Thesis By Milky

BAHIR DAR UNIVERSTY BAHIR DAR INSTITUTE OF TECHNOLOGY (BiT) FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING

DISTRIBUTION SYSTEM RELIABILITY ENHANCEMENT USING SMART GRID TECHNIQUES: CASE STUDY BAHIR DAR POWER DISTRIBUTION SYSTEM

By: MILKY ALI

Advisor: Dr.DEREJE SHIFERAW

FEBRUARY, 2016

BAHIR DAR UNIVERSTY BAHIR DAR INSTITUTE OF TECHNOLOGY (BiT) FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING

DISTRIBUTION SYSTEM RELIABILITY ENHANCEMENT USING SMART GRID TECHNIQUES: CASE STUDY BAHIR DAR POWER DISTRIBUTION SYSTEM A thesis submitted in partial fulfillment of the requirements for the award of the degree of MASTER OF SCIENCE IN ELECTRICAL AND COMPUTER ENGINEERING ( Power Systems Engineering)

By Milky Ali Advisor Dr. Dereje Shiferaw February, 2016

BAHIR DAR UNIVERSTY BAHIR DAR INSTITUTE OF TECHNOLOGY (BiT) FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING

DISTRIBUTION SYSTEM RELIABILITY ENHANCEMENT USING SMART GRID TECHNIQUES: CASE STUDY BAHIR DAR POWER DISTRIBUTION SYSTEM

By: MILKY ALI Approved by Board Examiners:

Dr. Belachew Bantyirga Chairperson

Signature

Dr. Dereje Shiferaw Advisor

Signature

Dr. K.E.CH.V.Sagar Internal Examiner

Signature

Dr.Getachew Biru External Examiner

Signature

Declaration I, the undersigned, declare that this thesis is my original work, has not been presented for a degree in this or any other universities, and all sources of materials used for the thesis have been fully acknowledged. Milky Ali Name Signature Date of Submission: --------------------------This thesis has been submitted for examination with my approval as a university advisor.

Dr. Dereje Shiferaw Thesis advisor

Signature

Acknowledgement First of all, I would like to thank God for the time and the patience he gave me for completing this thesis. I would like also to express my deepest gratitude to my advisor, Dr. Dereje Shiferaw, for his critical guidance and encouragement with constructive comments and suggestions. I would like also to thank all my friends and class mates who have been besides me during this thesis work.

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Table of Contents LIST OF FIGURES ................................................................................................................................. v LIST OF TABLES ................................................................................................................................. vi LIST OF ACRONYMS AND SYMBOLS ........................................................................................... vii CHAPTER 1: INTRODUCTION ........................................................................................................... 1 1.1. Background .................................................................................................................................. 1 1.1.1 What’s smart grid? ................................................................................................................. 3 1.1.2 Traditional power grid versus smart grid ............................................................................... 6 1.2 Statement of the problem .............................................................................................................. 7 1.3 Objectives ...................................................................................................................................... 8 1.3.1General Objective .................................................................................................................... 8 1.3.2 Specific Objective .................................................................................................................. 8 1.4 Significance of this study .............................................................................................................. 8 1.5 Expected Outcomes of the study ................................................................................................... 9 1.6 Scope ............................................................................................................................................. 9 1.7 Thesis Outline ............................................................................................................................. 10 CHAPTER 2: LITERATURE REVIEW .............................................................................................. 11 2.1 Smart grid technologies ............................................................................................................... 11 2.1.1 Phasor measurement units (PMUs) ...................................................................................... 12 2.1.2 Fault locator schemes ........................................................................................................... 13 2.1.3 Distribution automation........................................................................................................ 14 2.1.4 Dynamic voltage restorer ..................................................................................................... 14 2.1.5 Fault passage indicator ......................................................................................................... 14 2.1.6 Intelligent electronic devices ................................................................................................ 15 2.1.7 Feeder automation ................................................................................................................ 16 2.2 Network reconfiguration ............................................................................................................. 17 2.3 Reconfiguration with distributed generator ................................................................................. 19 2.4 Smart grid distribution system .................................................................................................... 20 2.5 Optimal placement of switches ................................................................................................... 23 CHAPTER 3: DISTRIBUTION AUTOMATION................................................................................ 25 3.1 Categories of distribution automation ......................................................................................... 26 3.2 Benefits of distribution automation ............................................................................................. 27 3.3 SCADA ....................................................................................................................................... 28 3.3.1 SCADA applications: ........................................................................................................... 28 3.4 SCADA system Parts .................................................................................................................. 31 ii

3.4.1 Master terminal unit (MTU) ................................................................................................. 31 3.4.2 Remote terminal units .......................................................................................................... 32 3.4.3 SCADA software’s ............................................................................................................... 33 3.4.4 Telemetry network ............................................................................................................... 35 3.4.5 SCADA Protocols ................................................................................................................ 38 3.4.6 Communication system ........................................................................................................ 39 CHAPTER 4: RELIABILITY IN POWER SYSTEM .......................................................................... 42 4.1. Reliability evaluation ................................................................................................................. 42 4.2. Distribution system reliability indices ........................................................................................ 44 4.2.1 System average interruption frequency index (SAIFI): ....................................................... 45 4.2.2 System average interruption duration index (SAIDI): ......................................................... 45 4.2.3 Customer average interruption duration index (CAIDI): ..................................................... 45 4.2.4 Average service availability (unavailability) index ASAI (ASUI): ..................................... 46 4.2.5 Expected energy not supplied index (EENS) ....................................................................... 46 4.2.6 Momentary interruption indices ........................................................................................... 46 4.3 Radial distribution network ......................................................................................................... 47 4.4 Network reliability equivalent ..................................................................................................... 50 4.5 Distribution system feeder model................................................................................................ 53 4.6 Switch Placement Optimization .................................................................................................. 53 CHAPTER 5: PARTICLE SWARM OPTIMISATION ....................................................................... 55 5.1 The basic model of PSO algorithm ............................................................................................. 55 5.1.1 Global best PSO ................................................................................................................... 56 5.2 Comparison of ‘gbest’ to ‘lbest’.................................................................................................. 57 5.3 PSO algorithm parameters .......................................................................................................... 59 5.3.1 Swarm size ........................................................................................................................... 59 5.3.2 Iteration numbers.................................................................................................................. 59 5.3.3 Velocity components ............................................................................................................ 59 5.3.4 Acceleration coefficients ...................................................................................................... 60 5.3.5 Inertia weight........................................................................................................................ 60 5.4 Applications of PSO .................................................................................................................... 64 5.5 Problem formulation for optimal placement of IEDs .................................................................. 64 5.5.1 BPSO to solve the IEDs optimal placement to improve the reliability of system ................ 66 CHAPTER 6: SYSTEM DESIGN AND DISCUSSION ...................................................................... 67 6.1 SCADA system design ................................................................................................................ 67 6.1.1 Communication system ........................................................................................................ 67 6.1.2 RTU ...................................................................................................................................... 68 6.1.3 Main control room ................................................................................................................ 69 iii

6.1.4 Other components needed .................................................................................................... 69 6.2 System Operation ........................................................................................................................ 71 6.3 Reliability analysis on the existing system.................................................................................. 73 6.3.1 Causes of power outage for Bahir Dar distribution system .................................................. 77 6.4 Predictive reliability assessment using optimal placement of IEDs ............................................ 82 CHAPTER 7: CONCLUSION AND RECOMMENDATION ............................................................. 86 7.1 Conclusion................................................................................................................................... 86 7.2 Recommendation......................................................................................................................... 87 Reference............................................................................................................................................... 88 Appendix-1: Interruption data for Bahir Dar distribution system ......................................................... 93 Appendex-2: Transformers reliability data for Ghion distribution feeder ............................................ 94 Appendex-3: Reliability line data for Ghion distribution feeder........................................................... 94

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LIST OF FIGURES Figure 1.1: Hierarchical levels for power system reliability assessment ................................... 2 Figure 2.1: Intelligent Electronic Devices ................................................................................ 15 Figure 4.1: Single Line Diagram of a Radial Distribution Network ........................................ 47 Figure 4.2: Single Line Diagram of a General Feeder ............................................................. 48 Figure 4.3: Reliability network equivalent ............................................................................... 51 Figure 5.1: Flowchart for global best PSO ............................................................................... 58 Figure 5.2: Neighbourhood topologies ..................................................................................... 62 Figure 6.1: overall hardware system architecture ..................................................................... 71 Figure 6.2: operation sequences when a fault occurs. .............................................................. 72 Figure 6.3: Interruptions frequency of Bahir Dar II feeders for 2013-2015............................. 74 Figure 6.4: Interruption Duration of Bahir Dar II feeders for 2013-2015 ................................ 75 Figure 6.5: Interruptions frequency of Bahir Dar I feeders for 2014&2015 ............................ 76 Figure 6.6: Interruption Duration of Bahir Dar I feeders for 2014&2015 ............................... 76 Figure 6.7: Major causes of permanent interruption rates for selected feeders ........................ 78 Figure 6.8: Total momentary interruption rates........................................................................ 78 Figure 6.9: Reasons for average frequency interruption of Bahir Dar II substation ................ 79 Figure 6.10: Reasons for average frequency Interruption of Bahir Dar I substation ............... 80 Figure 6.11: Reasons for average interruption duration for Bahir Dar II substation................ 80 Figure 6.12: Reasons for average interruption duration of Bahir Dar I substation .................. 81

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LIST OF TABLES Table 1.1: Comparison between traditional distributions and smart grid………………………6 Table 3.1: Specification of SCADA/HMI automation package programs…………………….34 Table 3.2: Transmission Mode………………………………………………………………..37 Table 6.1: Reliability indices of Bahir Dar substation I and II…………………………………73 Table 6.2: Comparison of Bahir Dar distribution system with other utilities…………………74 Table 6.3: Reliability Indices for IEDs optimal placement……………………………………84

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LIST OF ACRONYMS AND SYMBOLS ASAI

Average service availability index

AENS

Average energy not supplied

AI

Artificial intelligence

BPSO

Binary Particle Swarm Optimization

CAIDI

Customer average interruption duration index

CENS

Cost of expected energy not supplied

DA

Distribution automation

DG

Distribution generation

ECOS

Expected interruption cost

EENS

Expected energy not supplied

GAG

Global Agent

IED

Intelligent electronic device

LAG

Local Agents

MAIFI

Momentary average interruption frequency index

MTTF

Mean time to failure

MAS

Multi agent System

PSO

Particle Swarm Optimization

SCADA

Supervisory control and data acquisition

SG

Smart grid

SAIFI

System average interruption frequency index

SAG

Switch Agents

SAIDI

System average interruption duration index

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SYMBOLS λ

The expected failure rate

U

Annual outage time

r

Expected repair rate

La

Average connected load

N

Total number of customers served

MWh Mega Watt hour KV

Kilo Volt

C

Acceleration coefficients

V

Velocity

X

Position

W

Inertia weight

r1,2

Random number 1,2

Χ

Constriction coefficient

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ABSTRACT The distribution system is the core part of power system network which deliver power to the end user. Reliable power delivery plays a key role to profitability and customer satisfaction but achieving reliability of power supply has been a major challenge. Unreliable power supply does

not only slows down or damages production or results in shut down of plant but also leads to equipment damage, additional maintenance and the industries’ reputation for the quality of product. The Electric utility improves those reliability through tree trimming, construction design modification, installation of lightning arresters, use of animal guards, replacing overhead bare conductors by underground cables and so on. Rather than those conventional methods there are different intelligent methods among them using smart grid technique is becoming popular in recent time. A Smart grid is a conventional electric power system that has been equipped with advanced technologies for purposes such as reliability improvement, ease of control and management, integrating of distributed energy resources and electricity market operations. Ethiopian electric power utility has been trying to improve the delivery mechanism and quality of supply, but the power distribution system in the country has remained inadequate to meet customers demand both in required reliability and reduced safety risks of the public. Thus, this thesis tries to assess the reliability performance of the current Bahir dar distribution system using analytical methods for the years of 2013 to 2015 and suggest solutions of reliability

improvement in smart grid environment. In this thesis the techniques used to make a distribution reliable, adding intelligent switches (IEDs) is considered to isolate the failures and restore the energy to some consumers. However, such switches are costly. It needs proper planning for installation so that the utility company can make the most benefit at optimal cost. BPSO is used to determine optimum number and location of IEDs fulfilling reliability and economic constraints. 15 kV Ghion feeder overhead line distribution feeder of Bahir Dar city is selected as the test system under study. To make the system automatic by identifying and responding to faults, SCADA system has been designed. Historical reliability indices (SAIFI 235.848 int/yr and SAIDI 175.692 hr/yr) shows the Bahir Dar distribution system is unreliable as compared to standard practices and Ethiopia’s reliability requirement and predictive reliability results shows that the proposed technique improves the reliability index (SAIDI) by 48.98 % in comparison with exiting system. Key words: Distribution system, smart grid, SCADA, Reliability Assessment, IEDs, BPSO. ix

CHAPTER 1: INTRODUCTION 1.1. Background An electrical utility company engages in producing and providing electricity to its customers. A typical utility contains a large interconnected power system consisting of generation plants, substations, transmission lines, distribution systems and the load. The generation plants and load are normally located geographically far from each other and they are connected by transmission lines which may go through desert, jungles or even mountains. From generating plants to load, voltage quality, power loss and reliability are challenging issues in electrical utility industry. Electric power distribution systems are responsible for delivering the electrical energy from the bulk power systems to the end users but aging infrastructures, poor design and operation practices, radial operating status and high exposure to environmental conditions are the main contributor of reliability problems in electric power distribution systems. To improve customer reliability problems of power distribution system different methods can be used. Performing distribution system reliability assessment is used to identify the relevant improvement techniques to get better reliability of the system. The reliability analysis is an essential study for the design, operation, maintenance, and planning of the power system. For example, with a specific reliability requirement, an optimum maintenance strategy can be determined to minimize the operation cost. In fact, the maintenance influences the deterioration process, failure rate, and reliability of the components and the system, accordingly. In order to study the reliability of a power system, three hierarchical levels have been defined as shown in figure 1.1. The reliability of the power generation is studied through hierarchical level one (HL1). The reliability of a composite generation and transmission system is studied using HL2. Finally, the reliability of the whole system including generation, transmission, and distribution system is evaluated using HL3. Typically, in reliability evaluation of a power distribution system dealing with the interruptions, three key factors should be considered: 1) frequency of the interruptions; 2) duration of the interruptions; and 3) severity or extent of the interruption. The first two factors are important from

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both customer and utility perspectives, and the third factor could represent the number of the customers affected or the priority of their loads.

Figure 1.1: Hierarchical levels for power system reliability assessment Distribution system is evaluated based on its reliability and reliability is evaluated by reliability indices. Distribution system reliability evaluation is a measure of continuity and quality of power supply to the consumers, which mainly depends on interruption profile, based on system topology and component reliability data. Different reliability parameters are used in the distribution system in order to measure the system reliability indices. There are a number of indices for evaluation of the reliability throughout the power system. IEEE has developed a number of standards to include reliability related definitions and evaluation indices; IEEE Standard 762 is for generation reliability indices ; IEEE Standard 859 includes transmission facility reliability indices ; and IEEE Standard 1366 is for distribution reliability indices [7]. Electric utilities have traditionally improved the distribution system reliability through simple measures such as tree trimming on a regular basis, installation of lightning arresters, use of animal guards, replacing overhead bare conductors by covered conductors or underground cables, protection scheme modification, and so on. In addition to this there are other conventional solutions for reliability improvement and also there are other advanced reliability improvement measures that now a days are categorized as smart grid Technology. 2

1.1.1 What’s smart grid? The term “smart grid” or ‘’intelligent grid’’ comes from the clue showing that the system achieves the degree of high power quality and reliability in stable working mode. Though many literatures define it in different ways, the ideas the definitions convey is how the smart grid is very stable, efficient and reliable system. Generally speaking, there is no specific or unique definition of smart grid. According to the U.S. Department of Energy (DOE), a Smart Grid is defined as an electricity network that can intelligently integrate the behavior and action of all users connected to it through communication, computational ability control and information technologies in order to enhance efficiency, reliability, economics and sustainability of electricity services. In other words, it is an electrical grid that is the integration of electric infrastructure and information technology [1]. In a broad sense, the term “smart grid” is referred to a conventional electric power system that has been equipped with advanced technologies for purposes such as reliability improvement, ease of control and management, integrating of distributed energy resources and electricity market operations [7]. Smart grid technology includes the application of automation and intelligent controls to power systems, and it includes several significant characteristics [2], including: 1) increased use of digital control and information technology with real-time availability; 2) dynamic optimization relating to grid operability; 3) inclusion of demand side response; 4) demand side management strategies; 5) integration of distributed resources including renewable and energy storage; 6) deployment of smart metering; 7) distribution system automation; 8) smart appliances and customer devices at the point of end use. The most significant goals in the application of the smart grid are to improve safety reliability inside grid, but its implementation doesn’t come without change in distribution structure. Recent studies show that smart grid technology can improve the reliability of electric power systems to increase protection efficiency, and enhance fault detection, isolation, and restoration in the grid. This will reduce the duration of outages and the number of customers impacted by these outages. Furthermore, smart grid technology will decrease power line loss and energy usage to improve system efficiency. 3

Concerning Smart Grid, Advanced Distribution Automation (ADA) is an important building block. ADA employs automation technology and digital control of electrical distribution systems to improve safety, reliability, and self-healing enablement as compared to a classic distribution system. There are many factors that degrade the reliability of the distribution system. The major reason is faults. There are various types of faults that commonly occur in the distribution system. Different protective devices are used in the distribution system in order to locate and isolate faults. Reliability of the distribution system is proportional to the average time taken to restore power. Hence, proper coordination between protective devices must be assured to speed the restoration process which will improve the reliability of the system significantly. There are various methods to speed up the restoration process in order to improve the reliability of the distribution system. One of the methods is to use automatic switches. The power utility company is deploying feeder automatic switching devices like intelligent electronic devices (IEDs). IEDs provide self-healing, automatic restoration as well as supervisory control and data acquisition (SCADA) functionality. These automated capabilities make implementation of fault detection, isolation and restoration (FDIR) faster. From the perspective of the distribution network, a reliable distribution automation system is the key to enable autonomous smart distribution system operation to any changes, such as timevarying load demands, unexpected faults and planned actions, and to ensure the efficiency, reliability and optimality during distribution network operations. A distribution network can change its topology by opening or closing switches to optimize system operation, isolate faults, and to restore the supply during outages due to contingencies. In addition, the change of topology can improve load balancing between feeders by transferring loads from heavily loaded feeders to other feeders, thus improving voltage levels, reducing losses and increasing levels of reliability. It is also possible to reduce average customer outage times, annual unavailability and expected unserved energy by distribution system automation. In recent years, new methodologies of distribution network reconfiguration have been presented, exploring the greater capacity and speed of computer systems, the increased availability of system-wide data, and the advancement of automation, in particular supervisory control and data acquisition (SCADA). With the increased use of SCADA and distribution automation using switches and remote controlled equipment, distribution network reconfiguration becomes more viable as a tool for real-time planning. 4

