Design and Simulation Studies of D-STATCOM for Mitigating Voltage

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Design and Simulation Studies of D-STATCOM for Mitigating Voltage Sag Problem by Using Genetic Algorithm, Fuzzy Inference System, and Proportional Integral

THESIS

Organized to Meet a Part of the Requirements to Achieve the Master Degree of Mechanical Engineering Department / Specialization of Electrical Engineering for Renewable Energy

By HAMZA JABER MOHAMED S951208507

GRADUATE PROGRAM MECHANICAL ENGINEERING DEPARTMENT SEBELAS MARET UNIVERSITY SURAKARTA commit to user 2014

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ORIGINALITY AND PUBLICATION STATEMENT

I Declare that: 1.

Thesis entitled: ―Design and Simulation Studies of D-STATCOM for Mitigating Voltage Sag Problem by Using Genetic Algorithm, Fuzzy Inference System, and Proportional Integral“ is my work and free of plagiarism, and there is no scientific papers that have been asked by others to obtain academic degrees and there is no workor opinion ever written or published by another person except in writing used as areference in this text and a reference source as well as mentioned in the bibliography.If at a later proved there is plagiarism in scientific papers, then I am willing to accept sanctions in accordance with the provisions of the legislation (Permendiknas No 17, tahun 2010)

2.

Publication of some or all of the contents of the thesis or other scientific forums and permission must include the author and the team as a supervisor. If within at least one semester (six months after the examination of the thesis) I did not make the publication in part or entire of this thesis, the Program in Mechanical Engineering of UNS has the right to publish in a scientific journal published by Study Program in Mechanical Engineering of UNS. If I violateofthe provisions of this publication, then I am willing to get an academic sanction.

Surakarta, August 2014

HAMZA JABER MOHAMED NIM: S9512085107 commit to user

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HAMZA JABER MOHAMED, Student Number: S951208507 Design and Simulation Studies of D-STATCOM for Mitigating Voltage Sag Problem by Using Genetic Algorithm, Fuzzy Inference System, and Proportional Integral Supervisor I: Prof. Muhammad Nizam, S. T., M. T., Ph.D. Supervisor II: Dr. Miftahul Anwar, SSi, M Eng. Thesis: Mechanical Engineering Department, Graduate School, Sebelas Maret University. Abstract Power quality issues are gaining significant attention due to increase in the number of loads. In fact, voltage sags problem is the most occurring power quality problems. These events are usually associated with a fault somewhere on the supplying power system. Therefore, the aim of this study was to assess the applicability of Distribution Static Compensator (D-STATCOM) for the mitigation of voltage sag problem. Three methods were used in control to get the performance of mitigating voltage sag, namely Genetic Algorithms (GA), Fuzzy Inference system (FIS), and Proportional Integral (PI) to determine the injection of voltage and comparison of timely responses. As a result, D-STATCOM injected the voltage into the distribution system to recover the voltage sag problem at single-phase, two-phase, and threephase fault scenario. The results showed that Genetic Algorithms were capable to overcome the voltage by 98.50%, 98.40%, and 94.15% in single-phase, two-phase, and three-phase appropriately. On the other hand, Fuzzy Inference System was capable to overcome the voltage by 98.15%, 92.15%, and 90.75% in single-phase, two-phase, and three-phase appropriately. While, proportional Integrative was capable to overcome the voltage by 98.05%, 85.80%, and 80.00% in single-phase, two-phase, and three-phase appropriately. Among them, the best performance was obtained from the Genetic Algorithms method in order to mitigate voltage sag. Consequently, D-STATCOM was substantiated as a fancy compensator for the reactive power requirement of the load. Furthermore, it is to be hoped that in future this proposed distribution system device will be established as a new proficient customer power device commercially.From the result analysis, it can be concluded that GA showed to be a better performance for solving voltage sag problem than FIS and PI controller.

Keywords: Distribution Static Compensator (D-STATCOM), Proportional Integral (PI), Fuzzy Inference Systems (FIS), Genetic Algorithms (GA), Voltage Sag.

