another DC/AC is grid side converter. Doubly fed induction generator (DFIG) ability to control rotor currents allows for reactive power control and variable speed operation, so it can operate at maximum efficiency over a wide range of wind speeds [43].

DOUBLY FED INDUCTION GENERATOR

GEAR BOX

TRANSFORME R

3~ GRI DDD D

AC

DC DC

AC

Fig. 4.1: Wind Energy System.

In modern DFIG designs, the frequency converter is built by self-commutated PWM converters, a machine-side converter, with an intermediate DC voltage link. Variable speed operation is obtained by injecting a variable voltage into the rotor at slip frequency. By controlling the converters, the DFIG characteristics can be adjusted so as to achieve maximum of effective power conversion or capturing capability for a wind turbine and to control its power generation with less fluctuation. The DFIG is a WRIG with the stator windings connected directly to the three phases, constant-frequency grid and the rotor windings connected to a back-to-back voltage source converter. Thus, the term “doubly-fed” comes from the fact that the stator voltage is applied from the grid and the rotor voltage is impressed by the power converter [5, 41]. Vector control of a doubly fed induction generator drive for variable speed wind power generation is described. The control scheme uses stator flux-oriented

control for the rotor side converter bridge control and grid voltage vector control for the grid side converter bridge. The purpose of the grid side converter is to maintain the dc link voltage constant. It has control over the active and reactive power transfer between the rotor and the grid, while the rotor side converter is responsible for control of the flux, and thus, the stator active and reactive powers. A complete simulation model is developed for the control of the active and reactive powers of the doubly fed generator under variable speed operation [6, 9, 43].

Sign Vc,Vg

control

Pitch angle

Fig. 4.2: DFIG with converter control signal.

The wind turbine and the doubly-fed induction generator (DFIG) is shown in the Fig. 4.2. The AC/DC/AC converter is divided into two components: the rotor-side converter (Crotor) and the grid-side converter (Cgrid).A capacitor connected on the DC side acts as the DC voltage source. A coupling inductor L is used to connect C grid to the grid. The three-phase rotor winding is connected to Crotor by slip rings and brushes and the three-phase stator winding is directly connected to the grid. The power captured by the wind turbine is converted into electrical power by the induction generator and it is transmitted to the grid by the stator and the rotor windings. The control system generates the pitch angle command and the voltage command signals Vr and Vgc for Crotor and Cgrid respectively in order to control the power of the wind turbine, the DC bus voltage and the reactive power or the voltage at the grid terminals.[6, 9, 43]. 4.2 POWER-SPEED CHARACTERISTIC

From previous discussion it is clear that the controller, i.e Crotor and Cgrid have the capability of generating or absorbing reactive power and control the reactive power or the voltage at the grid terminals. The power is controlled is follow the power-speed characteristic (Fig. 4.3).

Fig. 4.3: Power-speed characteristic.

The above ABCD curve shows the power characteristics. The actual speed of the turbine ωr is measured and the corresponding mechanical power of the tracking characteristic is used as the reference power for the power control loop. The tracking characteristic is obtained over four points. From zero speed to speed of point A the reference power is zero. Between point A and point B the characteristic is a straight line, the speed of point B must be greater than the speed of point A. Between point B and point C the tracking characteristic is the locus of the maximum power of the turbine. The tracking characteristic is a straight line from point C and point D. The power at point D is 1 pu and the speed of the point D must be greater than the speed of point C. Beyond point D the reference power is a constant equal to 1 pu [43].

4.3 WIND-TURBINE MODEL. Wind turbines convert aerodynamic power into electrical energy. In a wind turbine two conversion processes take place. The aerodynamic power is first converted into mechanical power. Next, that mechanical power is converted into electrical power. Wind energy conversion systems are systems that generate electrical power from mechanical power derived from the wind. The major components of a typical wind energy conversion system include a wind turbine, a generator and control systems as shown in Fig. 4.2. Cp is the power coefficient which, in turn, is a function of tip speed ratio and blade angle θ .i.e.

Cp = Cp (λ, θ) and

λ= (ω*r)/ v; One common way to control the

active power of a wind turbine is by regulating the c p value of the rotor turbine. In the model, the c p value of the turbine rotor is approximated using a non-linier function [7, 15].

116 C P ( ,  )  0.22(  .4  5)e

i

12.5

r

Where  the tip is speed ratio and  is the pitch angle. The value 

(4.1) i

is given

according to the following relation. 1

i



1 .035  2   .08   1

(4.2)

The maximum value of c p can be found using a graphical method, this tip speed value is assigned as the optimum tip speed. Based on this value, the optimum turbine speed curve at any given wind speed can be obtained. This curve is then used as a reference in the active power control. The variation of Cp as a function of λ assuming constant pitch angle θ= const [43]. The out put power from turbine: Pt  Torque is Tm  Pt / r

1 AC p vw3 2

(4.3)

wind_speed_pu 3 -Ku(1)^3 Wind speed 1/wind_base wind_speed^3 (m/s)

Pwind_pu cp_pu

Avoid division by zero

Pm_pu -KProduct

pu->pu

Scope1 lambda

-K1 -Klambda_pu Generator speed (pu) pu->pu Product lambda_nom

lambda

cp

-K-

cp_pu 2 cp(lambda,beta) 1/cp_nom Pitch angle (deg) beta

Scope

-KAvoid division by zero Fig. 4.4: simulation model of turbine.

4.4 PITCH ANGLE CONTROL The pitch angle control is a common control method to regulate the aerodynamic power from the turbine. Pitch angle controller controls the wind flow around the wind turbine blade, thereby controlling the toque exerted on the turbine shaft. If the wind speed is less than the rated wind speed of the wind turbine, the pitch angle is kept constant at its optimum value. It should be noted that the pitch angle can change at a finite rate, which may be quite low due to the size of the rotor blades. Small change in pitch angle can have a dramatic effect on the power output. The maximum rate of change of the pitch angle is in the order of 3 to 10 degrees/second. In this controller a slight over-speeding of the rotor above its nominal value can be allowed without causing problems for the wind turbine structure. The relationship between the pitch angle and the wind speed is shown

1 Tm (pu)

in

Figure

4.6.[11,43].