The implementation level of distribution automation (DA) depends upon the need, one of which could be basic up grading of manual switching scheme with remote control or a fully automated system integrated with intelligent electronic devices (IEDs). As DA in one way will assist in automatically monitoring, protecting and controlling switching operations through IEDs to restore power service during fault by sequential events, DA is required and its latest version in terms of the new automation principles and techniques is also needed [3]. Thus DA will assist in maintaining better operating conditions and restore the network back to normal operations in case of faults. Automation of the distribution network therefore significantly increases the reliability of the system by isolating a fault and reconfiguring the system in a very short period of time. However, the cost associated with the installation of the automatic switches is very high [3]. The installation of more automated devices will increase the cost tremendously. Therefore, proper planning must be done for the installation of such automatic switches so that the utility company can make a significant benefit. Usage of the optimal number of switches at optimum location of the distribution network can give a more reliable and economic system. However, the selection of an adequate number of manual and automatic switches and the optimal placement of them in the distribution networks is a difficult task [4]. The selection of the number of automated switches and their locations depends on the customers connected, reliability cost, installation and maintenance cost. Therefore, proper Economic analysis should be done which will take care of the reliability improvement by minimizing the customer interruption, the switches and the maintenance costs. Distribution system reliability cost/worth assessment can be used to study the reliability worth of different distribution reinforcements. These include the selection of the optimal distribution configuration, the optimal number of switches and their locations. Assessment of reliability cost/worth in distribution facilities has a very direct association with the actual customers served by these facilities. In reliability cost/worth concept the system cost will generally increase with higher investment cost in equipment and facilities which provide higher reliability. On the other hand, the customer interruption costs due to higher reliability will decrease. The total cost to society is the sum of these two costs. There is a minimum point in the resulting total cost curve which indicates the optimal target level of reliability. Reliability worth/cost analysis is performed to find this optimal point [38]. 5

It is difficult to directly measure reliability worth. An indirect measurement of reliability worth can be obtained by evaluating the costs associated with customer service interruptions. The interruption cost at an individual customer load point is dependent on the type of customer, the load curtailed, the duration of interruption and the time of interruption. 1.1.2 Traditional power grid versus smart grid The smart grid has much distinguishing benefits over the traditional Grid advancing into new level of power transmission and distribution. The aspects of handling uncertainties, failure prediction and automatic system control make easier power restoration. Data acquisition, and information transfer and gathering attain level of stability with digital bi-directional communication among system interconnectivity while the existing distribution operates in the level of electromechanical devices and analog communication. The smart grid answers the question of reliability, which is the concern of this thesis, with high priority to customers enabling rapid restoration reconnecting as many customers as possible. Table 1.1: Comparison between traditional distributions and smart grid Existing grid

Smart grid

Electromechanical

digital

One way communication

Two way communication

Centralized generation

Distribution generation

Manual monitoring

Self-monitoring

Manual restoration

Self-healing

Slow response to power quality

Power quality a priority

Limited customers

Many customers given priority

Vulnerable to attacks

More or less secured from attacks

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1.2 Statement of the problem The existing Ethiopian grid has various problems that affect the reliability of the distribution system including poor protection system, poor infrastructure, line loss, efficiency, voltage profile, high interruption rate etc. are the most common ones. Because of above causes power blackouts or power shading have increased in recent times. Many costumers complain that the frequency as well as the duration of power cuts has also risen, with outages lasting (permanent) many hours or even days. The problem is particularly more in industrial areas, more so than residential customers. Because of this customer satisfaction is becoming low. According to medium voltage feeders interruption report of Ethiopian Electric Utility on the area covered by this thesis Bahir Dar radial distribution network, monthly average number of interruption (frequency) and duration of interruption for 2013, 2014 and 2015 are 23.84 interruptions and 20.74 hr. respectively. The main Cause of the interruptions are Distribution Permanent Earth Fault (DPEF), Distribution Permanent Short Circuit (DPSC), Distribution Temporary Earth Fault (DTEF), Distribution Temporary Short Circuit (DTSC) and Operational (OP) i.e. When medium voltage feeders are interrupted voluntarily for maintenance ,Load transfer ,new transformer erecting etc. are the most common ones. The power system is composed of many elements that are exposed to failures and require removal from operation for the purpose of maintenance. It is not possible to prevent all component failures in the electric system but, it is possible to minimize it. To find relevant alternative improvement techniques for this problem historical reliability assessment is an important part of the decision base. The Electric utility improves those distribution system reliability through simple measures such as tree trimming on a regular basis, construction design modification, installation of lightning arresters, use of animal guards, replacing overhead bare conductors by covered conductors or underground cables, protection scheme modification, and so on. Rather than the above methods other conventional and intelligent methods are there to improve the reliability performance of the distribution system. Among the intelligent methods using smart grid technologies are becoming popular in recent times. Thus, this thesis tries to assess the reliability performance of the current Bahir dar distribution system and suggest solutions of reliability improvement in smart grid environment.

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1.3 Objectives 1.3.1General Objective The main objective of this study is: Analyzing the reliability performance improvements to be gained by applying a part of smart gird technology to Bahir Dar distribution system. 1.3.2 Specific Objective 1. To assess historical reliability of Bahir Dar city distribution system. 2. To design SCADA automation system for Bahir Dar distribution system. 3. To determine optimal placement of intelligent electronic devise by using appropriate AI based optimization method which can improve system reliability and minimize the total system cost of the power distribution systems on selected 15KV feeder. 4. To analyze the reliability improvements of the selected 15 KV feeder of Bahir Dar distribution system with the applied smart grid devices. 5. Draw relevant conclusions and recommendations.

1.4 Significance of this study 1. Show the reliability performance of the existing distribution system. 2. Provide the reliability improvement method for Bahir Dar distribution system. 3. Decrease environmental impacts:-Because of power interruptions costumer uses alternative energy e.g. residential customer’s uses sources such as biomass or charcoal for cooking purposes but they are bad for health besides the environmental negative impact. 4. This study can be a base for future investigation of smart grid related to power interruption, power quality and other problems in Bahir Dar city and others.

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1.5 Expected Outcomes of the study The study is expected to identify the reliability causes of Bahir Dar substation systems. The historical reliability performance of the Bahir Dar distribution system will be analyzed and conclusion will be derived based on the analysis. For reliability improvement, optimal number and placement of smart device will be cared out for selected 15 KV Ghion Feeder. Optimized number and locations for smart switches placement will be achieved in order to improve system reliability and minimize the total system cost. SCADA system will be designed which controls the optimally placed smart devices in order to make the system automatic in fault identifying and clearing. 1.6 Scope 

The thesis is neither aimed at full smart grid system implementation nor to the study of the Ethiopian grid in general.

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The SCADA system design will include the appropriate selection of SCADA parts such as communication system, main control room equipment’s, software needed and operator display structure for Bahir Dar distribution (no modelling of the above SCADA parts).

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The frequency and duration of interruption are recorded on a feeder level thus limits to allocate the exact failure rate and repair times of each component.

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Out of eleven outgoing main feeders in Bahir Dar distribution substation, the predictive reliability analysis is carried out to only one of the feeders, namely Ghion outgoing feeder.

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In predictive reliability analysis the demand side costs associated with energy not supplied are considered. Since Electric power utility company is not forced to pay penalties for customers associated with power interruptions.

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The implementation of this thesis is limited on computer simulation.

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1.7 Thesis Outline This Thesis has a total number of seven chapters and the discussion of the study is organized as follows: Chapter One provides a brief description about back ground, statement of problem, objectives, scope and contributions of the research. Chapter Two discuses about smart grid technologies, literature reviews on reliability improvement methods proposed by different researchers such as network reconfiguration with and without distribution generator, smart grid distribution system and optimal placement of automatic /manual switches in distribution system. Chapter Three discuses about categories and benefits of distribution automation system, SCADA system parts (master terminal unit, remote terminal units, communication systems, telemetry networks, SCADA protocols and soft wares). Chapter Four describes reliability in power system, distribution system reliability evaluation techniques and indexes, network reliability equivalent, distribution feeder modeling and switch placement optimization. Chapter Five particle swarm optimization and problem formulation for optimal placement of IEDs switches in distribution system are presented. Chapter Six contains SCADA system design for Bahir Dar distribution, results of historical reliability assessment using reliability indices, major cause and reasons of interruption of Bahir Dar distribution system and the results of predictive reliability assessment using optimal placement of IEDs in Ghion feeder are discussed in detail. Chapter Seven presents conclusions that have been derived from this study, followed by recommendations for further study and at the end reference and appendix are also included.

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CHAPTER 2: LITERATURE REVIEW A smart grid is a version of the future power grid that employs advanced/intelligent equipment’s and services together with intelligent monitoring, control, communication, and smart protection. It is referred to as a revolution in the future of electric power grids because by using applicable technologies, it is a modern and integrated system. With increased energy demands and the expansion of renewable energy sources, power grid systems must be moderated and improved. Quality of power delivery is a major goal of the smart grid that will provide a variety of needs and options at different costs. Furthermore, smart grids will provide advanced monitoring and control by employing intelligent equipment such as digital sensors, electronic switches, smart energy metering, and intelligent and advanced communication systems. Its data acquisition and control systems include interactive software, real time control, and power flow analysis. All different types of renewable energy sources will be interconnected with the energy grid system to improve quality, reliability, and stability by using intelligent and advanced devices. Providing advanced technology such as the smart grid requires a smart and intelligent protection system to improve the efficiency of power delivery to customers, and to reduce outages. Employing the smart grid allows energy consumers to be active participants by providing information and options to control the electric demand balance. 2.1 Smart grid technologies A smart grid can be described as containing four essential building blocks called layers. Power is converted, transmitted over long distance, stored from renewable energy sources, and consumed in loads in what is called the physical layer. The sensor/Actuator layer is the sensor system, which includes current transformers (CT), voltage transformers (VT), digital relays, phasor measurement units (PMUs), smart meters, temperature, and IEDs used to provide a reliable, secure, and welldeveloped system. The communication layer, which plays a major role in collecting information, propagates all retrieved data and information from optimal sensors and intelligent electronic devices (IEDs). The final layer is the decision intelligence layer. It processes information and generates control signals received from sensors that require change in the state of the grid or communication systems. The smart grid technologies can be categorized in the following five key areas [7]:

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Integrated Communications – High-speed, fully integrated, two-way communication technologies will make the smart grid a dynamic, interactive “mega-infrastructure” for real-time information and power exchange. Open architecture will create a plug-and-play environment that securely networks grid components to talk, listen and interact.

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Sensing and Measurement – These technologies will enhance power system measurements and enable the transformation of data into information. They evaluate the health of equipment and the integrity of the grid and support advanced protective relaying.

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Advanced Components – Advanced components play an active role in determining the grid’s behavior. The next generation of these power system devices will apply the latest research in materials, superconductivity, energy storage, power electronics, and microelectronics. This will produce higher power densities, greater reliability and power quality, enhanced electrical efficiency producing major environmental gains and improved real-time diagnostics.

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Improved Interfaces and Decision Support– In many situations, the time available for operators to make decisions has shortened to seconds. Thus, the smart grid will require wide, seamless, real-time use of applications and tools that enable grid operators and managers to make decisions quickly. Decision support with improved interfaces will amplify human decision making at all levels of the grid.



Advanced Control Methods – Advanced control methods are the devices and algorithms that will analyze, diagnose, and predict conditions in the smart grid and determine and take appropriate corrective actions to eliminate, mitigate, and prevent outages and power quality disturbances. To a large degree, these technologies rely on and contribute to each of the other four key technology areas. For instance, they will monitor essential components (Sensing and Measurements), provide timely and appropriate response (Integrated Communications; Advanced Components), and enable rapid diagnosis (Improved Interfaces and Decision Support) of any event.

2.1.1 Phasor measurement units (PMUs) A phasor measurement unit (PMU) utilizes time synchronization to take real-time measurements of multiple remote points on a grid. These devices are considered one of the most important

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measuring devices in a smart grid. They can be used as dedicated devices, or they can be incorporated into a protective relay or other device. PMUs are used to detect un-stationary waveform shapes that generated by faults, which is recognized mathematically and called a phasor. A PMU can measure 50/60 Hz AC waveforms of voltages, currents, and phase typically at a sampling rate of 6-60 samples per second. The analog AC waveforms, retrieved from voltage or current signals, are converted from an analog to a digital signal (A/D) for each phase. A phase-lock oscillator, which operates along with a global positioning system (GPS) is used as a reference source, is used to provide the high-speed synchronized sampling with accuracy of 1 microsecond. The consequential time of phasors can be transmitted to a local controller or remote receiver at rates from 6 to 60 samples per second [6]. 2.1.2 Fault locator schemes Fault locator schemes are devices and algorithms that are used to identify the location of a fault. Developed schemes for automatic fault location in the electricity distribution networks are generally operate based on a special fault locating technique. Most of the fault location techniques have been developed for power transmission systems. Few methods for the electric power distribution networks due to the following reasons. 

Variety of Conductors and Structures: Along a typical distribution feeder there are different cables, lines and configurations (cross-arm, twisted, spacer, underground, etc.); therefore, there is no linear relation between the line impedance and the distance between the fault location and the substation.



Lateral Branches: Unlike transmission lines, typical distribution feeders have several lateral branches. Thus, short circuits in different geographical locations can produce the same currents and voltages measured at the substation. Consequently, the fault location procedure may result in several different points as possible locations.



Load Distributed along the Feeder: The current measured at the substation during a fault includes a contribution given by the sum of the load currents at each node and, in contrast to transmission systems, it is impossible to estimate these currents accurately.



Modifications in the Feeder Configuration: Distribution networks are subject to constant modifications in their topology. As a result, any fault location algorithm must have access to a database, periodically updated, in order to give a better estimate of the fault point. 13

2.1.3 Distribution automation Distribution automation is a complete system that enables a utility to monitor, coordinate and operate the distribution network components in a real-time mode from remote locations. Distribution automation allows utilities to implement a flexible monitoring and control of an electric power distribution system, which can be used to enhance efficiency, reliability, and quality of the electric service. Flexible monitoring and control also results in a more effective utilization and life-extension of the existing distribution system infrastructure. An advanced distribution automation system has all the necessary components required for efficient fault management activities in the feeder and the substation levels. It can automatically perform the fault detection, isolation and service restoration activities without an intervention of distribution system operators. It can also identify the fault location and assist the control center operators and the repair crews during the fault management activities. 2.1.4 Dynamic voltage restorer Dynamic voltage restorer is a waveform synthesis device based on power electronics that is seriesconnected directly into the network by means of a set of single-phase insertion transformers .This device can be installed in strategic locations of an electricity distribution network to mitigate the effect of voltage sags on the customers. The dynamic voltage restorer cannot protect a load against an interruption. When the voltage of one or more phases of incoming supply drops below a preset threshold, this device injects a controlled amount of voltage into the affected phase or phases to boost the voltage of outgoing side back to a more suitable level. 2.1.5 Fault passage indicator Fault passage indicator is a device that can be located at some convenient point on an electricity distribution network to give an indication as to whether the fault current has passed the point where it is located or not. It is able to distinguish between the fault current and the load current associated with the healthy feeder, and has some means of displaying its operation to a repair crew. The status of a fault passage indicator can be recognized remotely or by visiting its physical location. Usually, the status of an indicator used with overhead line networks is illustrated in the form of flashing indication. The status of such an indicator can be retrieved remotely from a short distance (a few meters) without the need to access the distribution substation to recognize its status. By using the 14

fault passage indicators, the repair crews waste less time to travel around the network in search for location of the fault. 2.1.6 Intelligent electronic devices Instrumentation & Control devices, which are built using microprocessors, are commonly referred to as Intelligent Electronic Devices (IEDs). Microprocessors are single-chip computers that can process data, accept commands and communicate information. Digital protective relays are primarily IEDs, using a microprocessor to perform several protective, control and similar functions. With available microprocessor technology, a single IED unit can now perform multiple protective and control functions, whereas before microprocessors a unit could only perform one protective function. A typical IED today can perform 5 to 12 protection functions and 5 to 8 control functions, including controls for separate devices, an auto-reclose function, self-monitoring function, and communication functions etc. It can do this without compromising security of protection (the primary function of IEDs) [8].