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PREFACE I would like to express my greatest appreciation to my supervisors, Prof. Muhammad Nizam, S.T., M.T., Ph.D and Dr. Miftahul Anwar, SSi, M Eng for their guidances, support and encouragements throughout my entire Master study. Their meticulous attention to details, incisive but constructive criticisms and insightful comments have helped me shape the direction of this thesis in the form presented here, on. I am also thankful to them for their strong supports in other aspects of life than research. I would also like to convey my gratitude to the head of mechanical engineering department Dr. techn Suyitno. I deeply appreciate my parents and my family. Their love and encouragement light up many lonely moments in my life as a graduate student away from home and have been the source of courage when I was down. I would like to express my sincere thanks to all my friends and colleagues in the study. Their support, friendship and encouragement made my Master study a journey of happiness. Last, but not least, I am grateful to every individual who has helped me in one way or another during my master study.

Surakarta, August 2014

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CONTENT LIST

TITLE ....................................................................................................................

i

APPROVAL PAGE ..............................................................................................

ii

CONTENT LIST ...................................................................................................

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ORIGINALITY AND PUBLICATION STATEMENT .......................................

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ABSTRACT ........................................................................................................

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PREFACE .............................................................................................................

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LIST OF CONTENTS .........................................................................................

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LIST OF FIGURES ..............................................................................................

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LIST OF TABLES ...............................................................................................

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CHAPTER I

INTRODUCTION ....................................................................

1

1.1

Background .....................................................................

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1.2

Problems statements ........................................................

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1.3

Objective .........................................................................

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1.4

Benefit .............................................................................

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1.5

Research Limitation ........................................................

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1.6

Contribution ....................................................................

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CHAPTHER II THEORY ..................................................................................

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2.1

Defined of Power Quality ...............................................

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2.2

Power Quality Standards .................................................

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2.3

Power Quality Problems Nature and Solutions ...............

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2.4

Voltage sag ......................................................................

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2.5

Distribution Static Compensator (D-STATCOM) ..........

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2.6

Operating Principle of the D-STATCOM .......................

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2.7

Proportional Integrative (PI) CONTROLLER ................

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2.8

Fuzzy Inference System (FIS) ........................................

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2.8.1 Fuzzification ..........................................................

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2.8.2 Rule Evaluation......................................................

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2.8.3 Inference Engine .................................................... commit to user

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2.8.4 Defuzzification.......................................................

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2.8.5 Output ....................................................................

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Operation of FIS ..............................................................

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2.10 Genetic Algorithm (GA) .................................................

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2.11 Genetic Algorithm Basics ...............................................

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RESEARCH METHODOLOGY .............................................

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3.1

Simulink Model for D-STATCOM using PI...................

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3.2

Simulink Model for D-STATCOM using FIS ................

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3.3

Genetic Algorithm (GA) .................................................

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3.2

Flow Chart of the Research Methodology ......................

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RESULT AND ANALYSIS .....................................................

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4.1. 3-Dimensions Surface of FIS ..........................................

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4.2

Comparison of D-STATCOM with PI, FIS and GA .......

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CONCLUSION AND SUGGESTION .....................................

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5.1

Conclusion .......................................................................

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5.2

Suggestion .......................................................................

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REFERENCE .......................................................................................................

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2.9

CHAPTER III

CHAPTER IV

CHAPTER V

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TABLE LIST

Table 1

A Summary of Power Quality Problems Nature and Solutions .........

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Table 2

Fuzzy Rules .......................................................................................

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Table 3

System Parameters ............................................................................... 20

Table 4

Comparison of D-STATCOM with PI, FIS and GA............................ 30

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FIGURE LIST

Figure 2.1 Shows an rms representation of the voltage sag; sag starts when the voltage ..................................................................................................

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Figure 2.2 Schematic Representation of D-STATCOM........................................

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Figure 2.3 Basic Diagram of The DSTATCOM ...................................................