Fig. 4.5: Pitch Angle control

Fig. 4.6: Relationship between Pitch Angle and Wind Speed The pitch angle controller employs a PI (proportional integral) controller as shown below. In Fig.4..5. When the wind turbine output power Pmeasured is lower than the rated power Pref of the wind turbine, the error signal is negative and pitch angle is kept at its optimum value. When the wind turbine output power Pmeasured exceeds the rated power Pref, the error signal is positive and the pitch angle changes to a new value, at a finite rate, thereby reducing the effective area of the blade resulting in the reduced power output. The PI controller inputs are in per-unit. 4.5 ROTOR SIDE CONVERTER The rotor-side converter is used to control the wind turbine output power and the voltage or reactive power measured at the grid terminals.

Fig. 4.7: Rotor side and Grid _side converter control circuit.

The actual electrical output power, measured at the grid terminals of the wind turbine, is added to the total power losses (mechanical and electrical) and is compared with the reference power obtained from the tracking characteristic. A Proportional-Integral (PI) regulator is used to reduce the power error to zero. The output of this regulator is the reference rotor current Iqr_ref that must be injected in the rotor by converter Crotor. This is the current component that produces the electromagnetic torque Tem. The actual Iqr is compared to Iqr_ref and the error is reduced to zero by a current regulator (PI). The output of this current controller is the voltage Vqr generated by Crotor. The current regulator output is Vqr [8, 13].Reactive and Active Power at grid terminals is controlled by the reactive and active current flowing in the converter Crotor The output of the voltage regulator or the var regulator is the reference d-axis current Idr_ref that must be injected in the rotor by converter Crotor. The same current regulator as for the power control is used to regulate the actual Idr. The output of this regulator is the d-axis voltage Vdr generated by Crotor. The current regulator output is Vdr. Vdr and Vqr are respectively the d-axis and q-axis of the voltage Vr [8, 30].

Qref V I

+ VAR MEASUREMENT

Var

Q

Id ref +

-

I

CURRENT

Id Iq

TRACKING CHA

Wr

CURRENT REGULATOR

-

Pref + POWER REGULATOR

V I

POWER MEASUREMENT

P

+ Iq ref

-

Fig. 4.8: Rotor side converter control.

4.5.1 MATLAB SIMULATION MODEL

Demux 3 wr

Theta

Vd calculation

Idq _s

Freq

4 Iabc_s

f(u)

wr

idqs

Iabc _s

Iqr*

abc_dq

f(u)

Vdqs

Vq calculation

Vabc (pu) wt Freq

Sin _Cos

1 Vabc

Discrete 3-phase PLL

turbine power charecteristics 2 Q_ref 6 Q

Theta Vdq Vabc

abc to dq

Q_ref Idr* Q

reactive power

50 In1

Idq _r

Idr* Demux Iqr* DemuxIdr w-wr Iqr

PI

vd' Demux

Vdq*

vq' Vdc

8 Vdc

Uctrl_r

Angle

F

7 angle _rotor angle _rotor r_angle Iabc _r 5 abc to dqr Iabc_r1

Fig. 4.9: Simulation Model of Rotor-Side Controller.

4.6 GRID SIDE CONVERTER The converter Cgrid is used to regulate the voltage of the DC bus capacitor. In this thesis , this model Cgrid converter to generate or absorb reactive power. In this control system ,measuring the d and q components of AC currents to be controlled as well as the DC voltage Vdc. The output of the DC voltage regulator is the reference current Idgc_ref for the current regulator. The current regulator controls the magnitude and phase of the voltage generated by converter Cgrid (Vgc) from the Idgc_ref produced by the DC voltage regulator and specified Iq_ref reference. The current regulator give the C grid output voltage [6, 43]. The magnitude of the reference grid converter current Igc_ref is equal to Idgc _ ref 2  Iq _ ref 2 .The maximum value of this current is limited to a value

defined by the converter maximum power at nominal voltage. When Idgc_ref and Iq_ref

dq 2 abc

rotor s

are such that the magnitude is higher than this maximum value the Iq_ref component is reduced in order to bring back the magnitude to its maximum value [9]. Vdc ref + DC VOLTAGE REGULATOR

Vd

Idg ref +

-

Ig

CURRENT MEASUREMENT

Idg Iqg

CURRENT REGULATOR

-

+ Iqg ref

Fig. 4.10: Grid side converter control.

4.6.1 MATLAB SIMULATION MODEL

Vq

Theta Vdq Demux

1 Vabc

vd' vdq refDemux vq' Idq_ref

Vabc

abc to dq 2 Iabc_g

Iabc Idq

Theta

Idq

Vdq*_g

control_g

-K-

Vdc

curent to volatge tf

1/z

converter voltage

dq2abc

Theta1

Vdcref

abc to dq 1

Vdc_nom

Vdc_ref

3 Vdc 4 Iq_ref

Vdc

Idq_ref

Iq_ref Vabc

DC to Idq ref

Q

2 Q

P

3 P

5 Iabc Iabc

Freq Vabc (pu) wt

T_F

Sin_Cos

active and reactive power

Discrete 3-phase PLL

Fig. 4.11: Simulation Model of Grid-Side controller and Power. Vds Ids Vqs Iqs

P1

3 P1

Q1

4 Q1

Subsystem

2 Vas

Va

3 Vbs

Vb

4 Vcs

Vc

Vd

Ia

5 Ias

Vqs

Iqs

Id

Ib

6 Ibs

Vds

Ids

We

Ic

dq to abc We

Iqr

Vqr

Idr

Vdr

Wr

TL

Te

We

0 abc 2 dq

Constant 0

1 TL

Iq Vq

Constant 1

Add

7 Ics

Iq

Ia

8 Iar

Id

Ib

We-Wr

Ic

9 Ibr 10 Icr

dq to abc 1

IM model -KGain

1 Nr 2 Te

1 Vabc_g

1 Vqs 2 Vds

Vds

4 Vqr 5 Vdr

1 Iqs

Vqs Fqs

Fqs

Iqs

Fds

Fds

Ids

Vqr Vdr

2 Ids

Iqs

3 Iqr

Ids Fqr

Iqr

Fqr

Iqr

4 Idr

We

Fdr

Fdr

Idr

Subsystem 1

6 Te

Wr

Idr

Wr

Subsystem

Te

Ids

Iqr

Idr

3 We

Iqs

6 TL

TL

Subsystem 2

Fig. 4.12: Induction machine model

CHAPTER-5 MAXIMUM POWER POINT TRACKING AND POWER SMOOTHING

5.1 INTRODUCTION In this thesis different way to track the maximum power were implemented. All these tracking characteristic process are previously implemented, but here these processes are compared and new one is implemented in different way. The variable speed control is

5 Wr

done based on the optimal power curve that shows the relation between the maximum output of the system (output) and the generator speed (input), namely maximum power point tracking (MPPT). The wind speed control or the generator speed control is adopted for MPPT.