Figure 2.1: Intelligent Electronic Devices Many of today’s electric utility substations include digital relays and other Intelligent Electronic Devices (IEDs) that record and store a variety of data in relation to their control interface, internal operation and about the power system they monitor, control and protect. Nowadays, digital relays

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are widely replacing the aging electromechanical and solid state electronic component-type relays and relay systems. Digital relay’s popularity comes from their lower price, reliability, functionality and flexibility. However, the most important feature that separates a digital relay from previous devices is its capability of collecting and reacting to data and then using this data to create information. Such information includes. 1. Protection Data: Fault location and fault type, 2. Metering Data: Pre-fault, fault and post-fault currents and voltages, 3. Breaker and relay operation data, and 4. Diagnostic and historical data. IEDs can also run automatic processes while communications are handled through a serial port similar to the communication ports on a computer. Some examples of IEDs used in a power system are: • Remote Terminal Units (RTUs), • Digital fault recorders, and • Instrument transformers. For the last few years, the advancements in microprocessor based IEDs networked over high-speed communication networks using standardized communication protocols is leading the evolution of power system control technology. The use of existing standards and commonly accepted communication principles together with the new standards such as IEC 61850 and UCA provides a solid base for interoperability leading to more flexible and powerful protection and control systems [9]. 2.1.7 Feeder automation Feeder automation is an automatic control scheme that is used for automatic fault detection, isolation and service restoration in an electricity distribution network. When an electricity distribution network encounters with a permanent failure condition, there are basically two groups of affected customers. First group involves the customers that have to be waiting till the end of repair process of the faulted section before power restoration. In contrast, the second group includes the customers whose power supply can be restored through main or alternative supplies by means of proper switching actions. Usually, the number of customers in the second group is 16

much larger than that of the first group. For power restoration of the second group of customers, healthy sections of the distribution network should be isolated from the one that contains the fault. After that they can be restored from main or alternative supplies by means of appropriated switching actions. In the case of a manually operated distribution system, fault isolation and service restoration activities can just be accomplished after the fault is located by utility repair crews. However, by employing a feeder automation scheme, the interruption duration experienced by the affected customers can be reduced. Feeder automation schemes can automatically perform the necessary switching operations to isolate the faulted section from the healthy ones and restore power to as many as possible of the affected customers. Since the fundamental objectives of power utilities are to improve reliability and minimize operational costs and it requires dealing with the common problems. Several methods have been proposed to solve reliability problem. Among the solutions for the problems are optimal reconfiguration of feeders, optimal placement of switching device (manual and automatic), optimal placement of distributed generators etc. have been presented during the past decades. 2.2 Network reconfiguration Distribution feeder reconfiguration can be used for system planning as well as real-time control and operation. From an optimization perspective, network reconfiguration is a mixed-binary nonlinear optimization problem where binary variables represent the switch states and continuous variables model the electric network. However, even for a distribution system of moderate size the number of switching options is so large that conducting load-flow studies for all the possible options is computationally inefficient and impractical as a real-time feeder reconfiguration strategy. As a result, during the past decades, numerous approaches have been proposed to solve reconfiguration problems. Merlin and Back [11] determined the network configuration with minimum or near-minimum line losses using a branch-and-bound type heuristic technique. According to their proposed method, all network switches are initially closed to obtain a meshed network. Then, network switches are opened one at a time until a new radial structure is reached, and the switch selected to open at each time minimized the losses of the resulting network. Merlin and Back’s work has been the foundation for all other network reconfiguration studies that have followed. However, there are

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several major drawbacks of the methodology including the assumption of purely active loads represented by current sources, neglecting voltage angles and network constraints. McDermott et al. [12] developed a reconfiguration algorithm starting with all network switches open, and a list of candidate switches is built at each step and the candidate with minimum loss increment is closed at that step. This proposed reconstruction procedure is repeated until a connected, radial network is achieved. Because the number of normally closed switches is much larger than the number of normally opened switches, more load flow calculations are needed in this approach than other sequential opening methods. Enrico Carpaneto et al. [13] Studied the performance of three iterative improvement algorithms and two algorithms based on the simulated annealing method for minimum losses reconfiguration of distribution systems has been compared by carrying out extensive tests on large real distribution systems. Mohamed Magdy and Hosam Kamal [14] presented genetic algorithm for reconfiguration of distribution system for loss minimization by taking into account all constraints including ac load flow. The method succeeded in handling network configuration with minimum losses even after fault conditions. Ganesh. Vulasala et al. [15] presented reconfiguration of unbalanced distribution systems for loss minimization using GA and showed that genetic algorithm can be used to find the configuration of the unbalanced distribution networks to minimizes overall power losses of the distribution system and also showed the result of GA is better than other conventional methods used by other scholars for loss reduction by using feeder reconfiguration. A.Y. Abdelaziz et al. [16] Performed real power loss minimization by using Modified Particle Swarm Technique. In this optimization approach penalty function is applied to limit the position and velocity range and uses bus incidence matrix to chick radiality of the network. The work verifies the effectiveness of the proposed method, by comparative studies conducted on two test systems with encouraging results. Wu-Chang Wu and Men-Shen [17] proposed binary coding Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration to minimize real power loss and to distribute load on feeder evenly; the operators of typical PSO algorithm have been reviewed and redefined in this research to fit the application of distribution feeder reconfiguration considering all the constraints. 18

Tamer M. Khalil and Alexander V. Gorpinich [18] proposed a method to solve the distribution network reconfiguration problem by modifying the binary particle swarm optimization; this algorithm, called selective particle swarm optimization, the proposed technique divided to two steps. The first step is to simplify the network and specify the number of dimensions and search space for each dimension. The second step is to apply the selective particle swarm optimization to choose the optimal branch from each dimension to be opened. Since the main function of optimal network reconfiguration is to maximize the power supply using existing breakers and switches in the system. The ability to quickly and flexibly reconfigure the power system of an interconnected network of feeders is a key component of Smart Grid. New technologies are being injected into the distribution systems such as advanced metering, distribution automation, distribution generation and distributed storage. With these new technologies, the optimal network reconfiguration becomes more complicated. 2.3 Reconfiguration with distributed generator Wardiah Mohd and Hazlie Mokhlis [19] proposed a network reconfiguration method for distribution network connected with DGs using the PSO algorithm. The proposed method is able to produce an optimum configuration in network distribution and at the same time yield the optimal size of DG and reduce power loss. But they don’t consider the position and capacity of DG connected to distribution system.The proposed PSO also improves convergence characteristics and less computation time as compared with GA method. D-kavitha et al.[20] Hybrid Genetic Algorithm and Particle Swarm Optimization Search (HGAPSO) was presented for optimal place and size of Distributed Generators (DGs) in distorted distribution system by considering the total real power losses and THD (total harmonic distortion) should be minimized simultaneously. They used a hybrid GA–PSO approach to get both advantages of genetic algorithm and particle swarm optimization. The optimal location of DG needs an integer-based optimization algorithm so GA has been chosen to play this role because of its attractive quality. The answer obtained from GA solution is used in PSO algorithm to optimize the sizing for DG. Since PSO has the fast convergence ability which is a great attractive property for a large iterative and time consuming problem.

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K. Nagaraju, et al, [21] developed a heuristic approach reconfiguration method for radial distribution systems. The proposed scheme is based upon maximizing the line maximum laudability. The algorithm gives the solution with a few numbers of switching operations. Seyed Hossein et al.[22] proposed algorithm for handling DGs in a Power flow program, which is developed for practical unbalanced distribution network and shows the location and DG control mode (as PV and PQ mode) not only decrease voltage unbalance factor and voltage deviation but also decrease total power loss. But in this paper the DG placement is not optimal it selects the position by conducting load flow where DGs as PQ operation are modeled as constant PQ-load. After this, load-flow study without considering PV node will be done (Nodal Current Calculation, Backward Sweep and Forward Sweep Handling DGs as PV Node) and select the candidate based on the voltage gained by the load flow. Sridhar Chouhan et al. [23] proposed a multi-agent based system to reconfigure the smart distribution system when faults occur. There are mainly two categories of agents available in the proposed Multi agent System (MAS) architecture, which are Local Agents (LAG) and Global Agent (GAG). Load Agents (LAGs) and Switch Agents (SAGs) fall into the category of local agents. These agents represent loads and switches in the power distribution system respectively. The reconfiguration algorithm is embedded inside the GAG, such that whenever it receives fault location it starts reconfiguring the network to reroute the power to critical loads. The objective function of reconfiguration is to always supply the critical load without exceeding the line capacity while maintaining the voltage limits. The simulation results show that multi agent based system can reconfigure the system in a way that it always try to supply all the critical loads in the network. 2.4 Smart grid distribution system In [6] proposed self-healing protection system using advanced and intelligent devices, such as IEDs and PMUs to design proficient and accurate self-healing protection systems which are associated with future smart grids. Simulation results showed that the self-healing protection system could monitor and control the grid during outages to locate the fault, isolate the affected area and restore electricity to the unaffected area. But the thesis don’t show the required number and placement of intelligent devices on the system. E.Vidya Sagar and P.V.N.Prasad [25] described the concept and characteristics of smart grid distribution systems, basic difference between conventional and smart grid distribution systems, 20

functional management and reliability evaluation of smart grid distribution systems and the reliability indices of a radial distribution system for conventional (non-automated),automated and smart grid configurations are calculated and the results are compared .The result they got showed that smart fault detection, isolation and restoration process is very fast in the smart grid distribution system than others. Kevin Mets et al. [26] is studied a comprehensive overview of various tools and their characteristics, applicable in smart grid research: cover both the communication and associated ICT infrastructure, on top of the power grid. It discussed design challenges of smart grid simulators and three types of simulators in the smart grid area. Use of standards and multi-agent based modeling in smart grid simulation was discussed but don’t show simulators by cabining both the communication and power system by considering the real power grid. Fangxing Li et al. [27] studied a unique vision for the future of smart transmission grids in which their major features are identified. The features and functions of each of the three functional components, smart components, i.e., smart control centers, smart transmission networks, and smart substations. As well as the enabling technologies to achieve these features and functions, are discussed in detail in this paper but don’t analyze the performance of the transmission system or distribution system by applying smart grid technologies. Raman kumar Bhamboria1 and Ram Avtar Jaswal [28] studied Challenges which has to be meet to improve the power distribution system by upgrading of primary feeders from a radial to a loop configuration in Smart Distribution system. To study this it introduce design scheme of a conventional power distribution system configuration that adopts distribution automation and studying the challenges for its implementation. E. M. Natsheh et al. [29] developed a model of smart grid-connected hybrid system using Perturb and observe algorithm. They used Mat lab\Simulink software for modeling and real data gathered from power system located in central Manchester. From the result the proposed model and its control strategy offer a proper tool for smart grid performance optimization but the study don’t analysis performances of the model by considering the reliability improvement of the system either by considering distribution system or grid. Haughton and Heydt [30] discussed the importance of having a rapid reconfiguration algorithm in the smart distribution systems which has the roles of the average interruption duration reduction 21

and the un-served energy minimization. In order to achieve this distribution system needs automatic switching devices. Boštjan Blažič Igor et al. [31] presented schedule and dispatch of power using the smart grid technology (automation for distributed systems), for the usage of conventional energies as a secondary source and develop a model which consists of feedback closed loop control to perform the demand management during normal operating conditions, islanding the micro grid during the fault conditions and scheduling. Daniel bernardon et al.[34] proposed a multicriterial decision making method, AHP - Analytic Hierarchic Process for automatic reconfiguration of distribution network in real-time is developed, from the standpoint of Smart Grid. The methodology used assumption that only the remote controlled switches are analyzed and it consists in applying the branch exchange algorithm twice: first, to determine the individual result of each configuration test, and after to determine the final combination of configurations and For obtaining load flow values in the distribution system, the proposed algorithm implements the classical backward/forward sweep method From there result they conclude the feasibility of the methodology. Yousaf H. Khattak et al. [35] Presented a Smart Energy Management System designed and developed for monitoring an efficient load management of electric utility and photovoltaic power system .The design consists of an Energy Management Center and Field Programmable Gate Array The system is designed on Xilinx ISE using Spartan 3AN FPGA and LAB VIEW. The proposed system is cost effective which improves energy efficiency and gives an incentive to user. As shown from the above papers minimizing electric power loss of distribution system at normal condition is a very important thing for economical operation of power system. But there are factors that affect the implementation of power loss minimization using feeder reconfiguration for different existing practical system especially like different parts of Ethiopia. The first thing is most of the distribution system is not automated (have no remote control switch) therefore reconfiguration will take some time for the technical team to go and work on it. The second and the main factor for the implementation of feeder reconfiguration is: it needs real time data as an input to process and make a decision therefore SCADA system is required. The other factors that highly affect the performance of reconfiguration process are: the amount and location of the sectionalizing and tie switches.

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2.5 Optimal placement of switches In the last decades, researchers have made several attempts to improve the reliability of a distribution system using optimal switch placement techniques. K. Alekhya, P. Murthy and C. Bhargava [5], the reliability assessment of a distribution system is done on the basis of cost analysis. Two stage restoration (partial automation) is used and the objective is to minimize the cost due to energy not supplied (ENS). C.-S. Chen, C.-H. Lin and H.-j. Chuang [36], proposed the immune algorithm (IA) to figure out the optimal placement of switching devices by minimizing customer interruption cost (CIC) and investment of line switches. The reliability index of each service zone is derived to solve energy not served (ENS), and then customer interruption cost is determined according to customer type and power consumption. A. Kazemi and F. T. Asr [37] uses the reliability indices in order to derive the optimum location of automated switches in the distribution network. The calculation of reliability is presented, and the influence of automation on reliability is discussed in detail. Finally, the best configuration of the switches is derived using Genetic Algorithm (GA). P. Jintagosonwit et al. [32] proposed method for improving reliability on electrical distribution system by relocate feeder switches and pole-mounted RTUs in the main feeder, using Genetic Algorithm as an optimization tool. However, no location and cost considerations are taken into account. G. Celli and F. Pilo [33] studied the optimal allocation of the switching devices and importance of optimal allocation and automatic switching devices. In the allocation of automatic switching devices most papers don’t consider cost but this paper consider costs. In [39] studied optimization technique to find optimal switch placement and number of additional switches to improve the reliability of the system at a reasonable cost. Both overcurrent relays and distance relays and their coordination were considered in the optimization problem. In [9] the reliability assessment of Bahir Dar town distribution system has studied. In this thesis the reliability assessment of Bahir Dar distribution system on 15 kV selected four feeders is done. Simulation method (Monte Carlo simulation systems) using ETAP software is used to assess and simulate the overall behavior of the distribution system reliability indices.

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In [10] optimal capacitor allocation using particle swarm optimization in Bahir Dar electric distribution has studied for loss minimization. In [38] studied the historical and predictive reliability assessment of Bahir Dar distribution system has studied with the standardization of reporting distribution interruptions. The predictive reliability was analyzed by optimal placement of manual sectionalize switch not by automatic switches and the study don’t consider the automation of the distribution system. This research therefore aims to solve the reliably of Bahir Dar distribution system by optimal number and placement of intelligent electronic devices on selected feeder by fulfilling reliability and economic constraints and designing distribution SCADA system that uses IEDs as remote terminal units will be cared out.

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CHAPTER 3: DISTRIBUTION AUTOMATION Control and automation of electricity networks play the key role in electricity business environment for different enterprises of production, supply, bulk transmission, delivery or distribution and metering. Distribution automation (DA) generally covers functions for safety and protection as well as operation and control as well it offers functions for business and asset management. Companies implementing DA achieve reliability improvement, operating efficiency and extend of asset life amongst other benefits. Automation for operations in entire distribution system is referred to the DA concept. DA concept is an umbrella term covering the complete range of functions from protection to network control system (NCS), generally called SCADA, and applications applied. Essential systems in DA are NCS, substation automation (SA), feeder automation (FA) and AMR supported with distribution management system (DMS). Traditionally electricity distribution is handled by primary processes and management processes and therefore DA is applied within a structured control hierarchy with different layers of the network. The processes can be divided up into horizontal levels by their locations in the distribution network. The levels are the LV network (or consumer), MV network (or distribution), bay, substation, control (or net-work) and enterprise (or utility) level. With the increase in electrical power demand, the corresponding increase in network complexities necessitates enhanced levels of automation and communication for remote control as well as for management of power network, thus requiring the upgrading of the existing network infrastructure which comes along with numerous complicated changes. As a vital element of SG, DA facilitates the utilization of the advanced computer and communication technology and infrastructure to develop the management and operation of distribution network from a semi-automated approach towards a fully automated one. In the initial stages, the main driver of DA was improving efficiency but now it has advanced to improvement in reliability and quality of power distribution. In present days DA has received a vast nomenclature from different researchers and authors some refer its related systems as the Distribution Automation System (DAS). [6] calls it Intelligent Distribution Automation (IDA) and defines it as DA which takes advantage of advances in computing technology and communication to move the intelligence closer to the problems that need to be solved and this intelligence is reached through the utilization of the intelligent electronic 25

devices (IEDs) On the other hand, the DAS can be defined as a system that enables electric utility to monitor, coordinate and operate distribution components in a real-time mode from remote locations [54]. In general DAS includes all devices consisting of a number of components which contribute by some means to the automation and remote operation of the distribution network. 3.1 Categories of distribution automation DA, with its numerous capabilities and applications, can be implemented at different levels of the network and there are different ways to classify the automation functions which are Monitoring, Control, Measurement and Protection. In terms of location, DA functions can be classified into three key categories: 1. Secondary Substation (SS) Automation: The DA functionalities at the SS include: a) Substation equipment monitoring and control (local and remote) b) Transformer protection and Load-Tap-Changer (LTC) control c) DG incorporation d) Earth fault compensation e) Protection coordination f) Communication (upstream and downstream) 2. Feeder Automation (FA): The DA functionalities at the feeder include: 

Feeder automatic switching/sectionalizing and dynamic reconfiguration



Feeder voltage (through VAR control via capacitor banks and voltage regulator control)



FLISR



Optimal network reconfiguration o Set the optimal switch orders o Calculate load among the feeder lines which are redistributed

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Intentional (planned) islanding for island operation of part of the network i.e. Microgrid Management (MM)

Typically, the DA on transformer substation and feeder are integrated to share common monitoring and controlling equipment and devices and this forms the base for the thesis research.

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3. Customer (premises) Automation (CA): The DA functionalities at the customer level are quite extensive and include: a) Load control b) Real-time price signaling c) Remote meter reading and billing d) DR and LM as part of DSM Apart from the above mentioned features, there are several other functionalities of DA including but not limited to Outage Management System (OMS), Distribution State Estimation (DSE), Voltage/VAr Optimization (VVO), Electric vehicle (EV) integration, Load Forecast and Modelling and others. 3.2 Benefits of distribution automation Similar to the classification above, the distribution automation (DA) benefits categorized as well, which include but not limited to financial benefits, operational & maintenance benefits customer related benefits and others. There are far too many benefits of DA and it would be impractical to describe them all but the reliability improvement as part of operational benefits is being given the utmost priority as these days the reliability is being linked to financial compensation for the network operators. The latest role of DA also includes the demand side management (DSM) functions including Automatic Meter Reading (AMR), which was started to assemble those devices in Ethiopia by MITEC and Load Management (LM). Form the foundation of the integrated monitoring system infrastructure of distribution automation and to monitor, coordinate and operate them in distribution network in real-time from network Control Centre (NCC) leads to the formation of DAS which necessitates the extension of the SCADA systems from the generation and transmission i.e. from power system level to the distribution level. Distribution SCADA is the foundation of DA and a prerequisite for the realization of DAS. Moreover, for achieving the desired performance and reliability from the distribution network. From the perspective of the distribution network, a reliable distribution automation system is the key to enable autonomous smart distribution system operation to any changes, such as time-

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varying load demands, unexpected faults and planned actions, and to ensure the efficiency, reliability and optimality during distribution network operations. 3.3 SCADA SCADA is an acronym for Supervisory Control and Data Acquisition, which is a computer-based control system that is used for collecting and analyzing real-time data. SCADA systems are designed to collect field information, transfer it to a central computer facility, and display the information to the operator graphically or textually, thereby allowing the operator to monitor or control an entire system from a central location in real time. The majority of data in a power system is acquired from substations by means of SCADA system. Traditionally a SCADA system is built up by installing a remote terminal unit (RTU) to the substation which is connected to protection relays and auxiliary contacts of switches as well as to the central control system as a communications inter-face. SCADA offers functions like data acquisition, data processing, remote control, alarm processing, historical data, graphical human machine interface (HMI), emergency control switching and load planning for demand side management (DSM). The relay protection in a distribution system initiates corrective actions at malfunctions of network operation. Currently IEDs provide more functionality, performance and scalability than traditional protection relays. In addition to a large number of different protection functions IEDs provide control, measurement, power quality monitoring and condition monitoring for distribution network and its components. Control functions of an IED include position indications and control commands of switching devices like Circuit Breakers and dis connectors. Position information and control signals are transmitted over station bus and they can be used for inter-bay interlocking schemes. Measurements provided an IED are for example phase currents, neutral current(s), phaseto-phase or phase-to-earth voltages, residual voltage, frequency and power factor [41]. 3.3.1 SCADA applications: Following are the main application commonly used. 