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Figure 2.4 Simulink Model of PI Controller. ........................................................ 11 Figure 2.5 Fuzzy Inference System ....................................................................... 12 Figure 2.6 FIS Scheme .......................................................................................... 14 Figure 2.7 Input1 membership function of FIS .................................................... 15 Figure 2.8 Input2 membership function of FIS. .................................................... 15 Figure 2.9 Output membership function of FIS..................................................... 15 Figure 3.1 Simulink Model for D-STATCOM ...................................................... 20 Figure 3.2 Simulink Model for D-STATCOM using FIS ..................................... 21 Figure 3.3 Simulink Model for D-STATCOM using GA ..................................... 22 Figure 3.4 Flow chart of the thesis. ....................................................................... 23 Figure 4.1 3-Dimensions Surface of FIS ............................................................... 25 Figure 4.2 PI Controller with D-STATCOMand without D-STATCOM ............. 26 Figure 4.3 Single phase fault scenario Result of comparison uses the PI Controller, FIS and GA. ....................................................................... 27 Figure: 4.4 Two phase fault scenario Result of comparison use the PI Controller, FIS and GA........................................................................................... 28 Figure 4.5 Three phase fault scenario result of comparison uses the PI Controller, FIS and GA. ....................................................................... 29

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CHAPTER I INTRODUCTION

1.1

Background Power quality is one of major concern in the present era. It has become

important, mostly, with the introduction of flexible alternating current transmission systems (FACTS), whose performance is very sensitive to the quality of power supply. The power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that results in a failure of end use equipment. One of the major problems dealt here is the voltage sag. To eliminate this problem, custom power devices are used. One of those devices is the distribution static compensator DSTATCOM, which is the most efficient and beneficial modern custom power device used in the distribution system. Its appeal includes lower cost, smaller size, and fast response to the disturbance (Deshmukh, and Dewani, 2012). Voltage sag is one of the most occurring power quality problems.Voltage sag is caused by a fault in the utility system, a fault within the customer’s facility or a large increase of the load current, i.e, starting a motor or transformer energizing. Voltage sags are one of the most occurring power quality problems. For an industry, voltage sags occur more often and cause severe problems andeconomical losses. Utilities often focus on disturbances fromend-user equipment as the main power quality problems. There are different ways to mitigate power quality problems in transmission and distribution systems. Among these, the D-STATCOM is one of the most effective devices. The D-STATCOM protects the utility transmission or distribution system from voltage sags and/or flicker caused by rapidly varying reactive current demand. In utility applications, a D-STATCOM provides lagging reactive power to achieve system stability (Kadam et al., 2012). The low distribution static compensator (D-STATCOM) was presented based on the application of space vector pulse width modulation (SVPWM) for three phases voltage Source Converter (VSC) and it is the standard PWM techniques to utilize the DC-AC power conversion and proposed a control system based on park technique commit to user which is a scaled error on the between source side of the D-STATCOM and its

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reference for sag correction. (Kumar and Nagaraju, 2007) and the importance of this thesis is to resolve voltage sag problem manifested in voltage/current or frequency deviations that result in failure of customer equipment. and to present the model of the custom power device, namely, D-STATCOM and its control application to mitigate voltage sag, the proposed D-STATCOM model was developed using MATLAB/Simulink environment, Simulation results were presented to demonstrate the voltage sag of the D-STATCOM (Hussain, and Praveen, 2012).

1.2 Problem Statement Voltage sag is the most significant power quality problem in the distribution system. As a consequence, various advance devices, such as capacitor banks, dynamic voltage restorer (DVR), unified power quality conditioner (UPQC) even DSTATCOM, have already been applied to alleviate the voltage sag problem. However, yet an efficient method to resolve the voltage sag problem. The problem statements of the study are: 1. How to design D-STATCOM by using MATLAB/Simulink environment. 2. How to study the performance D-STATCOM for determining injection of voltage & time response.

1.3 Objective The objectives of this study are: 1. To design D-STATCOM using MATLAB/Simulink environment. 2. To study the performance D-STATCOM using genetic algorithms (GA), Proportional Integral (PI) controller and Fuzzy Inference system (FIS) in determining the injection of voltage & time response.

1.4 Benefit The benefits of this study are: 1- To develop a proficient method for the Mitigation of voltage sag problem and increase its usage widely for commercial purposes and order to get low cost commit to user and fast response in distribution system.

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2- To show the advantages and effectiveness of Genetic Algorithms, FIS, PI controller in injection of current.

1.5 Research limitation The MATLAB/SIMULINK with its Fuzzy Logic Toolbox and Optimization Algorithms' software were used to control D-STATCOM which is used for the enhancement of power quality in distribution system based on GA, FIS, PI controller in order to alleviation voltage sag.