At a given wind velocity, the mechanical power available from a wind turbine is a function of its shaft speed. To maximize the power captured from the wind, the shaft speed has to be controlled. For a given shaft speed turbine power increases with increase in wind velocity v. Also peak power points of turbine power occurs at different turbine speed for different wind velocity and shaft speed corresponding to maximum power increases with increase in wind speed. To trap maximum power from the wind some control algorithm should be incorporate such that rotational speed ω of the wind turbine adapts the to the wind speed v automatically leading to maximum power point operation. This is known as maximum power point operation of wind turbine, and the process of keeping track of peak Power points with change in wind speed is Maximum Power Point Tracking MPPT [17, 22].

The conventional method is to generate a control law to produce the target generator torque Te, which provides wind turbine with sufficient acceleration or deceleration torque to attain particular angular velocity leading to maximum power point operation. Irrespective of the generator used for a variable speed wind energy conversion system the output energy depends on the method of tracking the peak power points on the turbine characteristics due to fluctuating wind. The generator is operated in speed control mode with the speed reference being dynamically modified in accordance with the magnitude and direction of change of active power. If we operate at a peak power point a small increase or decrease in turbine speed would result in no change in

output power

because necessary condition for

maximum power point is dP/dw =0 [14]. 5.2 First Method using Power Point Tracking Characteristics

the speed to be a

The ABCD curve shows the power characteristics (Fig. 5.1). The actual speed of the turbine ωr is measured and the corresponding mechanical power of the tracking characteristic is used as the reference power for the power control loop. The tracking characteristic is obtained over four points. From zero speed to speed of point A the reference power is zero. Between point A and point B the characteristic is a straight line, the speed of point B must be greater than the speed of point A. Between point B and point C the tracking characteristic is the locus of the maximum power of the turbine. The tracking characteristic is a straight line from point C and point D. The power at point D is 1 pu and the speed of the point D must be greater than the speed of point C. Beyond point D the reference power is a constant equal to 1 pu [43].

Fig. 5.1: Power Point Tracking Characteristics.

5.2.1 MATLAB MODEL

power _D

power _C

Product

speed _D

speed _C 1 power

speed _C Product 1

Constant 1 wr

u(1)^3 wm ^3

power _C Gain

Switch

speed _A

speed _A-speed _B

Product 3

Constant 1 power _B

power _A

Product 2

speed _B

speed _A

Fig. 5.2: Simulation of Power Point Tracking Characteristics.

5.3 Second Method using MPPT curve implemented as look-up table

From the above discussion it can be conclude that for the maximum power characteristic divided in different region, then using slop equation manipulates the value of power which is used as reference power for the simulation. Here same characteristics is used as look-up table ,where the power only measured only some few wind velocity like at A,B,C & D. at other point it is not implemented i.e. other velocity in-between these point.

5.3.1 LOOK_UP Table:

Fig. 5.3: Look-up Table.

5.4 MPPT corresponding to the optimum power for particular velocity.

In this method the power is obtained from the different wind velocity by comparing the rotor speed and the active power. The proposed algorithm is explained with the help of Fig. 5.4, where the P vs. ω curves corresponding to three wind velocities v1, v2 & v3. The algorithm initiates the maximum power searching process by rotor speed ωr. Here the power varies according the variation of wind velocity. So this method helps to get the maximum power from the wind at different velocities [14].

Fig. 5.4: Tracking Characteristics for Different Velocity [14].

Let the present wind velocity be v1. The generator is run in the speed control mode with a speed reference of ω1.The generator output power and speed are sampled at regular intervals of time. If the wind velocity is steady at v1, the difference between successive samples of active power P (i.e., ΔP) will be very small and no action is taken. Now, let there be a step jump in wind velocity from v1 to v2. Since the turbine shaft speed cannot change instantaneously (the reference for the speed controller is not yet changed and the inertia of the system is extremely high), this would result in a change of operating point from P1 to P2. Therefore, ΔP would be large and positive. Corresponding to this change in ΔP, a positive change in speed reference is commanded. The change in speed reference ΔWr* is made proportional to ΔP. This shifts the operating point from P2 to P3, resulting in a smaller positive change in ΔP. Since this change in ΔWr* is due to a positive change in ΔP, it implies that the peak power point is further to the right-hand side on the curve. Thus, a further positive change in ΔWr* is commanded in proportion to ΔP. In this process, when ΔP becomes very small (within some defined band), no further change in speed command is given and the system keeps operating at P4 . Now, if the wind velocity again changes from v2 to v1 , the operating point shifts to P5, resulting in a large negative change in ΔP. Thus, a negative change in speed reference in proportion to ΔP is applied. However, this results in a positive change in ΔP as the operating point shifts to P6 . Since the positive change is due to a negative change in speed command, the peak power point is to the left of P6 . Therefore, the speed reference is further reduced. The algorithm continues until ΔP is within the predefined band and the operating point

again slides back close to P1 [14].

5.4.1 MATLAB MODEL 1 z Unit Delay 2 -Csign w

is_dw(n-1)=0

2 wr

Sign

signdet

w(n-1)

-C-

1

delw

kstep

z Unit Delay

is_dp <0.5

wstep

w

0

p 1 P

1 wref

dw

kstep1 Sign 1 p(n-1) 1

|u|

z Unit Delay 1

Abs

abs(P)

Fig. 5.5: Simulation Model of Tracking Method for Step by Step Procedure.

The algorithm is implemented in following manner [14]:-

1- The algorithm initiates the maximum power searching process by setting an arbitrary speed reference Wr*.