Network Connectivity Analysis (NCA)

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State Estimation (SE)

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

Load Flow Application (LFA)

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Voltage VAR control (VVC)

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Load Shed Application (LSA)

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Fault Management and System Restoration (FMSR)



Loss Minimization via Feeder Reconfiguration (LMFR)

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Load Balancing via Feeder Reconfiguration (LBFR)

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Operation Monitor (OM)



Distribution Load forecasting (DLF)

Network connectivity analysis (NCA): The network connectivity analysis function provides the connectivity between various network elements. The prevailing network topology will be determined from the status of all the switching devices such as circuit breaker, isolators etc. that affects the topology of the network modeled. NCA runs in real time as well as in study mode. Real-time mode of operation uses data acquired by SCADA. Study mode of operation will use either a snapshot of the real-time data or save cases. NCA can run in real time on event-driven basis. The NCA also assists the power system operator to know the operating state of the distribution network indicating radial mode, loops and parallels in the network. Distribution networks which are normally operated in radial mode; loops and/or parallel may be intentionally or inadvertently formed. State estimation (SE) The State Estimation (SE) is used for assessing (estimating) the distribution network state. It shall assess loads of all network nodes, and, consequently, assessment of all other state variables (voltage and current phasors of all buses, sections and transformers, active and reactive power losses in all sections and transformers, etc.) in the Distribution network.

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Load flow application (LFA) In Power system the quantities of electrical real & reactive power and Voltages are complex quantities and the equations linking them are non-linear. At the load centers (buses) the quantities of power both real & reactive will be known and at the power generating points the real power and Voltage magnitudes will be available. The Load flow analysis helps to evaluate the unknown quantities at all the buses for a given network topology. Volt –VAR control (VVC) In electrical power system the reactive power can be generated at source generators or can be injected at the substations through Volt-Var systems. It is more appropriate to inject at substations rather than producing then at generator points and transporting them over long distances. Any power system always tries to optimize on the reactive power flow over their networks. The coordination of voltages and reactive power flows control requires coordination of Volt and the VAR function. This function shall provide high-quality voltage profiles, minimal losses, controlling reactive power flows, minimal reactive power demands from the supply network. Load shed application (LSA) The power delivery to the consumers interrupts with Demand-Supply problems, with demand being always higher than supply. The reasons for less Supply are several including the faults, in generation, transmission, distribution system, tripping of lines etc. In these situations the power system operator tries to distribute available power through Shedding of loads to consumers over small definite periods till he tides over the situation of loss of power. Fault management & system restoration (FMSR) Application The availability of data related to the breakers/ switches and the level of The Fault current flowing in the networks helps one to Manage & Restore the System in an event of fault. This application helps to provide the assistance to the power system dispatcher for detection, localization, isolation and restoration of distribution system after a fault in the system has occurred with the help of operating through the supervisory control available on SCADA.

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Loss minimization via feeder reconfiguration (LMFR) The switching operation during fault and requirement to supply power through alternate feeders in the distribution network modifies the feeder configuration topology. The information of network topology and availability of adjacent feeder networks can be useful in right selection of feeders with overall aim of reducing the line losses and maximum power delivery to consumers. This function identifies the opportunities to minimize technical losses in the distribution system by reconfiguration of feeders in the network for a given load scenario. 3.4 SCADA system Parts SCADA systems can be defined by its main parts that are: 1. One or more field data interface devices, usually RTUs, or PLCs, which interface to field sensing devices and local control switchboxes and valve actuators. 2. A communications system used to transfer data between field data interface devices and control units and the computers in the SCADA central host. The system can be radio, telephone, cable, satellite, etc., or any combination of these. 3. A central host computer server or servers (sometimes called a SCADA Center, master station, Master Terminal Unit (MTU) or Main Control Room (MCR)). 4. A collection of standard and/or custom software (sometimes called Human Machine Interface (HMI) software or Man Machine Interface (MMI) software) systems used to provide the SCADA central host and operator terminal application, support the communications system, and monitor and control remotely located field data interface devices. 3.4.1 Master terminal unit (MTU) At the heart of the system is the master terminal unit. The master terminal unit initiates all communication, gathers data, stores information, sends information to other systems, and interfaces with operators. The major difference between the MTU and RTU is that the MTU initiates virtually all communications between the two. The MTU also communicates with other peripheral devices in the facility like monitors, printers, and other information systems.

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The master station in a SCADA system does the following: • Gets field data by periodically reading and/or receiving data directly from the remote stations or through a sub master. • Provides coordinated monitoring and control over the entire system through its operator interface The main elements of the SCADA master station (or SCADA master) are Human Machine Interface (HMI), application servers, firewall, communication front-end (to communicate with RTU’s/data concentrators), and external communication server/M2M gateway (to communicate with other control centers).These elements are networked within the SCADA master via real-time dedicated LAN. The application servers include servers that support all energy management system (EMS) or distributed management system (DMS) applications. Redundancy is provided for the hardware and software elements of SCADA master (e.g., redundant LAN) and substations (e.g., redundant critical computer) as well as for the M2M communication network [42]. 3.4.2 Remote terminal units In the early application of monitoring and control systems, the interface between the power system and the control system was in a remote location. This interface was designated a Remote Terminal Unit or RTU. An RTU consisted of a cabinet or panel of terminals for the instrumentation and control wires, which connected it to the power system. The position of the power system switches and circuit breakers were monitored by auxiliary relays. When the relay was closed, the power system switch was closed and a current was present resulting in a binary 1 signal. When the relay and the switch were open the binary count was a 0. Analog values were obtained from potential transformers and current transformers connected to the power system buses and circuits. The RTU had analog devices to convert the analog values into binary values (usually 8 to 12 bits) [8]. Thus, the digital and analog input values from the power system could be sent as a series of binary values to the master station for display and analysis purposes. The auxiliary relays in the RTU used for controlling power system devices were addressable so the operator could select the address for a specific power system device and function, (open or close) and send the command to the RTU. The use of microprocessors provided the opportunity to greatly increase the capabilities of the RTU. These capabilities included time keeping, more complex and powerful protocols, individual point numbering, local logging and time tagging of events, higher communication speeds, multiple 32

communication ports and numerous other functions. But the complex and costly interface wiring continued to exist and kept costs relatively high [8]. 3.4.3 SCADA software’s There are many software packages in today‘s Information Technology (IT) market which enables engineers with moderate programming experience to build SCADA applications. SCADA server applications handle data archiving, alarm processing and events logging. Main parts of the SCADA system are the device driver (RTU drivers) and the database servers. SQL server from Microsoft Company is widely used for data archiving, alarm processing and events logging. SMTP server from Microsoft Company may be used to build email and SMS alarm messages to alert the operators about unacknowledged alarm events happened for longer time than adjustable set delay time. SCADA Database servers are one of most important used by SCADA system. Any of the database servers used to store and implement data as SQL server [41]. Then it is managed by the SCADA system designer to display the data to the operator simply and quickly using different ways of data management as alarms, SMS messages and reports. SCADA systems include a HMI which uses graphical interface to visualize the state system variables, change set points, alerts operators of critical condition and generate data trends. There are several software packages used for designing HMI and SCADA. WINCC from SIEMENS, Cimplicity HMI from General Electric, Vijeo Citec

from Schneider Electric’,

LabVIEW from National Instrument’s (NI) and Lookout are widely used as SCADA/HMI package programs for industrial automation systems. These programs are compared in view of their specifications given in Table 3.1 to select the most appropriate software program for the measurement and control applications. Minimum systems requirements for all the programs are CPU Pentium 4, 1GB RAM, 2GB disk space, Windows XP/Vista/7. All the programs have measurement and control applications such as open and closed‐loop control, observation and measurement, HMI, Telemetry, data analysis and storage, distributed alarms and events [32].

33

Table 3.1: Specification of SCADA/HMI automation package programs WinCC

VijeoCitec

LabVIEW

Lookout

Development

Animate

Animate

Animate

Animate

Tools

program

program

program

program

execution,

execution,

execution,

execution,

debugging

debugging

break point,

debugging

debugging Toolkits

OPC server, OPC server,

NI OPC

NI OPC

Visual

Visual Basic

server,

server,

Basic

scripts,

Visual Basic

Visual Basic

scripts,

control

scripts, C

scripts,

control

algorithms

code gen.,

control

Math.func.,

algorithms

algorithms

control algorithms Communication

Profibus,

RS485,

RS232,

RS232,

Fieldbus,

Fieldbus,

TCP/IP,

TCP/IP,

Profi Net,

Modbus

UDP, VXI,

Profibus,

Industrial

TCP/IP,

GPIB, VISA

Modbus

Ethernet

Modbus RTU

Diagnostics, Diagnostics,

Diagnostics,

Diagnostics,

Safety,

Safety,

Robustness

Robustness

Security,

Security,

Robustness

Robustness

High Level

High Level

Medium Level

low Level

(TCP/IP) Optional

Depending on vendor’ products

34

As shown in the Table 3.1, recommended system requirements, programming languages, development tools and applications are suitable for almost all automation applications and training. WinCC and Vijeo Citec programs require their own vendor’s instruments, industrial communication protocol, and their security features, so they are expensive. However, LabVIEW and Lookout programs provide more appropriate and low cost because they needn’t specific vendors’ instruments. But toolkits of the LabVIEW are more appropriate for more complex applications, because they include many compilers, analysis, synthesis and advanced functions. In addition, Lookout has a user‐friendly graphical user interface (GUI), i.e. it is easy to be used via HMI interface entries such as start, stop, indicator and controller and with minimal cost. 3.4.4 Telemetry network A telemetry network provides the communication pathway in a SCADA system. Topologies, transmission modes, link media, and protocols make up a telemetry network. Topology Topology is the geometric arrangement of nodes and links that make up a network. For a SCADA system the topologies are point-to-point, point-to multipoint, and multipoint-to-multipoint topologies. Point-to-point Point-to-point is a communication link between only two stations, where either station can initiate communication with the other, or one station can inquire and control the other. Stations can be connected using: • Cables or permanent public media like leased telephone lines or digital data services. • Temporary connections, such as dial-up lines or microwave, radio, or satellite transmissions. Point-to-point is generally a 2-wire connection, with the transmission media using two wires for signal transmission/reception Point-to-multipoint (multi drop) Point-to-multipoint is a communication link among three or more stations with one station being a communication arbitrator (master) that controls when the other stations (remote stations) can communicate. The stations can be connected using: 35

• Permanent public media like leased lines or digital data services. • Atmospheric connections, such as microwave, radio, or satellite transmissions. Point-to-multipoint connections are generally four-wire connections, with the transmission media using four wires for signal transmission/reception: one pair to transmit and one pair to receive. Private leased lines and digital data services provide four-wire, point-to-multipoint connections. Multipoint-to-multipoint Multipoint-to-multipoint is a radio modem communication link among three or more stations where there is no communication arbitrator (master) and any station can initiate communication with any other station. This is the topology used by spread-spectrum Ethernet radio modems. It provides a peer-to-peer network among stations. Peer-peer communication In contrast with Master-slave communication model, in which slave device can only communicate with server when it is requested, in Peer-to-peer (P2P) communications model all the parties are equal that any party is able to initiate a communication with other whenever needed. Each node in the network has capability of both client and server that allow devices to exchange information to each other directly without the need of coordination by a server. A P2P network uses internet protocol (IP) for communication between peers and usually has distributed architecture. Each device in a P2P network has to be equipped with IP-based communication and P2P software application. Because every device can share its resources among the network, compare to other network architecture, P2P communication system has greater computing performance and data transfer speed. In a P2P communication network, Transmission Control Protocol/Internet Protocol (TCP/IP protocol) allows all the relays to communicate directly to each other without coordination from a server. Any relay can receive from and send information to the network by itself whenever needed. In such a way, a relay is able to re-configure distribution scheme itself for any change and contingency in the distribution system. User can program relays and system to isolate any fault after a predetermined number of re-closing attempts and restore power to the loads in non-faulty section. With P2P communication network, the protection scheme has become an adaptive one that is capable to reconfigure itself to adapt to any change in network configuration and any contingency in the system. Because all the relays can manage switching sequence itself, so the 36

switching operation can be executed very fast. In case of losing communication, the system can return to work on conventional protection scheme without the need of communication. The advantage of P2P communication shows the prospect of widely utilizing this network in distribution automation. It can help to reduce loss of load, interruption duration as well as reduce the stress on system equipment due to fast successive re-energizing faulted feeder. Transmission mode The transmission mode defines the way information is sent and received between and/or among devices on a network. For SCADA systems, your network topology generally determines your data transmission mode. Table 3.2: Transmission mode If you have chosen this topology

Then your transmission mode is

Point -to -multipoint

Half-duplex

Which means

Transmit

Station B Transmit

Station Areceive

Point -to -point

Full-duplex

Transmit Station A

Full-duplex (between station and modem)

Station A

receive

receive

Transmit Modem A

Transmit

receive

37

Transmit

Information is simultaneously sent and received over the station to modem link, whereas information is sent in only one direction at a time over the modem to modem link.

Half-duplex (between modems)

Transmit

receive Station B

receive

Multipoint- to- multipoint

receive

receive

Transmit

Modem B

Transmit

receive

receive Station B Transmit

3.4.5 SCADA Protocols Communications protocol is a system of rules that allow two or more entities of a communications system to transmit information via any kind of variation of a quantity. The information/control signals exchanged between SCADA devices and other control systems through a network, or other media including handshaking, error detection, and error recovery is governed by rules and conventions that can be set out in technical specifications called Communication protocols standards. The important part of any complex SCADA system design is involved in matching the protocol and communication parameters between connecting devices. Protocol designs in SCADA are compact and are so designed as to send information to master terminal unit (MTU) only in case the RTU is polled for information by the MTU. Typical legacy SCADA protocols include Modbus RTU, ASCII, RP-570, Profibus and Conitel. These communication protocols are all SCADAvendor specific. Standard protocols are IEC 60870-5-101 or 104, IEC 61850 and DNP3. These communication protocols are standardized and recognized by all major SCADA vendors [8]. Distributed network protocol (DNP3) DNP3 is a protocol that defines communications between master stations, remote terminal units (RTUs), and IEDs in SCADA. IEEE has opted DNP3 as a standard for Electric power system communications. It is also widely used in water infrastructure, oil, gas, security and other industries. Initially, DNP3 was designed without any security features. DNP3 is extended to DNP3 Secure Authentication (SA), which was designed to meet requirements of IEC 62351-59, which is a standard developed by IEC (international electro technical commission). DNP3-SA employs techniques including symmetric cryptography and hashed message authentication codes (HMACs).Implementation presumes that both master station and outstation share a common secret key, called an update key, which is used to generate a session key. The recently released DNP3SA5 reinforces overall security for data information gathering, exchange, and use in SCADA systems [8]. Both DNP3 and IEC60870-5-101 serve similar functions they both [43] 

Reliably and efficiently transfer field data to the master station or the control center



Allow commands to be issued to the field with a very high degree of control security



Suit medium bandwidth communication channels 38



Include good data link frame integrity checking



Support application layer data object identification



Include data validity checking flags



Supports the transmission of digital (on/off) and analog data (in integer or floating point formats), counters and digital and analog control commands or set points.



Support transfer of files, setting of clock etc.

IEC 60870-5-101 also supports some electric power specific objects related to transformers and substation protection devices. IEC 60870-5-101 is a standard for power system monitoring, control & associated communications for telecontrol, teleprotection, and associated telecommunications for electric power systems. The standard is suitable for multiple configurations like point-to-point, star, mutidropped etc. 3.4.6 Communication system The way in which SCADA systems are connected can range from fiber optic cable to the use of satellite systems. The following sections will present some of the common ways in which SCADA systems are deployed. The way the SCADA system network (topology) is set up can vary with each system but there must be uninterrupted, bidirectional communication between the MTU and the RTU for a SCADA or Data Acquisition system to function properly. This can be accomplished in various ways, i.e. private wire lines, buried cable, telephone, radios, modems, microwave dishes, satellites, or other atmospheric means, and many times, systems employ more than one means of communicating to the remote site. This may include dial-up or dedicated voice grade telephone lines, DSL (Digital Subscriber Line), Integrated Service Digital Network (ISDN), cable, fiber optics, Wi-Fi, or other broadband services. There are many options to consider when selecting the appropriate communication equipment and can include either a public and/or private medium. Public medium is a communication service that the customer pays for on a monthly or per time or volume use. Private mediums are owned, licensed, operated and serviced by the user. If you choose to use a private medium, consider the staffing requirements necessary to support the technical and maintenance aspects of the system. 39

The way in which SCADA systems are connected can range from fiber optic cable to the use of satellite systems. Some of the common ways in which SCADA systems are deployed. Direct connection Direct connection usually uses RS232, RS485 or Ethernet port to connect RTU with SCADA servers. This technology uses twisted pair media wires. This is the cheapest and most preferable connection method. But this is suitable for small distances that are less than 20 m for RS232, less than 100 m for Ethernet and less than 1200 m for RS485. So this connection is used when the remote terminals near the main control room. Optical fibers Optical fibers consist of an inner core and cladding of silica glass and a plastic jacket that physically protects the fiber. Two types of fibers are usually considered: multi-mode graded index and single-mode step index fiber. Single-mode fiber supports higher signaling speeds than the multi-mode fiber due to its smaller diameter and mode of light propagation. Communication services usually supported by optical fiber include voice, data (low speed), SCADA, protective relaying, telemetering, video conferencing, high speed data, and telephone switched tie trunks. The technology has improved to the point where commercially available fibers have losses less than 0.3 dB/km. Losses of this magnitude, as well as the development of suitable lasers and optical detectors, allow designers to consider fiber optic technologies for systems of 140 km or more without repeaters [41]. The advantage of optical fiber are enormous potential band width, signal security and small size and weight etc. The disadvantage are higher initial cost in installation ,high cost of connector and interfacing and requires specialized and sophisticated tools for maintenance and repairing. Dialup connection Connecting far remote terminals with the SCADA servers using PSTN needs external dial up modem with serial port at each well station. At the other side, at the main control room, the SCADA server connects to the PSTN through dial up modem. This scenario implies a relatively low running cost that composed of the monthly fixed charges and the dial up calls. There is no online data connections, data will be collected several times through the station work period, and collecting data through dialup takes a time, as every call need 1 minute. On line connection will 40

be expensive. Also, getting data at urgent actions will take time until dialing the modem and connecting to RTUs. Cellular Phone System Connecting the RTUs with the SCADA server through the cellular phone system need GSM modem with serial ports. The device is assigned an IP number which allows him to connect online with internet. Charging depends on the size of download packets not on the calling time. When using this service the RTUs is connected online with the SCADA server. Cost of using cellular phone system is more expensive than PSTN although there are no monthly fixed charges. Calling charges of cellular system are more expensive. It is good to benefit from the prepaid system by using it at well stations modem and use it for alarms and emergency cases only. Microwave technology This transmission is via radio using parabolic dishes mounted on a series of towers or on top of buildings. Microwave communication is known as a form of “line of sight” communication, because there must be nothing obstructing the transmission of data between these towers for signals to be properly sent and received. Microwave communication one of the most commonly used data transmission method for telecommunications service providers and takes place both analog and digital formats. Digital microwave communication utilizes more advanced, more reliable technology. It is much easier to find equipment to support this transmission method because it is the newer form of microwave communication. Because it has a higher bandwidth, it also allows transition more data using more verbose protocols. The increased speeds will also decrease the time it takes to poll microwave site equipment. This media uses point-to-point, lineof-sight technology and communications may become interrupted at times due to misalignment and/or atmospheric conditions.