1.6 Contribution This research is going to be set up three models, namely D- STATCOM with PI, D-STATCOM with FIS and D-STATCOM with GA. Afterwards, the performance of the three methods will be analyzed and compared among the three methods. Finally, the best method will be selected among the three systems. Consequently, the exploration of the best one among the three approaches is the key contribution of this research.

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CHAPTER II THEORY

Power quality is anything that affects the voltage, current and frequency of power being supplied to the customers. Constant voltage is the prime requirement of the customer because if the voltage is lower than the tolerable limits it will cause overheating of the equipment and less illuminating power to the lighting load. If it is higher than the limit it causesa material, insulation breakdown, reduces the life of lighting load, etc. Lightning (transient over voltages), switching over voltages (i.e capacitor switching, disconnection of lines), short circuit faults (such as voltage sags) and short interruptions are the main causes of voltage deviations which lead to permanent damage of the equipments (Singh, and Surjan, 2013). Reactive power control is a critical consideration in improving the power quality of power systems. Reactive power increase transmission losses, degrades power transmission capability and decreases voltage regulation at the load end. In the past, Thyristor-Controlled Reactors (TCR) and Thyristor-Switched Capacitors were applied for reactive power compensation. However, with the flexible alternating current transmission systems FACTS devices.

It has been proven that the D-

STATCOM is a device capable of solving the power quality problems. One of the power quality problems that always occur in the system is the three phase fault caused by a short circuit in the system, switching operation, starting large motors and etc. This problem happens in milliseconds and because of the time limitation, it requires the D-STATCOM that has continuous reactive power control with fast response. Nowadays, due to more sensitive nature of loads use of custom power devices/custom controllers (electronics based) to maintain power quality has become essential. Several research papers and reports addressed the subject of improving power in the distribution system by the custom power devices. The following research papers and reports present a brief review of the work undertaken so far control strategies and methods for the D-STATCOM. These models can also aid commit to user instructors in teaching power quality.

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2.1 Defined of Power Quality Power quality is defined in the IEEE 100 Authoritative Dictionary of IEEE Standard Terms as The concept of powering and grounding electronic equipment in a manner that is suitable for the operation of that equipment and compatible with the premise wiring system and other connected equipment (Khalid, and Dwivedi, 2010).

2.2 Power Quality Standards Power quality is a worldwide issue, and keeping related standards current is a never-ending task. It typically takes years to push changes through the process. Most of the ongoing work by the IEEE in electrical power quality. Standards development has shifted to modifying Standard 1159-1995 (Golkar, et. al., 2008)

2.3 Power Quality Problems Nature and Solutions It is observed from Table 1 that, the traditional solutions used to improve power quality arelargely static. While these problems can vary from half of a cycle 1 sec to one minute, the fastest solution should 0 sec a bandwidth much lower than 1 sec.

Table 1 A Summary of Power Quality Problems Nature and Solutions(Hingorani, 1991). Power Quality Problems Voltage Sags

Characteristic 0.1 — 0.9 Decrease in Per unit RMS Value at 0.5 cycles to 1 mm.

Voltage Swells

1.1 — 1.8 Increase in Per unit RMS Value at 0.5 cycles to 1 mm. Sudden change in voltage and current signals in steady state.

Solution DSTATCOMs, UPS, Ferroresonant Transformers, and Backup Generators. DSTATCOMs, UPS, Ferroresonant Transformers, and Backup Generators. Filters, Isolation Transformers, and Surge Arresters.

Sudden change in voltage and current signals at (< 5 commit to > 500 kHlz).to user

Filters, Isolation Transformers, and Surge Arresters.

Impulsive Transients

Oscillatory Transients

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Over/Undervoltages

Harmonic Distortion

Voltage Flicker

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> 110% Increase and <90% Decrease in RMS voltage for > 1 mm. Refer to IEEE Std 519 — 1992 for allowed THD (±5%) and TDD (± 10%). Variation in Magnitude with frequency.

Voltage Regulators and Ferroresonant Transformers. DSTATCOM as an Active Filter, Passive Filters and Ferroresonant Transformers. DSTATCOMs and SVCs.

2.4 Voltage sag

IEEE definition of voltage sag is sudden and short duration reduction in RMS value of the voltage at the point of electrical system between 0.1 to 0.9 Pu in duration from 0.5 cycles of 1 minute. The amplitude of voltage sag is the remaining value of the voltage duringsag. Voltage sags are considered the most severe disturbances to industrial equipment.