2- The active power is sampled at a particular rate and the incremental change is computed as ∆P(k) = P(k) – P(k-1). 3-The magnitude of ∆W* is given by Mag (∆Wr(k)*) = Mag(Kt*∆P(k)), where Kt is constant and is needed to be selected carefully.

4-The reference speed is sampled at same speed as power. 5- If P(k) >= P(k-1), the maximum power point is not attained, so the speed reference Wr* is increased by ∆Wr*. This process is repeated according to flowchart shown in Fig. 5.5 until maximum power point is reached. 6-If P(K) < P(K-1), the speed reference is decreased by ΔWr* . This process is repeated according to flowchart shown in Fig. 5.5 until maximum power point is reached. 7- If the magnitude of ∆P(k) is within prescribed tolerance band Pband, then the speed reference is not changed else it is to be changed by (∆Wr(k)). 5.5 Pitch Angle Controller In this chapter, a new logical pitch controller equipped with a fuzzy logic controller (FLC) has been proposed that can enhance the transient performance of a WTGS during severe network disturbance .Moreover , it can maintain the output power at the rated level when the wind speed is higher than the rated speed .To evaluate the effectiveness of the proposed controller in improving the transient stability ,simulation has been carried out for severe network disturbances and severe wind condition, considering the the mechanical dead zone of the pitch actuation system.[1] 1. Conventional Pitch Controller The conventional pitch controller shown in Fig(5.6). Can be used to maintain the output power of a wind generator at its rated level when the wind speed is over the rated speed. The pitch servo is modeled with a first order delay system with a time constant, Td.Beacuse the pitch actuation system cannot control, in general, respond instantly, a rate limiter is added to obtain a realistic response. e +

Kp Ti

PIG -

1 1 Td s

x°/s

90 0

β

1 Fig.(5.6) Conventional Pitch Controller

2. Wind Generator Power Smoothing by Using the New Pitch Controller[1] For wind generator output power smoothing, the most important part is to determine the pitch controller input power command , PIGREF .

The following steps explain the generation of the pitch controller input power command , PIGREF (i)The wind turbine captured power , PWT, can be obtained from 𝑃𝑚 = 𝐶𝑝

1 𝜌𝐴𝑉𝜔 3 2

(ii)The average value of wind power, P𝑊𝑇 ,can be calculated from the EMA(C) = [(C-P) xK] +P Where C=the current value, P=the previous period’s EMA and K=Weighting factor (iii)The slandered deviation can be calculated from the following equation 𝑃𝑊𝑇𝜎 =

𝑡 𝑡−𝑇

𝑃𝑊𝑇 − 𝑃𝑊𝑇

2

𝑑𝑡

𝑇

(iv) Finally the controller’s revised input power command, 𝑃𝐼𝐺 𝑅𝐸𝐹 can be obtained from 𝑃𝐼𝐺 𝑅𝐸𝐹 = 𝑃𝑊𝑇 − 𝑃𝑊𝑇𝜎 5.5.1 Proposed Fuzzy Pitch Controller

e

PIG

Ke

en

+ PIGRE

-

Fuzzy βcmdn Logic controller Kβ

F

1/Z

+

Δe

KΔe

Δen

βcmd β

MDZ Block

x°/s

1 1  Td s

Fig. (5.7) Fuzzy Pitch Controller Block diagram

90 0

5.6 Fuzzy logic system Zadeh introduced Fuzzy sets in his landmark paper in 1965. The main difference between fuzzy and conventional sets is in the definition of membership of a given set. For conventional set theory, a number either is or is not a member of any given set. The membership can then be represented as a binary quantity: either 1 or 0. On the other hand, the membership of a number in a fuzzy set is defined in terms of a membership function, which generally varies between 0 and 1. The membership function represents a degree of membership of the value in the particular fuzzy set. Fuzzy control uses fuzzy set theory to define a non-linear controller and was first developed by Mamdani in 1975. A fuzzy controller has three components: a fuzzifier, a rule base and a defuzzifier as shown in Fig.5.8 [25] [29]. The inputs to the controller are crisp numbers and the outputs are also crisp numbers, but all processing inside the controller is done using fuzzy variables. The first step is to convert the crisp inputs to memberships of each fuzzy set defined for the inputs. This operation is called fuzzification and the block that performs this operation is called a fuzzifier. These membership functions are used in the rule base which relates fuzzy values of the inputs to the output of the rule base. The output of the rule base is also a set of membership values of the fuzzy sets defined for the output variable. To interface with the physical world, this fuzzy variable has to be converted back to a crisp number. This is done by the defuzzifier.

Crisp inputs

Fuzzifier

Rule base

Defuzzifier

Crisp output

Fig.5.8 Components of a fuzzy controller (1) Fuzzifier To design the proposed FLC ,the error signal ,e(k),ans the change of error signal, Δe(k),are considered the controller inputs .The angle , βcmd , is considered the controller output,which is actually the pitch angle command signal for the mechanical servo system.For convenience, the inputs and the out puts of the FLC are scaled with coefficient Ke, KΔe, KΔe, respectively.In this work ,these scaling factor can be constants or variables and play an important role in the in the FLC design to achieve a good response in both transient and steady states.In this work , these scaling factors are considered constant for simplicity of the the controller design and selected by trial and error .The values of Ke, KΔe, KΔe are chosen as 1.0,1000and 100 respectively.In fig 5.7 Z-1 represent one sampling time delay.The triangular membership functions with overlap used for the input and output fuzzy sets are shown in Fig.(5.11) in which linguistic variables are represented by NB, NM, NS,ZO,PS,PM,PB .The grade of input membership function can be obtained from the following equation. 𝜇 𝑥 = 𝑤 − 2 𝑥 − 𝑚 /𝑤

where , 𝜇 (x) is the value of grade of membership, w is the width ,m is the coordinate of the point at which the grade of membership is 1, and x is the value of the input variable. [1]