41

CHAPTER 4: RELIABILITY IN POWER SYSTEM The definition of reliability may vary from different perspectives. The two main perspectives for reliability consideration of a power system are customer perspective and utility perspective. The customers care about quality of service and being able to use their appliances any time needed during a day. Therefore any interruption in service is undesirable from the customer’s perspective. The utility’s perspective of reliability considers both the service reliability at the load points and reliability of the supply side which may include the reliability of generation, transmission and distribution assets, as well. 4.1. Reliability evaluation The ultimate goal of reliability analysis should be to answer questions like: Is the system reliable enough? Which schemes will be effective? And where high capital should spent to improve the system? The Reliability of a component or a system is defined as the probability that they perform their assigned task for a given period of time under the operating conditions encountered. Power equipment and power systems are vulnerable to failures occurred due to internal or external sources. The failure of a component, is the inability of a component to perform its intended function at a particular time under specified operating conditions [9]. A failure is specified by its failure rate and repair rate. Failure rate (λ) is reciprocal of the mean time to failure, and it is defined as the number of failures of a component in a given period of time divided by the total period of time that component was operating. Repair rate (μ), on the other hand it is defined as the number of repairs of a component in a given period of time divided by the total period of time that component was being repaired. The concept of reliability in the power system may be interpreted using three different categories: 1) Adequacy, as the capability of the system to meet its demand at all times considering scheduled and expected unscheduled outage of the elements; 2) Security, as the ability of the system to withstand disturbances such as a short circuit; and 3) Quality, regarding voltage condition, and harmonic characteristics, etc. Since distribution systems are seldom loaded near their limits, system adequacy is of relatively small concern and reliability emphasis on system security [9].

42

The reliability analysis is an essential study for the design, operation, maintenance, and planning of the power system. For example, with a specific reliability requirement, an optimum maintenance strategy can be determined to minimize the operation cost. In fact, the maintenance influences the deterioration process, failure rate, and reliability of the components and the system, accordingly. The two main approaches applied to reliability evaluation of distribution systems are: 

Simulation methods based on drawings from statistical distributions (Monte Carlo).



Analytical methods based on solution of mathematical models.

Simulation techniques can be used to estimate the reliability indices by directly simulating the actual process and the random behavior of the system and its components. These techniques can be used to simulate any system and component characteristics that can be recognized. The Simulation method simulates component and system behavior in chronological time and the system and component states in a given hour are dependent on the behavior in the previous hour. Time sequential simulation techniques can be used to evaluate both the mean values of the reliability indices and their probability distributions without excessive complications due to the probability distributions of the element parameters and the network configuration complexity. Simulation can be used to provide useful information on both the mean and the distribution of an index and in general to provide information that would not otherwise be possible to obtain analytically. The disadvantage of the simulation technique is that the solution time can be extensive. The analytical techniques are highly developed and have been used in practical applications for many years. Analytical techniques represent the system by mathematical models and evaluate the reliability indices from these models using mathematical solutions and based on assumptions concerning the statistical distributions of failure rate and repair times. The exact mathematical equations can become quite complicated and approximations may be required when the system is complicated. A range of approximate techniques therefore has been developed to simplify the required calculations. Analytical techniques are generally used to evaluate the mean or expected values of the load point and system reliability indices. The mean values are extremely useful and are the primary indices of system adequacy in distribution system reliability evaluation [1]. Conventional techniques for distribution system reliability evaluation are generally based on failure mode and effect analysis (FMEA) [1]. This is an inductive approach that systematically

43

details, on a component-by-component basis, al1 possible failure modes and identifies their resulting effects on the system. In systems with complicated configurations and a wide variety of components and element operating modes, the list of basic failure events can become quite lengthy and can include thousands of basic failure events. A reliability network equivalent approach is discussed in section 4.4 to simplify the analytical process. The main principle in this approach is that an equivalent element can be used to replace a portion of the distribution network and therefore decompose a large distribution system into a series of simpler distribution systems. This is a novel approach to distribution system evaluation which provides a repetitive and sequential process to evaluate the individual load point reliability indices. 4.2. Distribution system reliability indices Reliability indices are statistical aggregations of reliability data for a well-defined set of loads, Components or customers. Most reliability indices are average values of a particular reliability characteristic for an entire system, operating region, substation service territory, or feeder.The distribution system reliability indices indicate the annual average performance of the network in terms of interruption frequency and duration. The basic function of a distribution system is to supply electrical energy from a substation to the individual customer load points. Service continuity is, therefore, an important criterion in a distribution system, Service continuity can be described by three basic load point indices and a series of system indices [l]. The three basic load point reliability indices usually used are the average failure rate λ, the average outage time r and the average annual unavailability or average annual outage time U. It should be noted that these indices are not deterministic values but are expected values of an underlying probability distribution and hence are long-run average values. The three primary load point indices are fundamentally important parameters. They can be aggregated to provide an appreciation of the system performance using a series of system indices. The additional indices that are most commonly used are defined in the following sections.

44

4.2.1 System average interruption frequency index (SAIFI): SAIFI is a measure of how many sustained interruptions an average customer will experience over the course of a year, for a fixed number of customers the only way to improve SAIFI is to reduce the number of sustained interruptions experienced by customers. 𝑆𝐴𝐼𝐹𝐼 =

∑ 𝜆𝑖𝑁𝑖 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 = ∑ 𝑁𝑖 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

(4.1)

where λi is the failure rate and Ni is the number of customers at load points i. 4.2.2 System average interruption duration index (SAIDI): SAIDI is a measure of how many interruption hours an average customer will experience over the course of a year. For a fixed number of customers, SAIDI can be improved by reducing the number of interruptions or by reducing the duration of these interruptions. Since both of these reflect reliability improvements, a reduction In SAIDI indicates an improvement in reliability.

𝑆𝐴𝐼𝐷𝐼 =

𝑆𝑢𝑚 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 =

(4.2)

∑ 𝑈𝑖𝑁𝑖 ∑ 𝑁𝑖

𝑤ℎ𝑒𝑟𝑒 Ui is the annual outage time and Ni is the number of customers at load point i. 4.2.3 Customer average interruption duration index (CAIDI): CAIDI is a measure of how long an average interruption lasts and is used as a measure of utility response time to system contingencies. CAIDI can be improved by reducing the length of interruptions, but can also be reduced by increasing the number of short interruptions. Consequently, a reduction in CAIDI does not necessarily reflect an improvement in reliability. 𝐶𝐴𝐷𝐼 =

∑ 𝑈𝑖𝑁𝑖 𝑆𝑢𝑚 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑠 = 𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠 ∑ 𝑁𝑖𝜆𝑖

45

(4.3)

𝑤ℎ𝑒𝑟𝑒 λi is the failure rate, Ui is the annual outage time and Ni is the number of customers at load point i. 4.2.4 Average service availability (unavailability) index ASAI (ASUI): ASAI is the customer-weighted availability of the system and provides the same information as SAIDI. Higher ASAI values reflect higher levels of system reliability. 𝐴𝑆𝐴𝐼 =

𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐻𝑜𝑢𝑟𝑠 𝑜𝑓 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐻𝑜𝑢𝑟𝑠 𝐷𝑒𝑚𝑎𝑛𝑑

=

∑ 𝑁𝑖×8760−∑ 𝑈𝑖𝑁𝑖 ∑ 𝑁𝑖×8760

𝐴𝑆𝑈𝐼 = 1 − 𝐴𝑆𝐴𝐼

(4.4) (4.5)

𝑤ℎ𝑒𝑟𝑒 𝜆𝑖 is the failure rate, 𝑈𝑖 is the annual outage time, Ni is the number of customers at load point i and 8760 is the number of hours in a calendar year. 4.2.5 Expected energy not supplied index (EENS) 𝐸𝐸𝑁𝑆 = 𝐿𝑎(𝑖) × 𝑈𝑖

(4.6)

Average Expected Energy not supplied (AENS) 𝐴𝐸𝑁𝑆 =

∑ 𝐿𝑎(𝑖)𝑈𝑖 𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑁𝑜𝑡 𝑆𝑢𝑝𝑝𝑙𝑖𝑒𝑑 = ∑ 𝑁𝑖 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑠𝑒𝑟𝑣𝑒𝑑

(4.7)

𝑤ℎ𝑒𝑟𝑒 𝐿𝑎(𝑖), and Ui are respectively the average connected load and the average annual outage time at load point i, and Ni, is total number of customers served. The customer and load oriented indices described above are very useful as a means of assessing the past performance of a system as well as for predictive reliability assessment. 4.2.6 Momentary interruption indices Momentary interruption is the interruption of a customer for duration of less than 5 munities. Any interruption of duration limited to the period required to restore service by an interrupting device.

46

This must be completed within five minutes. The formula for Momentary Average Interruption Frequency Index (MAIFI) is: 𝑀𝐴𝐼𝐹𝐼 =

𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑀𝑜𝑚𝑒𝑛𝑡𝑎𝑟𝑦 𝐼𝑛𝑡𝑒𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛𝑠 𝑝𝑒/𝑦𝑟/(4.8) 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑆𝑒𝑟𝑣𝑒𝑑

In compiling the interruption data, some utilities have a policy of not counting outages shorter than 5 min. This does not affect the duration indices much; however, it significantly reduces the frequency index. The customer and load oriented indices described above are very useful for assessing the sensitivity analysis in predictive reliability assessment. They are also extensively used, however, as a means of assessing the past performance of a system. 4.3 Radial distribution network A typical radial distribution network consisting of root node, main line, lateral line, sub lateral line and minor line is shown in Figure 4.1.

Lateral section

Sub lateral section

Root node

Main line

Minor line

Figure 4.1: Single Line Diagram of a Radial Distribution Network 47

The distribution system components naming are: 

Root node: The node connected to the voltage regulating station/substation in the radial distribution network.



Main line: Line emanating from the root node.



Lateral line: Line emanating from the main line.



Sub lateral line: Line emanating from the lateral line.



Minor line: Line emanating from the sub lateral line.

A simple distribution system is usually represented by a general feeder which consists of n main sections, n lateral sections and a series component as shown in Fig. 4.2 In this feeder, Si, Li, Mi and Lpi represent series component i, lateral section i, main section i and load point i respectively. Li could be a transmission line, a line with a fuse or a line with a fuse and a transformer. Mi can be a line, a line with one disconnect switch or a line with disconnect switches on both ends.

Lp1

Lp(n-1)

L1

S1 M1

Alternate Supply

L(n-1)

M2

Feeder

L2

Mn

Lp2

Ln Lpn

Figure 4.2: Single Line Diagram of a General Feeder Based on the element data (λi , λk ,λs , ri , rk ,rs , Pk) and the configuration of the general feeder, a set of general formulas for calculating the three basic load point indices of load point failure rate λj, average outage duration rj, and average annual outage time Uj for load point j of a general feeder is as follows:

𝑛

𝑛

𝜆𝑗 = 𝜆𝑠𝑗 + ∑ 𝜆𝑖𝑗 + ∑ 𝜆𝑘𝑗 𝑝𝑘𝑗 𝑖=1

𝑘=1

48

(4.9)

𝑛

𝑛

𝑈𝑗 = 𝜆𝑠𝑗 𝑟𝑠𝑗 + ∑ 𝜆𝑖𝑗 𝑟𝑖𝑗 + ∑ 𝜆𝑘𝑗 𝑝𝑘𝑗 𝑟𝑘𝑗 𝑖=1

𝑟𝑗 =

(4.10)

𝑘=1

𝑈𝑗 𝜆𝑗

(4.11)

Where Pkj is the control parameter of lateral section k that depends on the fuse operating model. It can be 1 or 0 corresponding to no fuse or a 100% reliable fuse respectively and a value between 0 and 1 for a fuse which has a probability of unsuccessful operation of Pkj. The parameters λij, λkj and λsj are the failure rates of the main section i, lateral section k and series element s respectively and rij, rkj and rsj are the outage durations (switching time or repair time) for the three elements respectively. The rij, rkj and rsj data have different values for different load points when different alternate supply operating modes are used and disconnect switches are installed in different locations on the feeder. This is illustrated in the following three cases. Case I: no alternate supply In this case, rs, is the repair time of the series element s and ri is the switching time for those load points that can be isolated the repair time for those load points by disconnection from the failure main section i or that cannot be isolated from a failure of the main section i. In this case, rk, is the switching time for those load points that can be isolated by disconnection from a failure on a lateral section k or the repair time for those load points that cannot be isolated from a failure on a lateral section k. Case II: 100% reliable alternate supply In this case, ri and rk, take the same values as in Case 1. The parameter rs is the switching time for those load points that are isolated from the failure of a series element by disconnection or the repair time for those load points not isolated from the failure of a series element S. Case III: alternate supply with pa availability In this case, I; ri is the repair time (r1) for those load points not isolated by disconnection from the failure of main section i, the switching time (r2) for those load points supplied by the main supply and isolated from the failure of the main section i or r2 pa + (1- pa) r1 for those load points supplied 49

by an alternate supply and isolated from the failure of the main section i. The parameter rk is the repair time r1 for those load points not isolated by disconnection from the failure of lateral section k, the switching time r2 for those load points supplied by the main supply and isolated from the failure of lateral section k or r2 pa + (1- pa) r1for those load points supplied by an alternate supply and isolated from the failure of a lateral section k. rs is the same as in Case 2. The three basic equations presented earlier (equation 4.9, 4.10 and 4.11) cannot be used directly to evaluate the reliability indices of this system. The reliability network equivalent approach, however, provides a practical technique to solve this problem. 4.4 Network reliability equivalent A practical distribution system is usually a relatively complex configuration as shown in Fig. 4. 3. A complex radial distribution system is reduced to a series of general feeders using reliability network equivalents. If the original configuration is given in Figure 4.3 (a) and successive equivalents are shown in Figure 4.3 (b) and (c). Basic equations are used to calculate the individual load point indices. The reliability network equivalent method provides a simplified approach to the reliability evaluation of complex distribution systems. Reliability evaluations for several practical test distribution systems have shown this technique to be superior to the conventional FMEA approach. This method avoids the required procedure of finding the failure modes and their effect on the individual load points and results in a significant reduction in computer solution time. For calculating the reliability indices in a complex distribution system the reliability network equivalent approach uses bottom-up and top-down procedure as shown in Figure 4.3. The failure of an element in Feeder 3 will affect Load points not only in Feeder 3 but also in Feeder 1 and Feeder 2. The effect of Feeder 3 on Feeder 1 and Feeder 2 is similar to the effect of a lateral section on Feeder 2. A bottom-up process is used to search all the sub feeders and to determine the corresponding equivalent lateral sections. As shown in Figure 4. 3, Feeder 3 can be replaced using the equivalent lateral section (El 3) shown in Figure 4.3 (b). The equivalent must include the effect of the failures of all elements in Feeder 3. The equivalent lateral section (El 2) of Feeder 2 can then be developed as shown in Figure 4.3(c).

50

Lp1 S1

Lp2

L1

Feeder 1

M1

M3

M2 Lp3

Lp4

Alternate Supply

L2

Feeder 2

Lp5

M4 L3

M5

L4

Lp2

L1

M3

M2 Lp3

L7

Lp6

(a)

Feeder 1 M1

M9 L6

Lp1

S1

M8

Feeder 3

M6

Equivalent

L5

M7

Lp7

L2

Alternate Supply

Feeder 2 M4

L3

M5

E13 M6

Lp4

Equivalent

Lp1 S1 Feeder 1 M1

L4

(b)

Lp2

L1

M3

Alternate Supply

L2

M2 E12

(c)

Figure 4.3: Reliability network equivalent Following the bottom-up process, a top-down procedure is then used to evaluate the load point indices of each feeder and equivalent series components for the corresponding sub feeders until all the load point indices of feeders and sub feeders are evaluated.

51

The load point indices and the equivalent parameters of the series components are calculated using Equations (1)-(3). Referring to Figure 4.3, the load point indices in Feeder 1 and the equivalent series element S2 for Feeder 2 are first calculated, followed by the load point indices in Feeder 2 and s3. The load point indices in Feeder 3 are finally calculated. After all the individual load point indices are calculated, the final step is to obtain the feeder and system indices. The contributions of the failures of different elements to parameters of an equivalent lateral section will depend on the location of the disconnect switches. The reliability parameters of an equivalent lateral section can be divided into two groups and obtained using the following equations:

𝑚

𝜆𝑒1 = ∑ 𝜆𝑖

𝑛

𝜆𝑒2 = ∑ 𝜆𝑖

𝑖=1

𝑖=1

𝑛

𝑈𝑒2 = ∑ 𝜆𝑖 𝑟𝑖 𝑖=1

𝑟𝑒2 =

𝑈𝑒2 𝜆𝑒2

(4.12)

𝑚

𝑈𝑒1 = ∑ 𝜆𝑖 𝑟𝑖

(4.13)

𝑖=1

𝑟𝑒1 =

𝑈𝑒1 𝜆𝑒1

(4.14)

Where 𝜆𝑒1 and 𝑟𝑒1 are the total failure rate and restoration time of the failed components that are not isolated by disconnects in the subfeeder and m is the total number of these elements. The basic formulas can now be used to evaluate the load point indices of Feeder 1. The simplification in computation provided by the proposed method can be illustrated using Figure 4.3 (a). In this distribution system, there are 7 load points and 19 elements. Using the standard FMEA approach, 19x7 =133 calculations are required as all load points are checked for each element failure. Using the reliability network equivalent approach, however, 7+7+7 =21 calculations are required to find the equivalent lateral sections and 7x3+7x3+ 7x3 =63 calculations to find the load point indices for a total of 84. This is a simple network.