Figure 2.1 shows an rms representation of the voltage sag; sag starts when the voltage (Davis, et. al., 1998)

Falls below the threshold voltage V

thr

(0.9 P.u) at T1. Sag continues to T2 at which

the voltage reaches to a value above the threshold value. Duration of the sag is (T2T1) and its magnitude is Vsag (Davis, et. al., 1998) commit to user

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2.5 Distribution Static Compensator (D-STATCOM) A distribution static compensator (D-STATCOM) is the most efficient and effective modern custom power device used in power distribution system. Its appeal includes lower cost, smaller size, and its fast dynamic response to the disturbance. DSTATCOM consists of a voltage source converter (VSC), a DC energy storage device (ESD), a coupling transformer connected in shunt to the distribution system through a coupling transformer. The VSC converts the DC voltage across the storage device into a set of three phase AC output voltages. These voltages are in phase and coupled with the AC system through the reluctance of the coupling transformer. Suitable adjustment of the phase and magnitude of the D-STATCOM output voltages allows effective control of active and reactive power exchanges between the D-STATCOM and the AC system. Such configuration allows the device to absorb or generate controllable active and reactive power. As shown in Figure 2.2 (Rambabu, et. al., 2011).

Figure 2.2 Schematic Representation of D-STATCOM (Rambabu, et. al.,2011).

2.6 Operating Principle of the DSTATCOM Basically, the D-STATCOM system is comprised of three main parts: a VSC, a set of coupling reactors and a controller. The basic principle of a D-STATCOM installed in a power system is the generation a controllable Ac voltage source by a commit to of user

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(VSC) connected to a DC capacitor (ESD). The AC voltage source, in general, appears behind a transformer leakage reactance. The active and reactive power transfer

between

the

power

system and

the D-STATCOM is caused by the

voltage difference across this reluctance. The D-STATCOM is connected to the power system at a Point of Common Coupling (PCC), where the voltage-quality problem is a concern. All required voltages and currents are measured and are fed into the controller to be compared with the commands. The controller then performs feedback control and outputs a set of switching signals to drive the main semiconductor switches (IGBT’s, which are used at the distribution level) of the power converter accordingly. The basic diagram of the D-STATCOM is illustrated in Figure 2.2

Figure 2.2 Basic diagram of the D-STATCOM (Reddy, and Laxmi, 2012) The AC voltage control is achieved by firing angle control. Ideally the output voltage of the VSC (where the D-STATCOM is connected) voltage. In steady state, the DC side capacitance is maintained at a fixed voltage and there is no real power exchange, except for losses. There are two control objectives implemented in the DSTATCOM. One is the AC voltage regulation of the power system at the bus where the DSTATCOM is connected.Inthe conventional control scheme, there are two voltage regulators designed for these purposes: AC voltage regulator for bus voltage control and DC voltage regulator for capacitor voltage control. In the simplest strategy, both the regulators are proportional integral (PI) type controllers. The commit to user

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reference values for these currents are obtained by separate PI regulators from DC voltage and AC-bus voltage errors, respectively (Reddy, and Laxmi, 2012). D-STATCOM is a shunt device which has the capability to inject reactive current. The reactive power output of a D-STATCOM is proportional to the system voltage rather than the square of the system voltage, as in a capacitor. This makes the D-STATCOM more suitable rather than using capacitors. Though storing energy is a problem for long term basis, considering real power compensation for voltage control is not an ideal case. So most of the operations are considered steady state only and the power exchange in such condition is reactive. as shown in Figure 2.3 (Ramesh, et. al., 2013). From the injected shunt current component IOUT correct the faults by adjusting the impedance component ZTh= (RTh+JXTh). The value of I controlled by the converter output voltage. The injected shunt current component I out can be written as follows.