(2) Rule base The rule base maps the fuzzy sets for the inputs to the fuzzy sets for the output and constitutes the core of a fuzzy controller. Table 5.1 shows a rule base. NB, NM, NS,ZO,PS,PM,PB are notations for fuzzy sets Negative Big ,Negative Medium , Negative Small ,Zero, Positive Small ,Positive Medium Positive Big respectively. The rule base has two dimensions corresponding to a fuzzy controller with two inputs and one output. Each row gives the resultant output fuzzy set for each combination of input fuzzy sets. The entire rule base can be described in terms of such IF-THEN statements or rules, which are AND and OR together. Δen βcmdn NB NB NM NS

en

ZO PS

NB NB

NS

NB

NM

NM

NM

NM

NS

NM

NS

ZO

NS

NS

PS

NM

NS PM ZO PB

NM

ZO PS

ZO

PS

PM

NM

NS

NS

NM

NS

PM

ZO PS

ZO

PS

PS

ZO

PS

PS

PM

PS

PS

PM

PM

P M

PB

NS

PS

ZO

PB

PS

PM

P M PM

PB

PB

Table 5.1 Rule base for a fuzzy pitch controller The fuzzy mapping of the input variables to the output is represented by IF-THEN rules of the following forms: IF IF< IF<

en

en

is NB> and < Δen is NB >THEN< βcmd is NB>

is ZO> and < Δen is ZO >THEN< βcmd is ZO>

IF<

en

is PB> and < Δen is PB >THEN< βcmd is PB>

The advantages of this characteristic are: • The rule base can be tuned very easily, since for some given inputs, depending on the performance of the controller, only the active rules need to be adjusted. This is in contrast to linear controllers, where changing any gain affects the system performance over its entire range of operation. • The output of the rule base is converted to a crisp value using the defuzzifier. This provides for inherent smoothening of the controller output. Thus, a fuzzy controller is a true non-linear controller. (3) Defuzzifier The output of the rule base is a fuzzy variable which is converted to a crisp value by the defuzzifier. Many defuzzification methods have been proposed, of which the centre of area or centroid defuzzification is most common. Using this method, the crisp output is given by: βcmd =

𝑁 𝜇𝑐 𝑖=1 𝑖 𝑖 𝑁 𝜇 𝑖=1 𝑖

(4.11) Where, μi = membership value of control input for the ith fuzzy set of the output ci = centroid of the membership function for the ith fuzzy set of the output N = number of fuzzy sets for the output If the areas of all membership functions for the output are the same, the Ai term drops out of the defuzzification equation.[1]

5.7 Fuzzy logic Control Block

Fig (5.9) Block diagram of pitch controller to which fuzzy controllers are applied.

Fig.5.10. FIS structural characteristics, used in simulation system

Fig(5.11) Inputs, and their membership functions appear to the left of the FIS structural

characteristics, while outputs and their membership functions to the right side of FIS

CHAPTER-6 RESULTS

DFIG SIMULATION AND

6.1 INTRODUCTION The DFIG model uses the power electronic IGBT converters for controlling the signals which is generated by the discrete PWM generators. This converters are achieve an acceptable accuracy with the 1620 Hz switching frequency used in this model, the model must be discretised at a relatively small time step . This model is well suited for observing harmonics and control system dynamic performance over relatively short periods of times. A 9 MW wind turbines connected to the 575 V generation bus. . Wind turbines using a doubly-fed induction generator (DFIG) consist of a wound rotor

induction generator and an AC/DC/AC IGBT-based PWM converter modelled by voltage sources [43]. 6.1.1 The DFIG Simulation Model is grouped in four groups: Generator, Converters, Turbine, and Control [wr] [Va]

+ - v

Va_ph

Vo

N

A

pitch angle

Vabc

Iabc

A

[Iabc ]

B C

c

C

c

A B C

stator_curent

g A

b

c

c

C1

B -

C

g

Iabc A

A

a

+

A B

b

+

B

b

C

c

-

C

Universal Bridge

R_l A

A

B

B

B

C

C

C

Universal Bridge

Asynchronous Machine pu Units1

Filter 0.9 Mvar Q=50

0

Iabc

a

B

b

grid _curent

rotor_ccurent

m

A

Grid voltage and current

[Vabc]

[angle ]

a

C

575 voltage source

Tm

[Iabc _s]

B

b

C

V_cap [Iabc _g]

a

a

[Vdc]

Vin_meas

[Iabc _r]

wind [Vabc]

Iabc

B

thetam

Wind speed (m/s)

+ - v

[wr]

m

Wind Turbine

I_grid _phase

+ i -

A

wm

[T] Pitch angle (deg) Tm (pu)

V

[Ia]

2

Generator speed (pu)

0

[vcr]

Vabc

Uref

Pulses

[vcg ]

Uref

Discrete PWM Generator

Q_ref

Pulses

Discrete PWM Generator 1

Qref (pu) [wr]

[Vabc]

wr

0 [Iabc _s]

Iabc _s

[Q]

Vabc_g

Iabc _r1

[vcr]

[P]

Iabc _g

Q

[Q]

Vdc

[Iabc ]

Iabc

Scope 1 reactive power (pu)

Scope 3

reactive power P

[P] [wr]

Q

Scope

active power (pu)

[Q]

[Vdc]

Vph_grid

Vph _grid

active power

[Iabc _g]

Grid side control [angle ]

[vcg ]

Iqref

Iq_ref (pu ) Vabc_r

[Iabc _r]

[Va] Vabc

Wr (rad/sec)

Scope 4

Gen _speed 1

[Vdc ]

angle _rotor

Vdc

From Vdcref [Vdc]

[T ]

Vdc

Torque (rad/sec)

Gen _speed 2

rotor side control

Scope 5

Vdc_nom

Vdc*

Scope 2

Fig. 6.1: Simulation Model of DFIG.

The simulation of DFIG is done in order to predict the results theoretically, prior to the experimental verification.

Discrete, Ts = 5e-005 s. powergui [wr1] [Va]

+ v -

Vo 2 + i -

A

Generator speed (pu)

[Pitch_deg] Tm (pu)

m

Vw

I_grid_phase

wind [Vabc]

A

Vabc

A

[Iabc_r] Tm

[Iabc_s]

Iabc

B

B

575 voltage source

B

b C

Grid voltage and current

A B C

0 Qref (pu) [wr]

b

C

c

Filter 0.9 Mvar Q=50

Vabc

0

Iqref

Iq_ref (pu) Iabc_s

[Iabc_r]

Iabc_r1

Vabc_g

Uref

Pulses

A

a

B

b

C

c

Uref

[P] Scope1

[Q]

active power

P

[P]

reactive power

[Q]

Iabc

Grid side control

Q

[wr]

[Vdc] [P] active power2

[T]

angle_rotor

[Vdc]

Vdc

B

C

C

C

[wr1] wref

wr

MPPT_Controller

Clock1 [wr]

is_dp<0.2

Wr (rad/sec)

Scope4

Torque (rad/sec)

Scope5

[P]

P

Pitch (deg)

Vdc

[Va]

From Vdcref Vdc_nom

Vph_grid

Vph_grid

Scope

Vdc*

Speed & Pitch Control

Scope2

Fig. 6.1.1: Simulation Model of DFIG with mppt and fuzzy pitch controller.