52

4.5 Distribution system feeder model Reliability modelling can be classified in to two categories, this are component modelling and system modelling. In component modelling, all the components of the distribution system are modelled based on their failure rate and repair time of each component from the historical data recorded. System modelling is used to develop a predictive reliability model of a system. The model is calibrated to represent current system reliability. By incorporating reliability considerations in the system design and in the planning of system expansion, operation and maintenance the quality of supply can be improved. To obtain useful results from system reliability assessments, reasonable values of component reliability parameters need to be used. In predictive reliability analysis the first step is always to make a representative model of the real system that is going to be studied. When the model has been formulated, one can solve the desired problem with this model. In network/system modelling the relationship between the system and its components is considered. The model describes the behavior of the system if one or more of its components fail to fulfill its function. 4.6 Switch Placement Optimization Implementation of smart grid technologies will result in (a) more switching devices deployed in distribution systems and (b) a communication infrastructure that will provide information on status of switches / breakers and will also enable remote control of switches. The remote-controlled switches have become economically viable (feasible) due to the large amount of automation suppliers and the arrival of new communication technologies. This arrangement will be ideal to support optimization and reconfiguration for the purpose of restoring power to customers. It is obvious that smart switches are required to improve power system reliability, i.e., to reduce reliability indices. However, adding smart switches to the system is directly related to the increase in costs. Therefore, there exists a trade-off between power reliability improvement and costs. Then, the question is: What the optimal number of smart switches that leads to the minimal cost? Here, optimization techniques come in to play.

53

There are several modern heuristic optimization techniques such as evolutionary computation, simulated annealing, tabu search, particle swarm, etc. Recently, genetic algorithm (GA) and particle swarm optimization (PSO) techniques appeared as promising algorithms for handling the optimization problems because of their versatility and ability to optimize in complex multimodal search spaces applied to non-differentiable objective functions. Both GA and PSO are similar in the sense that they are population-based search methods and they search for the optimal solution by updating generations. While GA is inherently discrete, i.e., it encodes the design variables into bits of 0’s and 1’s, and therefore easily handles discrete design variables, PSO is inherently continuous and must be modified to handle discrete design variables. PSO is a population-based search algorithm and is initialized with a population of random solutions, called particles. PSO is similar to genetic algorithm (GA) in that the system is initialized with a population of random solutions. It is unlike GA, however, in that each potential solution is also assigned a randomized velocity, and each particle flies over the search space at velocity dynamically adjusted according to the historical behaviors of the particle and its companions. PSO has much more profound intelligent background than the genetic algorithm [25]. Also PSO could be executed more easily. On the one hand, PSO has very few parameters to adjust, so that it is convenient to make the parameters reach to the optimum values, a large amount of calculation work and much time can be saved. On the other hand, PSO can find the optimal solutions or near the optimal solutions with a fast convergent speed, because it only has two computation formulas for iteration. Based on these advantages, in this thesis Particle Swarm optimization is used to solve the Switch Placement Optimization problem.

54

CHAPTER 5: PARTICLE SWARM OPTIMISATION Optimization technique is used to find the best solution for any given circumstances. For example, in a company if it is required to improve its rating, technological and managerial plans have to be taken many times. Here, the goal of the plans is to either maximize the profits or to minimize the spending effort. Optimization is referred as both minimizing and maximizing the tasks. Since the minimization of any function is same as maximizing its additive inverse , the terminology minimization and optimization can be used interchangeably [52]. Because of this reason, optimization became very important in many fields. Basically, in order to solve the optimization problems, PSO algorithm is inspired by the animal’s activity. In PSO, swarm means population; particle represents each member of the population. Each particle searches through the entire space by randomly moving in different directions and remembers the previous best solutions of that particle and also positions of its neighbor particles. Particles of a swarm adjust their position and velocity dynamically by communicating best positions of all the particles with each other. In this way, finally all particles in the swarm try to move towards better positions until the swarm reaches an optimal solution. Thus, due to its easy implementation and its ability to obtain fast convergence, PSO technique is becoming very popular. Moreover PSO uses only basic mathematics and it does not involve any derivative or gradient information. 5.1 The basic model of PSO algorithm Kennedy and Eberhart [46] proposed a solution to non-linear and complex optimization problem by observing the behavior of flock of birds. They developed the concept of optimizing the function using swarm of particles. Consider a function of n dimension which is defined by f(x1, x2, x3 ……xn)=f(x)

(5.1)

Where xi is the optimizing variable, which represents the set of variables for a given function f(x). Here, the goal is to get an optimum value x* so that the function f(x*) can become either a maximum value or a minimum value. The Particle Swarm Optimization (PSO) technique is parallel search technique which utilizes multi-agents (swarm of particles). Each agent in the swarm represents a solution. All agents go through entire search space and updates its position and velocity based on their own experience 55

and on experience of other agents. Suppose xi(t) denote the agent or particle ‘i’ position vector search space at time step t, then each agent position is updated in the search space by 𝑋𝑖 (𝑡 + 1) = 𝑋𝑖 (𝑡) + 𝑉𝑖 (𝑡 + 1)

(5.2)

Where, vi(t) is the particle velocity vector which is used to update the own experience and other particles experience and also drives the optimization process. Thus, in PSO technique, all agents are randomly initialized and fitness value is computed by updating the personal best (best value of each agent) 𝑃𝑖,𝑏𝑒𝑠𝑡 = (𝑃𝑖1 , 𝑃𝑖2 , … , 𝑃𝑖𝑑 ) and global best (best value of all agents in the entire swarm) 𝑃𝑔,𝑏𝑒𝑠𝑡 = (𝑃𝑔1 , 𝑃𝑔2 , … , 𝑃𝑔𝑑 ).The loop starts by assuming initial values of position of the particles as personal best and then updates every particle position by using the updated velocity. When the stopping criterion is met, loop will be ended [40]. Basically, PSO algorithms are classified into two types. They are Global Best (gbest) and Local Best (lbest) PSO algorithms which differ in the size of their neighborhood particles. 5.1.1 Global best PSO The global best PSO (or gbest PSO) is a technique in which position of each agent is influenced by best agent in the whole swarm. In this method, information is obtained from all the agents in the swarm and thus it makes use of a star network topology [48]. Here, xi is the current position of each agent in search space, vi is the current velocity and a Pbest,i is personal best position of each agent in search space. If a minimization problem is considered, the personal best position Pbest,i represents the position of particle “i” in search space with smallest fitness function value. Gbest is the position of particle which yields the lowest value among all personal best positions [50]. Personal best Pbest,i at next step, t+1 ,where tϵ[0,…..N], for a minimization problem is calculated as

(5.3) Where f is the fitness function. The global best position Gbest at time step for a minimization problem is calculated as Gbest=min (P tbest,i ) , , where i ϵ[1,…..,n] and n>1 Thus we can note that personal best is best position of each agent among all time steps that each agent traversed. Global best is best position of all agents in the entire swarm. [50]. 56

For gbest PSO method, velocity of agent is obtained by

𝑉𝑖 (𝑡 + 1) = 𝑤 𝑉𝑖 (𝑡) + 𝑐1𝑟1(𝑃𝑙𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) + 𝑐2𝑟2(𝑃𝑔𝑏𝑒𝑠𝑡 − 𝑋𝑖 )

(5.4)

Where c1 and c2 are positive acceleration constants which are used to determine contribution level of the cognitive and social components respectively; r1 and r2 are random variables with uniform distribution between 0 and 1. In this equation, w is the inertia weight which shows the effect of previous velocity vector on the new vector. An upper bound is placed on the velocity in all dimensions 𝑉𝑖𝑚𝑎𝑥 . This limitation prevents the particle from moving too rapidly from one region in search space to another. This value is usually initialized as a function of the range of the problem. Local best PSO In local best PSO (or lbest PSO) technique each agent will be influenced by the best agent among its neighbor agents, and thus it resembles a ring social topology described in Section 5.4. In this method the social information that is exchanged within neighborhood of agents denotes local knowledge of environment [8] [10]. In this case, the velocity of agent is computed by 𝑉𝑖 (𝑡 + 1) = 𝑤 𝑉𝑖 (𝑡) + 𝑐1𝑟1(𝑃𝑙𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) + 𝑐2𝑟2(𝑙𝑔𝑏𝑒𝑠𝑡 − 𝑋𝑖 )

(5.5)

Therefore, from 5.1.1 and 5.1.2 respectively, we can notice that in gbest PSO technique every agent gathers the information from the best agent in the entire swarm, whereas in the lbest PSO technique each agent gathers the information from only its immediate neighbours in the swarm [47]. 5.2 Comparison of ‘gbest’ to ‘lbest’ Mainly, there are two differences between ‘gbest’ and ‘lbest’ PSO techniques: One is that convergence of gbest PSO will be faster than lbest PSO because of the larger agent interconnectivity. Second is, lbest PSO is less susceptible of being trapped in local minima due to the larger diversity.

57

Start Initialize Population Xij,c1,c2,Vij,evauate fitness fij using Xij, D=dimension, P=max.no of particles and N= max.no of iteration t=0 Choose randomly r1j(t) and r2j(t) i=1 j=1 Update Particle velocity (Vij) Update particle position (Xij)

J=J+1

yes

i=i+1

j
yes

i
Fij(t)
t=t+1

yes

flbest,i=fij(t),Gbest=Xij(t)

yes

fgbest,i=fij(t),Gbest=Xij(t)

No Fij(t)
yes

No Stop

Figure 5.1: Flowchart for global best PSO

58

5.3 PSO algorithm parameters For any given optimization problem, some of the parameters in PSO algorithm may affect its efficiency. Some of these parameter’s values and their choices have major impact on the performance of the PSO technique, and other parameters have small or no effect [49]. The basic parameters of PSO are 1. Size of the swarm 2. Number of iterations 3. Components of velocity, and 4. Acceleration coefficient. In addition to these parameters, PSO technique is also influenced by inertia weight, velocity clamping, and velocity constriction. 5.3.1 Swarm size Swarm size is defined as the number of agents n in swarm. A huge swarm generates more particles and most of the search space is to be covered per iteration. Number of iterations may be reduced in order to achieve best optimization value using large number of agents. But the computational complexity per iteration will be increased by using more amounts of agents and also it is more time consuming. 5.3.2 Iteration numbers Obtaining a best result depends on number of iterations which in turn depends on problem. If the number of iterations is too low, then the search process may stop prematurely. If the number of iterations is large, it may add computational complexity and thereby consumes more time. 5.3.3 Velocity components While updating agent’s velocity, the velocity components plays a vital role. There are three terms in agent’s velocity. They are inertia component, cognitive component and social component. 1. The term 𝑤𝑉𝑖 (𝑡) is called inertia component. It gives the information of the movement in the immediate past. This component is used to prevent sudden changes in the agents direction and provides tendency to move towards the current direction.

59

2. The term 𝑐1𝑟1(𝑃𝑙𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) is called cognitive component. It is used to measure the performance of the agents with respect to their past performances. It acts like an individual memory of the best position for an agent. The effect of this component is to make the agents to positions which satisfied them the most in past. 3. The term 𝑐2𝑟2(𝑃𝑔𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) for Gbest PSO is called 𝑐2𝑟2(𝑙𝑔𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) for lbest PSO is called social component. It is used to measures the performance of the agents with respect to a group of agents. It makes each agent to move towards best position found by agent’s neighborhood. 5.3.4 Acceleration coefficients The stochastic influence of the social and cognitive components of the agent’s velocity depends upon acceleration coefficients c1 and c2, together with the randomly generated numbers r1 and r2, respectively. The confidence that an agent has in itself is represented by C1 and the confidence that an agent has in its neighbors is represented by C2 [50]. The properties of C1 and C2: 1.

When C1 =C2 =0, until search space’s boundary is met, all the agents will continue to move with their current speed.

2.

When C1>0 and C2 =0, all agents become independent.

3.

When C1=0 and C2 >0, all agents in the swarm will get attracted towards a single point

4.

When, C1= C2 all agents will get attracted towards average of Pbest i, and Gbest .

5.

When, C1>> C2 each agent is greatly influenced by its personal best position, which results in excessive wandering.

6.

When C2 >> C1 then all agents in the swarm are greatly influenced by the global best position which makes all agents to run prematurely to the optima [51].

Initialization of C1and C2 plays a role in obtaining the optimum values. Wrong assumption of C1and C2 results in cyclic behavior. From many researches, the values of two acceleration constants should be C1 = C2 =2. 5.3.5 Inertia weight The inertia weight governs how much of the previous velocity should be retained from the previous time step. The best performance, however, was obtained by using an inertia weight that decreases from 0.9 to 0.2 during the first during the course of a simulation [56]. This setting allows the PSO 60

to explore a large area at the start of the simulation, when the inertia weight is large, and to refine the search later by using a smaller inertia weight. In addition, damping the oscillations of the particles around gbest is another advantage gained by using a decreasing inertia weight. These oscillations are recorded when a large constant inertial weight is used. Accordingly, damping such oscillations assists the particles of the swarm to converge to the global optimal solution. For the value of inertia weight (w) it is assumed to decrease linearly during the course of the simulation from 0.9 to 0.2 according to: 𝑤(𝑖) =

(𝑤𝑚𝑖𝑛 − 𝑤𝑚𝑎𝑥 ) (𝑖 − 1) + 𝑤𝑚𝑎𝑥 𝑖𝑡𝑒𝑟𝑚𝑎𝑥 − 1

(5.6)

Where, 𝑤(𝑖): The inertia weight at iteration i. 𝑤𝑚𝑖𝑛 : The minimum inertia weight (final) = 0.2. 𝑤𝑚𝑎𝑥 : The maximum inertia weight (initial) = 0.9. 𝑖𝑡𝑒𝑟𝑚𝑎𝑥 : Iteration by which internal weight should be at final value.

Neighborhood topologies For each agent a neighborhood must be defined [47]. The extent of social interaction within swarm is computed by the neighborhood. When the size of neighborhoods in the swarm is small, it leads to less interaction. Even though the convergence is slower, the quality of solutions will be improved for small neighborhood. The risk involving earlier convergence will be occurred in case of larger neighborhood. In order to solve this earlier convergence problem, search process should be started with small size of neighborhoods and later over the time, the size can be increased. As the agents move towards near to optimum region, this technique ensures faster convergence [50]. In the swarm, the social interaction among the agents is dealt in PSO technique. Each agent in the swarm exchanges the information about their success with other agents through communication. All agents tend to move towards the agent when that agent fined a better position. Different types of neighbourhood topologies are developed by many researchers [45]. Some of them are discussed below:

61

(a) Star or g best

(b) Ring or l best

(c) Wheel Figure 5.2: Neighbourhood topologies Figure 5.2 (a) explains the star topology. Here each agent is connected with every other agent. This has an advantage of converging faster than other topologies and disadvantage of being trapped in local minima. As all the agents in this topology know about each other, it is referred as the gbest PSO. Figure 5.2 (b) explains the ring topology. Here each agent is connected with its immediate neighbors only. In this topology, if anyone agent finds a good result, then it passes the information to its immediate neighbors, and they pass that information to their immediate neighbors. This process continues till the last agent is reached. Hence, spreading of the best result is very slow in this topology compared to star topology. It is referred as the lbest PSO. Figure 5.2 (c) explains the wheel topology. Here all agents are connected to only one agent (a focal agent), and through this agent information is communicated. By comparing the best performance

62

of all agents in the swarm, focal agent adjusts its position towards best performance. The focal agent informs the new position to all the agents. There are two versions of the basic PSO algorithm, the Continuous/real-valued Particle Swarm Optimization (PSO) version and Discrete Binary Particle Swarm Optimization (BPSO).Kennedy and Eberhart proposed a discrete binary version of PSO for binary problems [6]. In their model a particle will decide on "yes" or " no", "true" or "false", "include" or "not to include" etc. also this binary values can be a representation of a real value in binary search space. In the binary PSO, the particle's personal best and global best is updated as in real- valued version as explained before. The major difference between binary PSO with real-valued version is that velocities of the particles are rather defined in terms of probabilities that a bit will change to one. Using this definition a velocity must be restricted within the range[0,1].So a map is introduced to map all real valued numbers of velocity to the range [0, 1]. The normalization function used here is 𝑉′𝑖𝑗 (𝑡) = 𝑠𝑖𝑔 ( 𝑉𝑖𝑗 (𝑡)) =

1 1 + 𝑒 − 𝑉𝑖𝑗(𝑡)

(5.7)

Figure 5.3: Sigmoid function Also the equation (1) is used to update the velocity vector of the particle. And the new position of the particle is obtained using the equation below:

𝑋𝑖𝑗 (𝑡 + 1) = {

1 𝑖𝑓

𝑟𝑖𝑗 < 𝑠𝑖𝑔 (𝑣𝑖𝑗 (𝑡 + 1))

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

63

(5.8)

5.4 Applications of PSO . The following are some of applications of PSO that are successfully used: 

Telecommunications



System control



Data mining



Power systems design



Signal processing



Network training.

PSO algorithm was used mainly to solve unconstrained and single-objective optimization problems. But, in the present days, they are used to solve many problems like constrained problems, dynamically changing landscapes problems, multi-objective optimization problems. 5.5 Problem formulation for optimal placement of IEDs The optimum selection problem of the number and location of IEDs is to minimize the total supply interruption cost (TSC), which is the sum of the fixed cost associated with capital investment on IEDs and the cost of expected energy not supplied, and can be expressed mathematically as 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝑆𝐶 = (𝐹𝐶 + 𝐶𝐸𝑁𝑆 )𝐿𝑙𝑘 OR

(5.9)

𝑁𝐿𝑝

𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑇𝑆𝐶 = (𝐹𝐶 + ∑𝑖=0 𝐿𝑎(𝑖) 𝑈(𝑖) 𝐶(𝑖))𝐿𝑙𝑘

(5.10)

Where 𝐿𝑙𝑘 represent the kth location set for l IEDs, and corresponding system reliability and the total supply interruption cost be 𝑅𝑘𝑙 and 𝑆𝑐𝑜𝑠𝑡𝑘𝑙 respectively. The total annual supply interruption cost can be represented as a function of the set𝐿𝑙𝑘 : 𝑆𝑐𝑜𝑠𝑡𝑘𝑙 = 𝑆𝑐𝑜𝑠𝑡𝑘𝑙 (𝑅𝑘𝑙 (𝐿𝑙𝑘 ))

(5.11)

For l IEDs, the number of location sets NS is: 𝑁𝑆𝑁𝑙 =

𝑁! 𝑙! (𝑁 − 𝑙)!