Iout = IL-IS

(2.1)

Is = (VTh –VL)/ZTh

(2.2)

Iout= IL-IS = IL- {(VTh –VL)/ZTh}

(2.3)

Where

The complex power injection expressed by the D-STATCOM is given by Sout=VLI out Where is: Iout is current at output IS is current source IL is current at load Vl is Voltage at load VTh is Voltage Thevenin Sout is complex power injection ZTh is Thevenin impedance

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(2.4)

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The above equation expressed as the effectiveness of the D-STATCOM in the elimination of fault condition depending up on the value of the ZTh. Similarly when the injection of the Iout, the load voltage VLwill be minimized and if there elitists a low value of reactive current injection the total complex power at the fault decreases. So, in order to increase the voltage level during fault condition, injection of shunt component Iout should be must and ZTh should be minimized (Ramesh, et. al.,2013).

2.7 Proportional Integrative (PI) Controller The aim of the control proportional integrative (PI) scheme is to maintain constant voltage magnitude at the point where a sensitive load is connected under system disturbances. The control system only measures the root mean square (R.M.S) voltage at the load point, i.e., no reactive power measurements are required. The VSC switching strategy is based on a sinusoidal pulse width modulation (PWM) technique which offers simplicity and good response. Since custom power is a relatively lowpower application, PWM methods offer a more flexible option than the fundamental frequency switching (FFS) methods favored inflexible alternating current transmission systems (FACTS) applications. Besides, high switching frequencies can be used to improve on the efficiency of the converter, without incurring significant switching losses. The controller input is an error signal obtained from the reference voltage and the RMS value of the terminal voltage measured. Such error is processed by a PI controller the output is the angle δ, which is provided to the PWM signal generator. It is important to note that in this case, indirectly controlled converter, there is active and reactive power exchange with the systemsimultaneously an error signal is obtained by comparing the reference voltage with the RMS voltage measured at the load point. The PI controller process the error signal generates the required angle to drive the error to zero, i.e., the load RMS voltage is brought back to the reference voltage(Patil, and Madhale, 2007). The system is composed by a 230 kV, 50 Hz generation system, represented by thevenin equivalent, feeding two transmission lines through an 2-winding transformer connected in Y/ ∆ 230/115 kV.Such transmission lines feed one commit to user

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distribution system through two transformers connected in Y/ ∆, 115/11 KV. as shown in Figure 2.4

Figure 2.4 Simulink Model of PI Controller ( Patil, and Madhale, 2007) 2.8 Fuzzy Inference System (FIS) Fuzzy inference systems (FIS) are one of the most famous applications of fuzzy logic and fuzzy set theory. They can be helpful to achieve classification tasks, offline process simulation and diagnosis, online decision support tools and process control. The strength of FIS relies on their twofold identity. On the one hand, they are able to handle linguistic concepts. On the other hand, they are universal approximates able to perform nonlinear mappings between inputs and outputs. These two characteristics have been used to design two kinds of FIS. The first kind of FIS to appear focused on the ability of fuzzy logic to model natural language. These FIS contain fuzzy rules built from expert knowledge and they are called fuzzy expert systems or fuzzy controllers, depending on their final use. Prior to FIS, expert knowledge was already used to build expert systems for simulation purposes. These expert systems were based on classical Boolean logic and were not well suited to managing the progressiveness in the underlying process phenomena. FIS allows grading rules to be introduced into expert knowledge based simulators. It also points out the limitations of human knowledge, particularly the difficulties in formalizing interactions in complex processes (Sumalatha, et. al., 2011). Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping, then provides a basis from which decisions can be made, or patterns discerned (Ramnath, et. al.,2010). The fuzzy inference system is show in Figure 2.5 commit to user

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Fuzzy Rule Base Input

Fuzzifier

Defuzzifier

Output

Inference Engine

Figure 2.5 Fuzzy Inference System (Ramnath, et. al., 2010) A. Fuzzification The fuzzification process is performed during run time and consists of assigning membership degrees between 0 and 1 to the crisp inputs of working process (WIP) inventory, floor space required (FSR), and operator walking distance (OWD). B. Rule Evaluation The rule evaluation process consists of using the fuzzy value obtained during fuzzification and evaluating them through the rule base inorder to obtain a fuzzy value for the output. The rule evaluation follows theform of if (condition x) and (condition y) then (result z) rules are applied.Basically the use of linguistic variables and fuzzy IF-THEN rules utilize the tolerance for imprecision and uncertainty mimicking the ability of the human mind to summarize data and focus on decision-relevant information and are generated from expert Knowledge. C. Inference Engine The inference engine executes the fuzzy operation by using fuzzified data from fuzzifier with if-then rules. D. Defuzzification The Defuzzification process consists of combining the fuzzy values obtained from the rule evaluation step and calculating the reciprocal in order to get one crisp value.