6.1.2 Doubly Fed Induction Generator Control parameter initialization Name of parameter Capacitance Inertia constant Frequency[Hz] Friction factor R_L inductance Rotor inductance Stator inductance Mutual inductance PWM Frequency[Hz] Rated output[W] Base output[W] R_L resistance[ohm]

B

P

active power1

Scope3

Torque

P

rotor side control

A

B

Pulses

reactiv e power(pu)

Gen_speed1 [angle]

A

Discrete PWM Generator1

[Iabc] [Q]

R_l

A

Universal Bridge

[vcg]

activ e power(pu)

Q

Vdc

[Vdc]

Universal Bridge

[P]

Iabc_g

[vcr]

Iabc

-

[vcg]

[Iabc_g]

Vabc_r

C

Discrete PWM Generator

Q_ref

[Vabc]

c

C1

B

C

g +

A

b

c

[vcr]

[Iabc_s]

+

Asynchronous Machine pu Units1

Vabc

wr

g A B

c

stator_curent

[Vabc]

Iabc a

B

b C

grid_curent

rotor_ccurent

a

a

a

C

m

A

Iabc N

[Vdc] V_cap [Iabc_g]

Wind Turbine

[Iabc]

[angle]

thetam

Wind speed (m/s)

[Ia]

+ v -

Vin_meas

[wr]

wm

[T] Pitch angle (deg)

Va_ph

Variable name DC_link J Fnom F L_Rl Llr Lls Lm PWM_freq Pmec Pnom R_RL

Value taken 0.060 0.010 60 5.040 0.300 0.156 0.171 2.9 1620 9000000 10000000 0.003

[Pitch_deg]

Rotor resistance[ohm] Stator resistance[ohm] sampling time Rated dc Voltage Rated Voltage no of pole pair Tracking power at c Tracking speed at c

Rr Rs Ts_power Vdc_nom Vnom P Power_c Speed_c

0.005 0.00706 5.0e-05 1200 500 3 0.730 1.20

Table 6.1 DFIG Control parameter 6.2 RESULTS This chapter also gives the results that obtained in this thesis, along with their description. The MATLAB/ SIMULATION provide a convenient tool for the electrical systems. The DFIG control scheme being studied has been simulated using matlab.

Previously the basic structure of wind energy conversion system WECS employing for DFIG was discussed. The aerodynamic characteristics of the wind turbine and control and operation of various power electronic blocks for DFIG based variable speed constant frequency generation system were also discussed. In this chapter the proposed scheme of variable speed constant frequency wind generation system in which the stator windings of DFIG are connected to the utility grid directly and the rotor connected through PWM converter. This model is suitable for various wind velocity. Here also various MPPT scheme applied and the result taken for conclusion.

6.2.1 First Method using Power Point Tracking Characteristics

These simulation results give the information about the active and reactive power control. Here objective is to achieve zero reactive power, maximum active power and constant DC link voltage. Based on this report it is flexible to extend the design. Simulation result shows the controlled reactive power and active power, and constant DC link voltage.

Fig. 6.2: First Simulation results.

Detailed results of simulations performed are given in Fig.6.2. It can be seen from the Fig.6.2 that in the case of 1st method the maximum active power (pu) cannot be achieved but zero magnitude of reactive power (pu) is achieved. Here the simulation of Va shows the phase voltage (469.48 volt). This simulation shows the speed of the generator remains constant and DC link voltage response is strongly varying for 0.1 seconds then becomes stable near to reference voltage (1200 Volts).

6.2.2 Second Method using MPPT curve implemented as look-up table

Simulation results of DFIG shows that the second method gives the controlled reactive power and active power, and also maintains the DC link voltage constant. The second method achieves the reactive power output zero faster than the first method but this method works only for some particular wind velocities.

Fig. 6.3: Second Simulation results.

It can be seen from the Fig.6.3 that in the case of 2nd method the maximum active power (pu) cannot be achieved but zero magnitude of reactive power (pu) is achieved.

DC link voltage response is highly varying whereas it remains fairly stable after few seconds in the 1st method.

6.2.3 MPPT method.

The main advantage of this method is that it is applicable for different wind velocity. Here the active power also controlled and gives the maximum power output i.e. nearly 50% of the rated power which is shown in the MPPT curve for 10 m/s wind velocity. First and Second method shows that controlled reactive power (pu) and constant DC link voltage achieved, but active power (pu) cannot be achieved.

Fig 6.4: Third Simulation results.

Fig. 6.4 shows that in the case of proposed method the maximum active power achieved. The simulation result shows that accuracy of this method is better than the previous methods. In this case also the DC link voltage remains constant at 1200 volts after 0.27 second which shows the better output than the previous two methods.

6.2.4 Proposed method for MPPT corresponding to the fuzzy pitch controller:

In this method fuzzy pitch controller is used . Here the active power also controlled and gives the maximum power output i.e. nearly 60% of the rated power which is shown in the MPPT curve for 10 m/s wind velocity. First and Second method shows that controlled reactive power (pu) and constant DC link voltage achieved, but active power (pu) cannot be achieved. In this proposed method more active power is achieved and smoothing of power output of WECS is superior than the conventional methods. 1.6

1.4

1.2

Pref

1

0.8

0.6

0.4

0.2

0

0

0.05

0.1

0.15

Time(sec)

0.2

0.25

0.3

Smooth Active Power (p.u.)