(𝑙 = 0,1,2, … , 𝑁)

The total number of IEDs/location sets NT is:

64

(5.12)

𝑁

𝑁𝑇 = ∑ 𝑁𝑆𝑁𝑙 = 2𝑁 + 1

(5.13)

𝑖=0

FC is the fixed cost including the investment for purchase and installation of switches. The total annual switch investment for the set of 𝐿𝑙𝑘 can be calculated using the following equation: FC = (

𝑆𝐶 (1 + 𝑖𝑟)𝑡𝑦 (1 + 𝑚𝑐))𝐿𝑙𝑘 𝑡𝑦

(5.14)

Where SC is the total investment cost which includes the switch installation cost, ty is the life of the switch, ir represents the interest rate and mc is the percentage of annual switch cost for switch maintenance. CENS is the cost of the expected energy not supplied and for the set of 𝐿𝑙𝑘 it is calculated using the following equation: 𝑁𝐿𝑝

𝐶𝐸𝑁𝑆 = (∑ 𝐿𝑎(𝑖) 𝑈(𝑖) 𝐶(𝑖))𝐿𝑙𝑘

(5.15)

𝑖=0

Where La (i) is average load at load point I which is given by: 𝐿𝑎(𝑖) = 𝐿𝑃(𝑖)𝐿𝐹(𝑖)

(5.16)

Where LP is peak load demand and LF is the annual load factor of the line, which is calculated from: 𝐴𝑛𝑛𝑢𝑎𝑙 𝐿𝑜𝑎𝑑 𝐹𝑎𝑐𝑡𝑜𝑟(𝐿𝐹) =

𝑇𝑜𝑡𝑎𝑙 𝑎𝑛𝑛𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝐴𝑛𝑛𝑢𝑎𝑙 𝑝𝑒𝑎𝑘 ∗ 8760

(5.17)

U (i ) is annual outage time of load point i and C (i) is the respective cost per kilowatt as expressed by the following equation: 𝐶(𝑖) = (𝑅𝑒𝑠𝑖 (%) ∗ 𝑓𝑟𝑖 ) + (𝐶𝑜𝑚𝑖 (%) ∗ 𝑓𝑐𝑖 ) + 𝐼𝑛𝑑𝑖 (%) ∗ 𝑓𝑖𝑖 )

(5.18)

Where 𝑅𝑒𝑠𝑖 (%), 𝐶𝑜𝑚𝑖 (%), 𝐼𝑛𝑑𝑖 (%) 𝑎re load percentage of each type of customers in section i. where 𝑓𝑟𝑖 , 𝑓𝑐𝑖 , 𝑓𝑖𝑖 are interruption cost functions of residential, commercial, and industrial customers respectively. For Bahir Dar City distribution system, load factor for the year of 2006 is 0.72.

65

5.5.1 BPSO to solve the IEDs optimal placement to improve the reliability of system The complete computational flow of the binary PSO algorithm for optimal placement of IEDs is given in the following steps. Step 1: Initialization 

Read input data (reliability data) of the selected feeder of the distribution system



Generate n particles randomly which have an element equal to number of IEDs. 𝑡

pop𝑡 = [ 𝑋1 , 𝑋2 𝑡 , … , 𝑋𝑁𝑃 𝑡 ]

𝑋𝑁𝑃 Shows the existence of the IED (0 or 1) in

the section. 

Generate the initial velocities of all particles randomly



Initialize the acceleration coefficients as c1=2 and c2=2.



Evaluate each particle in the swarm using the objective function



For each particle i in the swarm, set , 𝑃𝑏𝑒𝑠𝑡𝑖 0 = 𝑋𝑖 along with its best fitness value



Set the global best

0

f 𝑔𝑏𝑒𝑠𝑡 (𝐺𝐵0 ) = min( 𝑃𝑙𝑏𝑒𝑠𝑡𝑖 0 ) Step 2: Update iteration counter t=t+1 Step 3: Update velocity by using the equation 𝑉𝑖 (𝑡 + 1) = 𝑤 𝑉𝑖 (𝑡) + 𝑐1𝑟1(𝑃𝑙𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) + 𝑐2𝑟2(𝑃𝑔𝑏𝑒𝑠𝑡 − 𝑋𝑖 ) Step 4: Update dimension (position) by using the sigmoid function Step 5: Update particle best; each particle is evaluated again with respect to its updated position to see if particle best will change. Step 6: Update global best Step 7: Stopping criterion If the number of iteration exceeds the maximum number iteration or when there is no significant improvement over a number of iterations then stop, otherwise go to step2. From the result, we can analyze the reliability improvement gained by optimal placing of IEDs which is obtained by using BPSO.

66

CHAPTER 6: SYSTEM DESIGN AND DISCUSSION 6.1 SCADA system design The SCADA system for case study area which, include the communication system, Main control room equipment’s, software needed and operator display structure will be. 6.1.1 Communication system Topology The most used topology in SCADA system is Point-to-multipoint since, Point-to-multipoint is a communication link among three or more stations with one station being a communication arbitrator (master) that controls when the other stations (remote stations) can communicate. So, point-to-multipoint topology is used between MTU and IEDs but between the IEDs peer-peer topology is used. Transmission mode The transmission mode will be half duplex and full duplex respectively to the used topologies, where point-to-multipoint topology between MTU and IEDs and peer-peer topology between the IEDs. Protocols The Ethiopian national SCADA system, which called National load dispatch Centre (NLDC) uses the international standard communication protocol IEC 60870-5-101; Bahir Dar distribution system is part of this national grid, so to communicate with the national grid and other advantages explained before the distribution protocol will be IEC 60870-5-101protocol. Communication It is recommended to use wireless radio signals to connect far stations with the main control room. But the line-of-site distance between the proposed main control room and remote locations is less than 5Km for all remote locations. Due to the location of the RTUs/IEDs, the wired communication is possible but the speed is low as compared to wireless one. Rather than the above options other wireless option is possible, which can be utilized via the GPRS gateway mentioned before by fulfilling the data size and speed needs cost-effectively. This could also be replaced by an EDGE or an LTE (Long-Term Evolution, commonly marketed as 4G LTE /or unlicensed radio 67

transmission with narrower bandwidths in case of remote locations) versions later, since the telecommunication system of the country have been upgraded to those generations. Ethiopia cellular phone system has service that is called GPRS; this service is used for internet connection and larger investments in terms of network establishment and maintenance are avoided by utilizing the existing public communication network. So the communication system Within the MTU and RTUs the SCADA system uses Cellular Phone System but the system can also use PSTN as a backup system to make the system more reliable so wireless Ethernet is used to connect IEDs. To maintain high communication security level for the time-sensitive data, robust data encryption protocols coupled with internal firewall becomes crucial for the SCADA system. As majority of the system modules/components have some electronic characteristics and are connected to each other to achieve the final level of system functioning, they have to interact to transfer information, commands and other relevant information and data. This necessitates a communication module to provide this capability for peer to peer communication or communication between MTU and RTU (IED). Wireless gateway: to enable wireless communication (GPRS, EDGE, LTE, Radio or other) signal transmission to the MTU and to transmit from MTU to IED via wireless GPRS network using public network operator (SIM card). M2M gateway (located at the MTU): to enable wireless communication signal reception from the IEDs (via the Wireless Gateway) and for making wired communication as backup communication link. The whole communication approach using GPRS is summarized below:

IED-> Ethernet-> Wireless Gateway-> GPRS-> M2M Gateway-> Ethernet-> MTU 6.1.2 RTU: not needed! As the key purpose of the RTU is to interface objects, do the acquisition of data parameters from objects local/substation, transmit them to the MTU and control objects using commands from MTU (e.g. using SCADA) these are being done by the IED itself and the wireless capability has also been added as just explained before. Thus the need for RTU vanishes as the remote monitoring and control of field equipment is enabled via two-way wireless communication using the IED and the related components.

68

6.1.3 Main control room Main control room is proposed to be at the North West Ethiopian electric utility office which located in the middle of the Bahir Dar city. At this room the computer system for the SCADA will be installed. This system consists of hardware and software. Hardware Computer System This includes all the hardware devices needed to implement the SCADA system that consists of: 1. SCADA server computer: this server needs to have very good specification. And will be used to install all the SCADA software packages that include software programs and tools. 2. Backup SCADA server, used as a backup of the main server and for redundancy. 3. Workstation: from 2 to 3 workstations for the supervisor and operators. 4. Laser printer. 5. Local Area Network: Ethernet LAN needed to connect the servers, printers and the client workstations with each other’s. Software System Software system will include all software packages needed to implement the SCADA system which are: SQL Server and HMI implementation program as Lookout from National Instruments (NI). Operator interface HMI package is used to design the operator interface with SCADA system Lookout Program from National Instruments (NI) can be used. This display need to be simple structure, enable the operator to reach to his target fast and simply. It is recommended to build the system display on several levels with the ability to move from one level to other levels and other advantages explained before. 6.1.4 Other components needed Power Source As majority of the system components within the SS require a reliable auxiliary voltage supply and with the addition of modules/components, (which have some power consumption) the rating of the auxiliary power system increases. If the auxiliary voltage fails, like in case of faults, there is no power in the system but it still has to function to maintain the IED and the Wireless Gateway

69

function. An energy store supplies the components for time periods reaching from few minutes to two hours which necessitates stored energy in the form of battery which further necessitates battery chargers and their size depends on the power demand from the components is needed. CTs: needed for feeding measured currents (transformed down to the relay levels) VTs: needed for feeding measured voltage (transformed down to the relay levels) but only in case the need is supported by the financial capability. Although the voltage measurements are expensive to be obtained from the MV side, but in case there is a transformer available at the RTUs, the voltage measurements could be obtained from its LV side which can be calibrated for use. The IEDs voltage module can take LV input directly (secured using LV fuses) as it is made to handle voltages up to Vrms rating of the IEDs continuously. Sensors: for current and/or voltage measurements (as an alternative to CTs and VTs) but in a different manner The issue with using sensors for MV measurements is that the number of available IEDs compatible with them is few and the manufacturers have designed them based on the standards for instrument transformers in the absence of the ones specifically defined for MV sensors. Due to their still-evolving nature, low cost-effectiveness, their use is needed be carefully considered to avoid their replacement in the future is case their effectiveness is not met by the needs [5]. Therefore the use of sensors for the scope of this research is excluded. Wiring: for connecting the components to the IED and LAN cables for connecting computers, servers (EMS server, data base server etc.). Fuses: for protecting the IED from surges in the incoming signals from the CTs and the voltage measurement from the LV side. The overall system design is shown below.

70

Figure 6.1: overall hardware system architecture 6.2 System Operation Applying smart grid technology activates a network of intelligent optimal line sensors and switches to create a self-healing protection system. Self-healing protection systems utilize equipment that restores power automatically when there is trouble, or a fault in the line. This reduces the number of consumers affected by power outages. The components of self-healing systems, such as optimal IEDs, are connected wirelessly to improve the performance of real-time information updates to the main controller. The operation sequences of the SCADA automation system when a fault occurs is: 

IEDs send a signal, which indicates a variation in current signal, a voltage drop, or other transient signal to the MTU.



The controller system sends a command to IED, which detects the over-current when the fault occurs



After milliseconds of tripping the faulted area, the controller sends a command to the IED to reclose the switch so that electricity can flow. However, if the fault was still affecting the same

71

area, the sensors updated the over-current signal wirelessly to the main controller system, commanding the switch to open again and isolate the area. 

Meanwhile, the main controller system, which connected with all sensors and IEDs at this area, sent a command to isolate the exact affected area,



Immediately, the MTU sends a command to IEDs closest to the fault to isolate the exact affected (faulted) area before the over-current takes a wide area of the distribution system.



At the same time, it is necessary to restore the rest of the grid affected by the outage by using green energy (if it exist but in this study this is not considered) employed in the distribution system. Backup sources applied in the smart grid technology and bi-directional system also help to restore other affected and nearby areas of the system.



The affected area can then be easily located, as it is isolated by switching devices. In this way, fault clearing can be short in time and the affected area is limited.



Once the fault is cleared, the isolated lines can be restored using the original source.

The operation sequences when a fault occurs in distribution system illustrated in Figure 6.2 below

Yes

 

IED-1 IED-2 IED-3 Fault

IED-4

Monitoring and controlling the distribution system

Disconnect and reclose switches

Isolate affected area Restore the unaffected area using other source

Indicate for fault existing

IED-5

NO

IED-6



IED-7

Figure 6.2: operation sequences when a fault occurs.

72

Restore the electricity using normal power source

6.3 Reliability analysis on the existing system The reliability measuring parameters (indices) are calculated for Bahir Dar I and Bahir Dar II substation feeders using the monthly interruption duration and frequency of these feeders for (2014, 2015) and (2013-2015) years respectively as shown in appendix A-1 and it could be noted that this value is recorded at the substation when all customers connected to the feeder is interrupted. The other interruptions occurred from different load points of the feeders are not included.Based on the recorded data the average reliability indices of the existing system are calculated and summarized in table 6.1. From the experience of other utilities distribution system value as shown in table 6.2 the interruption frequency of the system (SAIFI) and the time of interruption duration (SAIDI) for Bahir Dar distribution system are high as compared to others . In the other way the calculated value of average service availability index (ASAI) of the system are less. Table 6.1: Reliability indices of Bahir Dar substation I and II Bahir

Reliability

Dar I

Indices

Bahir

Unit

Calculated value 2013

2014

2015

AVE

SAIFI

inter./yr

_

240

348.652

294.326

SAIDI

h/yr

_

203

168.244

258.448

CAIDI

h/inter

_

0.8458

0.48255

1.1668

ASAI

%

_

0.97682

0.98079

0.9704

EENS

MWh/yr

_

1837.42608

1522.838

1680.13

SAIFI

inter./yr

263.1827

264.43349

194.469

240.695

SAIDI

h/yr

205.4887

147.71302

90.0156

147.7391

CAIDI

h/inter

0.780783

0.5586018

0.46287

0.600754

ASAI

%

0.976542

0.9831377

0.98972

0.983134

EENS

MWh/yr

3325.846

2856.416

1449.894

2544.052

Dar II

73

Table 6.2: Comparison of Bahir Dar distribution system with other utilities Calculated value Reliability Indices

Unit

The study area Average value

Other utilities value

SAIFI

inter./yr

235.848

<1

SAIDI

h/yr

175.692

<187 minute

ASAI

%

0.97994

≥0.9998

According to Ethiopian Electric Agency, Average system standards for reliability in distribution system are set. Average interruption (planned and unplanned) frequency shall not exceed 20 interruption/customer/year and average interruption duration (planned and unplanned) shall not exceed 25hours/customer/year. The standard provided by Ethiopian Electric Agency (EEA) states any customer shall not face more than 20 hours per annum unplanned non momentary interruptions duration and not more than 5 hours per annum planned interruption duration. For interruption frequency, any customer shall not face more than 15 non-momentary and unplanned interruptions and not more than 5 planned interruptions per annum. Bahir dar distribution system SAIDI and SAIFI values are much beyond the Ethiopian and the International standards, shown in Table 6.1and 6.2.The number of power outages (frequency) and durations of each Bahir Dar II feeders for 2013, 2014 and 2015 are summarized as shown in figure 6.3 and 6.4 respectively.

Intrruption frequence

Interruption frequence of feeders for three years 450 400 350 300 250 200 150 100 50 0

2013 2014 2015

GHION

ADDET

TISABAY

PAPYRUS

INDUSTRY

BATA

AIRFORCE

feeders name Figure 6.3: Interruptions frequency of Bahir Dar II feeders for 2013-2015 74

From the figures 6.3 the number of permanent average power outage (frequency) of feeders in 2013, 2014 and 2015 are 238.63, 269.48 and 184 interruptions per year respectively. The average power interruption of feeders for the three years is 230.71 interruptions per year. The duration of power outages for each feeder for the year 2013, 2014 and 2015 respectively are shown in figure 6.4. Interruption duration of feedersfor three years

Interruption duration

700 600

2013

500

2014

400

2015

300 200 100 0

GHION

ADDET

TISABAY PAPYRUS INDUSTRY

BATA

AIRFORCE

feders name Figure 6.4: Interruption Duration of Bahir Dar II feeders for 2013-2015 From the figures 6.4 the duration of permanent average power outage of feeders in 2013, 2014 and 2015 are 225.71, 229.59 and 97.56 hours per year respectively. The average power interruption duration of feeders for the three years is 184.29 hours per year. The number of power outages (frequency) and durations of Bahir Dar I feeders for 2014 and 2015 is summarized as shown in figure 6.5 and 6.6 respectively. From the figures 6.5 the number of permanent average power outage (frequency) of feeders in 2014 and 2015 are 60 and 50.75 interruptions per year respectively. The average power outage (frequency) of feeders for the two years is 55.375 interruptions per year.

75

Interruption frequency of Bahi Dar I feeders

Interruptions frequency

180

2014

160 140

2015

120 100 80 60 40 20 0 SEMAETAT

HAMUSITE

BOILER

GAMBI

feeders name

Figure 6.5: Interruptions frequency of Bahir Dar I feeders for 2014&2015 . Interruption duration of Bahir Dar 1 feeders

Interruption Duration

200 180

2014

160

2015

140 120 100 80 60 40 20 0 SEMAETAT

HAMUSITE

BOILER

GAMBI

feeders name

Figure 6.6: Interruption Duration of Bahir Dar I feeders for 2014&2015

76

From the figures 6.6 the duration of permanent average power outage of feeders in 2014 and 2015 are 87.16316 and 42.06104 hours per year respectively. The average power interruption duration of feeders for the two years is 64.612 hours per year. From the above figures the number of permanent average power outage (frequency) and duration of permanent average power outage for Bahir Dar distribution system feeders is 286.85 interruptions per year and 286.085 hours per year respectively. 6.3.1 Causes of power outage for Bahir Dar distribution system The Cause and reason for the reliability indexes to be high are different in different distribution system. The causes of power outage for Bahir Dar distribution system are maintenance, unknown causes, components failure and windy rain. The sources for these outages include poor jumper clearances; old equipment, wrong use of protection device and shortage of maintenance are majors. The major causes of power outage for each feeder is summarized in figure 6.7. It is not possible to prevent all component failures in the electric system but, it is possible to minimize it. There are different solutions to solve the problems among them, replacing the old equipment with new is one solution and applying schedule maintenance before failure is the other. For unknown fault power outages the type of fault indicated by smart device or actuated relay are over current, earth fault and short circuit from that it is possible to suggest the unknown fault cause. By farther identification of the cause it is possible to minimize the unknown cause for power outage.