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E. Output The crisp value obtained as the result of Defuzzification process is the output value.

2.9 Operationof FIS When the rule evaluator is first triggered by the start signal, it checks the input registers. If they are ready, the evaluation begins by starting to retrieve the rules from the memory one by one in order. Each rule is then decoded and ready to be executed. Execution of a rule first begins by sending the membership address to the membership memory, which in turn sends the membership function of the fire strength calculator module. The calculator computes the fire strength of each input of that rule and sends it to the min/max evaluator with a control signal sent to the rule evaluator indicating the fire strength is calculated and ready. The weight of the output of each rule is also computed and presented to the multiplier directly. The rule evaluator then sends a control signal to the Min/Max evaluator. The resulting fire strength of the evaluator is presented to both the multiplier and the summer. Then a control signal indicating the end of the operation evaluation is generated. Error Calculation The error is calculated from the difference between supply voltage data and the reference voltage data. The error rate is the rate of change of error (Prasad, at. el.,2013). The error and error rate are defined as: Error = Vref

– VS

Error rate = error (n) – error (n-1) Where is: Vref is voltage References. VS is voltage Source. Error is Error supply. Error rate is Error rate supply.

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(2.5) (2.6)

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Figure 2.6 (FIS) Scheme (Prasad, et. al., 2013).

The aim of the control system is to maintain voltage magnitude at the point where a sensitive load is connected under system disturbances. Voltage sag is created at load terminals via a three-phase fault.The above voltage problems are sensed separately and passed through the sequence analyzer.The control system of the general configurationtypically consists of a voltage correction method which determines the reference voltage injected by D-STATCOM. FIS has two inputs and one output, the input consisting of 5 members and output fuzzy consists of 5 members. Where the input variables in the range [-5 5], while the output variable in the range [-10 10]. A process for constructing a FIS can be summarized as follows: I. Choose a specific type of FIS (Mamdani or Sugeno) II. Select relevant input-output variables III. Determine the number of linguistic Terms associated with each input-output variable (determine the membership function for each linguistic term) IV. Design a collection of fuzzy if-then rules V. Choose thedefuzzification method. Fuzzification is an important concept in the FIS theory.Fuzzification is the process where the crisp quantities are converted to fuzzy. Thus Fuzzification process may involve assigning membership values for the given crisp quantities. This unit transforms the non-fuzzy (numeric) input variable measurements into the fuzzy set (linguistic) variable that is a clearly defined boundary, without a crisp (answer). In this simulation study, the error and error rate are defined by linguistic variables such commit to user

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as negative big (NB), negative medium (NM), negative small (NM), zero (Z), positive small (PS), positive medium (PM) and positive big (PB).

Figure 2.7 input1 (Error) membership function of FIS (Singh, et. al., 2013)

Figure 2.8 input2 (Delta-error) membership function of FIS (Singh, et. al., 2013)

Figure 2.9 output membership function of FIS (Singh, et. al., 2013) There are 49 rules for FIS. The output membership function for each rule is commit to user given by the Min (minimum) operator. The Max operator is used to get the combined

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FIS output from the set of outputs of Min operator.The output is produced by the fuzzy sets and fuzzy logic operations by evaluating all the rules.

Table 2 Fuzzy rules Error Delta-error NB NM NS Z PS PM PB

NB

NM

NS

Z

PS

PM

PB

NB NB NB NM NM NS Z

NB NB NM NM NS Z PS

NB NM NM NS Z PS PM

NM NM NS Z PS PM PM

NM NS Z PS PM PM PB

NS Z PS PM PM PB PB

Z PS PM PM PB PB PB

2.10 Genetic Algorithms (GA) Genetic Algorithms are reliable and robust methods for searching solution spaces. They are inspired by the biological theory of evolution through natural selection and much of the terminology is similar. (Milanovic, and Zhang, 2010).