6

5

4

3

2

1

0

0

0.05

0.1

0.15

Time(sec)

0.2

0.25

0.3

1800 1600 1400

DC Voltage(Volt)

1200 1000 800 600 400 200 0

0

0.05

0.1

0.15 Time(sec)

0.2

0.25

0.3

6

Pitch Angle(Degree)

5

4

3

2

1

0

0

0.05

0.1

0.15

Time(sec)

0.2

0.25

0.3

0.35

1

Active Power (p.u.)

0.8

0.6

0.4

0.2 With fuzzy pitch controller With conventional pitch controller 0

0

0.05

0.1

0.15

0.2

0.25

0.3

Time(sec)

Fig. (6.5) Proposed Fuzzy pitch controller Simulation Results Fig. 6.5 shows that in the case of proposed method the maximum active power smoothing is achieved. The simulation result shows that accuracy of this method is better than the previous methods. In this case also the DC link voltage remains constant at 1200 volts after 0.20 second which shows the smooth output than the previous three methods. This Fig. also shows the variation of pitch angle in the range of (0 to 5 degrees). The comparative simulation result show that the active power is increased from 0.4 to nearly 0.55 p.u. and the output power is smoother as compared to other method.

CHAPTER-7

CONCLUSION AND FUTURE WORK

7.1 CONCLUSION Due to wind’s unpredictable nature, power management concepts are necessary to extract as much power as possible from the wind when it becomes available. The work presented in this thesis studied peak power tracking in WECS using fuzzy logic controlled based pitch controller for power smoothening. The variable speed constant frequency (VSCF) wind generation system is being simulated in MATLAB/ Simulink software with an algorithm to track peak power points of wind turbine and a control scheme has been implemented to control active and reactive power injected in to the grid. The knowledge of the wind turbine aerodynamic characteristics is unnecessary in order for the maximum power point tracking algorithm to work. In the proposed wind energy conversion system (WECS) a DFIG converts the mechanical energy derived from wind turbine into electrical energy. As the voltage and frequency of generator output maintain along the wind speed change, PWM converter is utilized to maintain constant dc link voltage. The proposed scheme has been tested with the variable wind speed signal and the peak power tracking as well as smooth power is observed. Proposed scheme gives a low cost and high quality power conversion solution for variable speed WECS. The proposed method is compared with other conventional MPPT methods which show its effectiveness for extracting the maximum power as well as smoothing the power output of wind energy conversion system.

7.2 FUTURE WORK

DFIG modelling and control system helps to increase the use in electrification. So fuzzy logic controller can be developed for implementation in DFIG. The Maximum power point tracking scheme helps in proper exploitation of the wind power available at any site thus leading to increased annual energy capture. As the wind speed is always fluctuating, improved control concepts are needed for proper tracking process. Vector control with application adaptive control techniques can be developed for proper implementation of maximum power point tracking scheme. As a next step of this dissertation work, one can experimentally implement the algorithm on a low power wind turbine using field programming gate array (FPGA) techniques or using digital signal processors (DSP), also implementing these techniques for fault ride through. In addition to this, the proposed work MPPT in WECS may be extended to hybrid DG system consisting of wind and photovoltaic energy resources.

REFERENCES

[1]

S.M. Muyeen,Junji Tamura Toshiaki Murata“Stability Augmentation of Gridconnected Wind Farm” Springer 2009

[2]

Malcom Barnes, “Practical variable speed drives and power electronics”, Newness press, Elsevier, Oxford, Burlington, 2003.

[3]

Chichester, UK. Hindmarsh, J., “Grid Integration of Wind Energy Conversion Systems”, John Wiley and Sons, England, 2005

[4]

Ackermann Thomas, “Wind Power in Power Systems”, John Wiley and Sons, England, 2005.

[5]

Boldea, I, “Variable Speed Generators” , Taylor and Francis, 2006.

[6]

.R. Pena, J.C. Clare, G.M. Asher, "Doubly fed induction generator using backto-back PWM converters and its application to variable-speed wind-energy generation," IEEE Proc.-Electr. Power Appl., Vol. 143, No. 3, May 1996.

[7]

A. Beugniez, T. Ghennam generation for

“Centralized supervision of

reactive power

a wind farm” Power Electronics & Application European

conference on 2-5 sep 2007. On page(s): 1-10 ISBN: 978-9275815-10-8. [8]

Jamal A. Baroudi, Venkata Dinavahi, Andrew M. Knight, “A review of power converter topologies for wind generators”, Renewable Energy, vol.32, No. 14, pp. 2369–2385, Nov. 2007.

[9]

Seung-Ho Song, Shin-il Kang, Nyeon-kun Hahm, “Implementation and control of grid connected AC-DC-AC power converter for variable speed wind energy conversion system”, vol. 1, pp. 154-158, Feb 2003.

[10] A. Miller, E. Muljadi, and D. S. Zinger, “A variable speed wind turbine power control,” IEEE Trans. Energy Conversion, vol. 12, pp. 181–187,June 1997.

[11] Ekanayake, J.B, Holdsworth, L, Wu, X., Jenkins, N. Dynamic modelling of Doubly Fed Induction generator wind turbines. IEEE Transaction on Power Systems, 2003, 2:803-809. [12] S. K Salman and Babak Badrzadeh School of Engineering, The Robert Gordon University, IEEE paper on New Approach for modelling Doubly-Fed Induction Generator (DFIG) for grid-connection studies. [13] R. Datta, V.T. Ranganathan, “Variable speed wind power generation using doubly fed wound rotor induction machine “ a comparison with alternative schemes, IEEE Trans. Energy Convers. 17 (3) (2002) 414–421. [14] R. Datta, V.T. Ranganathan, “A method of tracking the peak power points for a variable speed wind energy conversion system”. IEEE Transactions on Energy Conversion, vol. 18, pp. 163-168, No. 1, March 2003.