77

Number of occurance

Major causes of interruption occurance rate 100 90 80 70 60 50 40 30 20 10 0

86

83 67 56

52

47

46

31 12

5

11

14

11

Ghion

8

4 3

Bata

9

3 0 0

13

11 0 0

Papyrus

Industry

Feeder name Component failure

For maintenance

Windy rain

wind

Fallen tree

Unknown

Figure 6.7: Major causes of permanent interruption rates for selected feeders As shown in table 6.7 average causes of power outage for the four feeders are maintenance 41%, component failure 11%, unknown 37%, windy rain7%, wind 2%and fallen tree 2%. Momentary interruptions are faults which are self-clearing or self-reclosing within 5 minutes or less without any maintenance or repair.

Temporary interruptions 140

number of occurance

120 100 80 60 40 20 0

Ghion

Bata

Papyrus feeders name

Figure 6.8: Total momentary interruption rates

78

Industry

As shown from the figure 6.8 momentary interruption number for Ghion feeder is greater than other feeders. The reasons of interruption and their average percentage frequency and duration of Bahir Dar distribution system for three years (2013, 2014 and 2015) is shown in figures below.The interruption reasons are distribution permanent earth fault (DPEF), distribution permanent short circuit fault (DPSC), distribution transient earth fault (DTEF), distribution transient short circuit fault (DTSC) and operational (OP). From figure 6.9 and 6.10 the reasons for interruption frequency in Bahir Dar distribution system are distribution permanent earth fault (DPEF) and distribution permanent short circuit fault (DPSC) i.e. 27% in Bahir Dar II and 33% in Bahir Dar I substation. Because of distribution transient faults (DTEF and DTSC) 46% in Bahir Dar II and 41% in Bahir Dar I are reasons for interruption frequency.

DPEF 14%

OP 27%

DPSC 13%

DTSC 18%

DPEF

DTEF 28%

DPSC

DTEF

DTSC

OP

Figure 6.9: Reasons for average frequency interruption of Bahir Dar II substation

79

DPEF 15%

OP 26%

DPSC 18% DTSC 26%

DPEF

DPSC

DTEF 15%

DTEF

DTSC

OP

Figure 6.10: Reasons for average frequency Interruption of Bahir Dar I substation

OP 23%

DPEF 31%

DTSC 5% DTEF 11% DPSC 30%

DPEF

DPSC

DTEF

DTSC

OP

Figure 6.11: Reasons for average interruption duration for Bahir Dar II substation

80

DPEF 18%

OP 20%

DTSC 8% DTEF 1% DPSC 53%

DPEF

DPSC

DTEF

DTSC

OP

Figure 6.12: Reasons for average interruption duration of Bahir Dar I substation From the figure 6.11 and 6.12 it may be seen 61% in Bahir Dar II and 71% in Bahir Dar I substation the reasons of frequency interruption are distribution permanent earth fault (DPEF) and distribution permanent short circuit fault (DPSC) . The average percentage of distribution transient faults (DTEF and DTSC) causes 16 % and 9% of interruption frequency in Bahir Dar II and Bahir Dar I respectively. From the figures above operational maintenance is a reason for interruption frequency ( 27% in substation two and 26% in substation one) and interruption duration (23% in substation two and 20% in substation one ),which can be reduced by optimal placement of smart switches and protective devices along with automation system, since maintenance scheduling is one future of automation system.

81

6.4 Predictive reliability assessment using optimal placement of IEDs Following the methodology described in chapter 5, optimal number and position of feeder switch(IEDs) optimization problem is solved using PSO algorithm .Particle swarm optimization(PSO) is implemented using MATLAB R2013a (8.1.0.604) and run on Intel(R) Core(TM) i3-2348 CPU, 2.00 GHz computer to solve the problem. The proposed algorithm was run for minimization of switch cost as the objective function. To obtain the optimal number and position of switches which improves the reliability of the selected feeder. The PSO based algorithm was run with different optimal control settings. The parameters used are the following. 

Population size: 200



Inertia weight: 1



C1:2



C2:2



Dimension of velocity and position :47

Historical data of the distribution network and characteristics of feeder automation can be used to predict long-term behavior of the feeders with and without FA. The failure rate, line length, repair time and switching time is used as input data to determine the outage durations of all sections in a feeder. System parameters used in this optimization are as follows: For the modelling of restoration of distribution system components, historical data of individual components is required. Since the company (EEPU, Bahir Dar) does not include the repair and switching time in a component level. The most basic components of distribution system are transformers and distribution main and lateral lines. For the selected Feeder the repair times and failure rates of transformers is computed based on the MVA rating/KW loading of the transformers. The failure rate of the feeder section is a function of the length and each section has a different length. The repair times and failure rates of the lines is computed after finding the length of each line. Failure rates and repair time of the distribution transformers and the distribution lines is shown in appendix -2 and appendex-3 respectively. The switches used in the distribution system are all pole top gang operated, at a cost of 1,000 USD/switch. The switch cost includes the installation cost. The annual maintenance cost was 82

assumed to be 2% of the annual investment cost and the interest rate 8%. The life of the switch was assumed to be twenty years. A 24.71 % commercial, 42.82 % industrial and 29.58 % residential customer mix was assumed for each load point and 0.5666, 0.6943, 0.5878 birr tariff of residential, commercial, and industrial customers respectively. Main Assumptions considered for the calculations are described below 

Radial operation of the network



All failures are statistically independent



Multiple faults are not represented

For the predictive reliability analysis a Ghion feeder is used. To evaluate the total cost of the system the equations described in network equivalent method, which is defined in chapter four is used. Optimal switch number and the locations for Ghion feeder were determined using the PSO optimization technique, which is defined in section 5.5.1 is used. The predictive reliability analysis is used to calculate reliability indexes (SAIDI, U, ASAI, EENS, AENS) and total cost (the sum of switches cost and cost of EENS). The algorithms were applied for four cases studies and the results are presented below:  when no IEDs switches are allocated,  If 47 IEDs switches are allocated in the Ghion feeder,  When optimal IEDs are used in the Ghion feeder  Allocation of manual switch instead of optimal IEDs in Ghion feeder The optimal number of IEDs are 7 and with optimal position of [19, 26, 31, 33, 36, 39, and 42]. The optimal location of IEDs on reliability model of Ghion 15KV feeder is shown in Figure 6.11 below

83

2

LP1

3

4

5

LP3

6

LP5

7

8 LP7

10

LP9

LP23

30 45

29

47

LP21

LP22

16

33

17

18

LP31

20

19

LP16

LP15

LP32 LP30

36

LP29

LP28 LP27

34

LP20

43

26

15

LP13

44

42

28 27

14

LP12

25 39

LP34

LP40

46

LP11

LP37

LP24

-IED

9

LP41

1

31 11 12 13

LP39

CB

32

37

35 21

LP17

LP18

40 41

24

23

22

LP19

LP35

15KV

LP14

LP33

LP10

LP38

LP8

LP36

LP6

LP26

LP4

LP25

LP2

38

Figure 6.11: The optimal location of IEDs on reliability model of Ghion 15KV feeder

Table 6.3: Reliability Indices for IEDs optimal placement Reliability Indices

Number of Switches 0(null)

47(all)

optimal (7 IEDs)

7(manual switch)

SAIDI[hr/Yr]

174.5898

75.7237

89.0590

101.4568

U [hr/yr]

173.2889

67.8956

85.3543

98.0837

ASAI

0.9801

0.9914

0.9898

0.9884

TCOST[Birr]

324,290

345,220

196,020

204,430

EENS [KW]

547.4214

330.2767

342.7454

372.0873

AENS[W]

77.5384

46.7814

48.5475

52.7036

84

In this thesis intelligent electronic devices (IEDs) are installed on selected 15 KV Bahir dar distribution system feeder. These devices increased the switching and reclosing capability of the feeder. The predictive Reliability analysis as result of new installed devices are shown in Table 6.3. The smart switch placement in selected feeder resulted in reduction of 48.98% in SAIDI and 39.55% in total supply interruption. The current SAIDI value is reduced to 89.0590 hr./yr. From table 6.3 we can see that SAIDI and total supply interruption cost of the feeders with automatic switches (IEDs) compared to manual switch placed instead of optimally placed automatic switches are improved by 12.21% and 4.11% respectively .The total supply interruption cost (TSC), , which is defined in section 5.5 decreased by 4.11% as compared optimal placed IEDs with optimal manual switch placed in the Ghion feeder because the Expected Energy not supplied (EENS) for manual switches are 7.88% greater as compared to the automatic switches. When we compare the result in table 6.3 with the result in [38] ( SAIDI 97.02 hr./yr and TSC 202,202 birr ) In which manual sectionalizer switch optimal placement proposed as reliability improvement method on the study area covered by this thesis, optimal smart switch placement shows 8.205% and 32.897% improvement in SAIDI and the total interruption cost (TSC) respectively. Even if they have 50% difference in price. The PSO algorithm used during the simulation study for the optimal placement of automatic switches of the distribution network converged in 48 iterations and took 61.695 seconds to complete the search. Which is good convergence characteristic.

85

CHAPTER 7: CONCLUSION AND RECOMMENDATION 7.1 Conclusion The quality and reliable power supply have a dominant effect in the development of the country socially and economically since, every technology used by customers is dependable on electrical power. To supply reliable and efficient power for the customers the power system networks could be planned, redesigned and reconnected by evaluating their past performance. To achieve this goal reliability assessment is necessary for system planning based on reliability cost and worth assessment approach. In this thesis historical reliability analysis for Bahir Dar radial distribution system is carried out using Ethiopian electric utility interruption data for the years 2013, 2014 and 2015. The historical analysis reliability indices, which include SAIDI, SAIFI, EENS, and ASAI, are used to measure the performance of the distribution system. The system reliability indices (SAIFI-235.848 int/yr, SAIDI -175.692 hr/yr and ASAI -0.97994 %) of the study area show that Bahir dar city distribution system is unreliable as compared to standard practices by other countries and Ethiopia’s reliability requirement. Based on interruption data obtained from EEPU potential causes of interruption are identified which helped to select which feature of smart grid in distribution system can tackle these problems. To improve reliability of the system various switch types either manual or automatic switch are there but reliability of the distribution system greatly depends upon the placement and number of switches in distribution system. The reliability improvement techniques suggested as smart switch can minimize the power outage frequency and duration this improvement has a great impact socially and economically both for the utility and the consumers. Predictive reliability analysis, considering placement of smart switches, which is controlled by SCADA system is carried out for a selected distribution feeder to investigate the reliability improvement of the feeder. In this thesis the optimum number and location of automatic switches that improve reliability of the distribution system is obtained using binary particle swarm optimization. A reliability network equivalent technique analytical method has been implemented for the system analysis. The 86

predictive reliability analysis of the study considers 15 kV Ghion feeder of Bahir Dar distribution system is selected as the test system. From the test results, the proposed technique presents much better reliability in comparison with existing system (48.98 % reduction of SAIDI) along with improvement in cost. To conclude, reliability of a distribution network can be improved by ultimate use of smart grid features. The reliability of a distribution network has direct relationship to the number, position and types of devices installed in distribution system. This study also shows that, particle swarm optimization is an effective method to optimize feeder switch locations based on minimum interruption cost for reliability improvement in power distribution system. 7.2 Recommendation As shown in this thesis the deployment of the smart grid technology improves the reliability performance of the distribution system. For a feeder with full load, long line, large proportion of commercial load and has a loop with other feeders as backup supply, automation often is very attractive. But for short feeders, it is not economical. To have a more exact planning in reliability improvement methods in distribution system , Ethiopian electric utility should do further study about the loads in the system to formulate its own interruption cost and know about customer’s willingness to pay for better reliability. Since the more information we get about the loads and customer’s willingness to pay for better reliability, the more precise the planning is. Further researches can be done on the reliability and economic analysis of Smart grid devices, automation system, distributed generator and intelligent sources of data and information.

87

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Appendix-1: Interruption data for Bahir Dar distribution system

1.1. For Bahir Dar II substation from 2013 to 2015 DPEF F 32 49 16 27 77 31 55

DPSC DTEF DTSC OP D[Hr] F D[Hr] F D[Hr] F D[Hr] F 13.1 32 16.04 32 10.17 4 4.59 82.32 37 77.55 36 101.48 33 55.06 52.67 27 55.92 31 43.02 3 0.01 17.91 30 29.79 50 23.57 16 11.06 34.04 31 33.75 85 40.43 13 2.34 11.33 40 48.34 66 61.86 40 30.21 55.43 51 40.65 32 34.37 9 10.47

GHION ADDET TISABAY PAPYRUS INDUSTRY BATA AIRFORCE

11 52 12 18 34 22 26

DPSC DTEF DTSC OP D[Hr] F D[Hr] F D[Hr] F D[Hr] F 17.04 22 29.02 53 7.33 43 1.02 251.54 28 173.32 58 4.42 36 11.88 98.52 24 207.02 10 0.37 35 0.93 30.13 16 15.28 62 1.86 20 1.27 65.03 25 49.15 112 4.78 112 2.23 30.69 19 21.45 78 1.51 47 1.16 35.26 26 27.57 129 8.02 58 1.2

F GHION ADDET TISABAY PAPYRUS INDUSTRY BATA AIRFORCE

D [Hr.] F 17 12.4376 38 92.17 12 17.3932 30 38.42 27 33.52 23 24.3876 15 10.49

GHION ADDET TISABAY PAPYRUS INDUSTRY BATA AIRFORCE DPEF F

D [Hr.] F 17 9.97514 23 24.5707 14 41.8808 21 13.88 30 29.37 19 33.89 18 19.54

40 46 19 58 74 51 56

D [Hr.] F 3.845 2.184 0.458 3.83 6.694 4.219 7.487

11 37 19 58 94 38 34

D [Hr.] F 0.185 12.554 14.59 3.67 11.3 4.301 0.615

76 47 34 57 38 91 64

TOTAL D[Hr] F D[Hr] F D[Hr] 68.11 176 112.01 211.2 134.412 68.3 202 384.71 242.4 461.652 26.97 111 178.59 133.2 214.308 35.87 180 118.2 216 141.84 40.42 244 150.98 292.8 181.176 45.34 268 197.08 321.6 236.496 34.18 211 175.1 253.2 210.12

52 64 23 57 55 79 54

TOTAL[10M] TOTAL[YEAR] D[Hr] F D[Hr] F D[Hr] 19.44 181 73.85 217.2 88.62 69.08 238 510.24 285.6 612.288 31.63 104 338.47 124.8 406.164 25.13 173 73.67 207.6 88.404 38.21 338 159.4 405.6 191.28 33.48 245 88.29 294 105.948 23.32 293 95.37 351.6 114.444

53 56 39 80 47 52 22

D [Hr.] F 44.8474 36.794 49.4396 29.361 21.0681 15.8715 7.7025

138 200 103 247 272 183 145

D [Hr.] F 71.2901 168.273 123.762 89.161 101.952 82.6692 45.8345

138 200 103 247 272 183 145

D [Hr.] 71.2901 168.273 123.762 89.161 101.952 82.6692 45.8345

1.2. For Bahir Dar I substation in 2013 and 2015 DPEF F SEMAETAT

2006 HAMUSITE BOILER GAMBI sum 2007 SEMAETAT HAMUSITE BOILER GAMBI sum

Frequency and Duration of Interruption DPSC DTEF D [Hr.]

15 20 4 0 39 0 21 5 3

6.94 55.75 3.3 0 65.99 0 24.49 1.26 1.96 27.71

F

D [Hr.]

25 16 0 1 42 0 38 0 0

17.5163 85.7164 0 111.1 214.333 0 57.1 0 0 38

F

D [Hr.]

5 26 1 0 32 0 33 1 0

57.1

0.9 2.1 0.1 0 3.1 0 3.4791 0.02 0 34

93

3.4991

DTSC F

OP D [Hr.]

19 43 4 0 66 0 36 9 2

1.82 3.66 1 0 6.48 0 16.5 19.237 0.06 47

35.797

F

TOTAL D [Hr.]

20 36 3 2 61 12 43 0 0

15.97 33.56 6.67 2.55 58.75 15.4721 28.666 0 0 55 44.13806

F

D [Hr.]

84 141 12 3 240 12 171 15 5

43.1463 180.786 11.07 113.65 348.653 15.4721 130.235 20.517 2.02

203 168.2442

Appendex-2: Transformers reliability data for Ghion distribution feeder Transformer Number

Failure Rate [f/yr]

Repair Time [h]

1 15 16 19 20 22 27 28 29 31 32 33 34 37 39 40 41

.14

8

2 3 4 5 6 7 8 9 10 11 12 13 14 17 18 21 23 24 25 26 38

.16

6

30 35 36

.84

6

Appendex-3: Reliability line data for Ghion distribution feeder Section

Length Failure Rate

Repair

Section

Length Failure Rate

Time [h]

Number

[km]

[f/yr.km]

Repair

Number

[km]

[f/yr.km]

1

1.25

1.49

6

25

0.47

1.49

6

2

0.21

1.49

6

26

0.22

1.49

6

3

0.56

1.49

6

27

0.38

1.49

6

4

0.13

1.49

6

28

0.03

1.49

6

5

0.71

1.49

6

29

0.21

1.49

10

6

0.15

1.49

6

30

0.21

1.49

6

7

0.10

1.49

6

31

0.50

1.49

6

8

0.77

1.49

6

32

0.09

1.49

6

9

0.16

1.49

6

33

0.39

1.49

6

10

0.06

1.49

6

34

0.10

1.49

10

11

0.16

1.49

6

35

0.13

1.49

6

12

0.50

1.49

6

36

0.32

1.49

6

13

0.09

1.49

6

37

0.16

1.49

6

14

0.20

1.49

6

38

0.14

1.49

6

15

0.10

1.49

6

39

0.02

1.49

6

16

0.26

1.49

6

40

0.41

1.49

6

17

0.12

1.49

6

41

0.10

1.49

6

18

0.13

1.49

6

42

0.03

1.49

6

19

0.10

1.49

6

43

0.19

1.49

6

20

0.26

1.49

10

44

0.06

1.49

6

21

0.17

1.49

6

45

0.24

1.49

6

22

0.37

1.49

6

46

0.13

1.49

6

23

0.23

1.49

6

47

0.05

1.49

6

24

0.02

1.49

6

94

Time [h]

95