[15] H. Joanne, “An Adaptive Control Algorithm for Maximum Power Point Tracking for Wind Energy Conversion Systems”, MS Thesis, Queens university, Department of Electrical and computer Engg. , Canada, December 2007. [16] H. E. M. Lopez, “Maximum power tracking control scheme for wind generator systems”,

MS Thesis,

Texas

A&M university, Department of

Electrical Engg. , Texas, December 2007. [17] Jamal A. Baroudi, Venkata Dinavahi, Andrew M. Knight, “A review of power converter topologies for wind generators”, Journal of Renewable Energy, vol.32, No. 14, pp. 2369–2385, Nov. 2007. [18] L.Szabo, K.A.Biro, C.Nicula and F.Jurca, “Useful Simulation Tool for Induction Generators Used In Wind Power Plants” IEEE Conference on clean electrical power (ICCEP). Page(s) 574-579; 21-23 May 2007. [19] Eftichios Koutroulis and K. Kalaitzais, “Design of maximum power point tracking system

for

wind-energy-conversion

applications”,

IEEE

Transactions on industrial electronics, Vol. 53, No. 2, pp. 486-494, April 2006. [20] Q. Wang and L.C. Chang, “An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems”, IEEE Transactions on Power Electronics, Vol. 19, pp. 1242–1249, September 2004. [21] K.V.V. Suresh, “Performance investigations of wind energy conversion system”, M.tech Thesis, IIT Roorkee, Department of Electrical Engg. , IIT Roorkee, June 2004. [22] G. Moor and H. Beukes, “Power point trackers for wind turbines,” Power Electronics Specialist Conference (PESC), pp. 2044–2049, 2004. [23] A. B Raju, K. Chatterjee, B. G. fernandes, “A Simple Maximum Power Point Tracker for Grid connected Variable Speed Wind Energy Conversion System with

Reduced Switch

Count

Power

Converters”,

power

electronics

specialist conference, vol. 2, pp. 748-753, June 2003. [24] J.G.Slootweg, H.Polinder and W.L. Kling, “Dynamic modeling of a wind turbine with doubly fed induction generator” IEEE PES, Vol. 1, Page(s):644 – 649,July 2001 [25] R. Hilloowala and A. M. Sharaf, “A rule-based fuzzy logic controller for a PWM inverter in a stand alone wind energy conversion scheme,” IEEE Transactions Industrial Applications, vol. IA-32, pp. 57–65, Jan. 1996. [26] M. Ermis, H.B. Ertan, E. Akpinar and F. Ulgut, “Autonomous wind energy conversion system with a simple controller for maximum power transfer”, IEE PROCEEDINGS-B, Vol. 139, No. 5, pp. 421-428, September 1992. [27] Shiri, A.; Vahedi, A.; Shoulaie, A. “The effect of parameter variations on the performance of indirect vector controlled induction motor drive” Power Electronics and Motion Control 5thInternationalConference Vol- 2, Page(s):1 – 5. 14-16 Aug. 2006.

Tahami, F.; Shojaei, A.; “A Novel Fault Tolerant Reconfigurable Concept

[28]

for Vector Control of Induction Motors” Power Electronics and Motion Control, 12th International Conference. Page(s):1199 - 1204 .Aug. 2006. Hu, Y. He, Lie Xu and B. W. Williams, “Improved Control of DFIG Systems

[29]

During Network Unbalance Using PI–R Current Regulators” IEEE Trans. Ind. Electron., vol. 56, no. 2, pp. 439-451, Feb. 2009. [30] F. Mei and B. Pal, “Modal Analysis of Grid-Connected Doubly Fed Induction Generators,” IEEE Trans. on energy conversion, vol. 22, no. 3, pp. 728-736, September 2007 A. Tapia, G. Tapia, J. X. Ostolaza, and J. R. Sáenz, “Modeling and

[31]

Control of a Wind Turbine Driven Doubly Fed Induction Generator,” IEEE Trans. on energy conversion, vol. 18, no. 2, pp.194-204 June 2003. [32] H. K.-Davijani, A. Sheikholeslami, H. Livani and M. K.-Davijani, “ Fuzzy Logic Control of Doubly Fed Induction Generator Wind Turbine,” World Applied Sciences Journal, vol.6, no. 4, pp.499-508, 2009 [33] Wei Qiao, Ganesh K. Venayagamoorthy,and R. G. Harley,“ Design of Optimal PI Controllers for Doubly Fed Induction Generators Driven by Wind Turbines Using Particle Swarm Optimization.” 2006 IEEE International Joint Conference on Neural Networks, Canada ,July 16-21, pp.1982-1987, 2006. [34] R. M. Hilloowala, and A. M. Sharaf, “A Rule-Based Fuzzy Logic Controller for a PWM Inverter in a Stand Alone Wind Energy Conversion Scheme,” IEEE Trans. on industry applications, vol. 32, no. 1,pp. 57-65, January/February 1996. [35] R. M. Hilloowala, and A. M. Sharaf, “A Rule-Based Fuzzy Logic Controller for a PWM Inverter in a Stand Alone Wind Energy Conversion Scheme,” IEEE Trans. on industry applications, vol. 32, no. 1,pp. 57-65, January/February 1996. [36] Amendola, C.A.M.

Gonzaga, D.P. ,“ Fuzzy-Logic Control System of a

Variable-Speed Variable-Pitch Wind-Turbine and a Double-Fed Induction Generator,” Seventh IEEE International Conference, Intelligent Systems Design and Applications, 2007. ISDA 2007, pp. 252 – 257, 20-24 Oct. 2007. [37] Z. Yongchang and Z. Zhengming,“ Comparative study of PI, sliding mode and fuzzy logic controller for rotor field oriented controlled induction motor drives,”IEEE International Conference, Electrical Machines and Systems, 2008. ICEMS 2008, pp. 1089 – 1094, 17-20 Oct. 2008 [38] B.Chitti Babu , K.B.Mohanty” Doubly-Fed Induction Generator for Variable Speed Wind Energy Conversion Systems-Modeling & Simulation” International Journal of Computer and Electrical Engineering, Vol. 2, No. 1, February, 2010 1793-8163 [39] G.D Rai , “Non-Conventional Energy Sources”, Khanna Publication, 1988 [40] P. Kundur, “Power System Stability and Control”, New York: Mc Graw Hill, 1994. [41] M. Godoy Simoes and F.A.Farret, “Renewable Energy Systems: Design and Analysis with Induction generators”, CRC Press, Boca Raton, FL, 2004

[42] Renewable Energy Sources, Available online: http://www.teachers.ash.org.au /jmresources/energy/renewable.html#general. (date: 10, Jan 2010).

[43] The Math works Inc.. Matlab and Simulink Documentation, Available online: http://www.mathworks.com [44] http://www.windpowerindia.com