2.11 Genetic Algorithm Basics A chromosome is an encoded string of possible values for the parameters to be optimized. These chromosomes can be made up of real-valued or binary strings. Often one of the main challenges in designing a genetic algorithm to find a solution to a problem is finding a suitable way to encode the parameters. A set of potential solutions, called a population, is created. Each member of this set is referred to as an individual and they are evaluated by decoding the parameter values from the chromosomes and applying them to the problem to see how well they perform the task at hand (the objective that is to be optimized). The score that an individual achieves at performing the required task is called its fitness. After the fitness of each individual has been calculated, a procedure known as selection is performed. Individuals are selected to contribute towards creating the commit tobeing userrelated to the individual’s fitness. next generation, the probability of selection

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Once selection has occurred, crossover takes place between pairs of selected individuals. The strings of two individuals are mixed. In this way, new individuals are created that contain characteristics that come from different here for relatively successful individuals. A third operation that occurs is mutated, the random changing of bits in the chromosome. It is generally performed with a relatively low probability. Mutation ensures that the probability of searching a given part of the solution space is never zero.There are many ways in which these different operations can be applied. Different algorithms can be used for each and they can also be applied with varying degrees of probability. Some of the more popular algorithms for each of these operations are now examined and their effects on the GA’s performance is investigated. GA as a powerful and widely applicable stochastic search and optimization techniques, starts with a population of randomly generated candidates and evolves towards a solution, is perhaps the most broadly known types of evolutionary computation method today. In 1960 the first serious investigation into Genetic Algorithms (GAs) was undertaken by John Holland. Genetic algorithms have become popular due to self-adaptive control systems, function optimization problems, computationally simple. The search method they use is robust since it is not limited like other search methods with regard to assumptions about the search space. The genetic algorithm is an algorithm which is based on natural evolution and the survival of the best chromosome. There are three basic differences between genetic algorithm and optimization classical methods. Firstly, the genetic algorithm works on the encoded strings that are the representative of one answer to the problem, and the real quantities of the parameters are obtained from the decoding of these strings. Secondly, it works with a population of search spaces. This quality causes the genetic algorithm to search different response spaces simultaneously reducing the possibility of being entrapped at local optimized points. Thirdly, the genetic algorithm does not need previous data from the problem response space such as convexity and derivable. It is only necessary to calculate a response function named commit to user

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fitness function. Binary encoding and real encoding are the two common methods that used in genetic algorithm.(Zanjani, et al,2007).

G(s)

(2.7)

Where is: Ki is constant integration Kp is constant proportional Fc is the viscous fiction coefficient P is the pole pairs Lr is the load of root G(s) = genetic system The following stages are carried out in the genetic algorithm controlling method 

The formation of initial population:A definite number of chromosomes are randomly selected with regard to the type of the problem.



Evaluation: Each chromosome from the initial population is processed on the basis of the initial goal of the problem.



Production of new population: At this stage a new population is selected on the basis of the previous one. The stages in the manufacture of this population are: 1. Transmit: The chromosomes with high efficiency are directly transmitted to the new population. 2. Selection: Two pairs of the remaining chromosomes from the previous population are selected according to their rate of efficiency. 3. Crossover: By selecting two chromosomes from the present population, it is trying to improve the efficiency rate of one of the produced chromosomes by employing the crossover method. 4. Mutation: it is trying to correct the gene in a chromosome, which has caused a reduction in the competence rate of the chromosome. With regard to the kind of the employed encoding the type of this function is determined.

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5. Reception: the obtained chromosome is placed in the new population, according to the previous states 

Replacement: The newly obtained population is replaced with the previous population and then our return to stage 2.



Stoppage:This factor determines the stoppage method of the algorithm, which can be based on the rate of the competence proximity of the chromosomes to each other in the new population or is determined on the basis of the number of the produced population in the algorithm (Zanjani, et. al.,2007).

Presently GA has been receiving a lot of attention and more research has been done to study its applications. Application in the area of Control Engineering has also developed tremendously. Even though in control system design, issues such as performance, system stability, static and dynamic index and system robustness have to be taken into account. However, each of these issues strongly depends on the controller structure and parameters. This dependence usually cannot be expressed in a mathematical formula, but often a trade-off has to be made between conflicting performance issues (Grefenstette, 1986).

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CHAPTER III RESEARCH METHODOLOGY

3.1 Simulink Model for D-STATCOM using PI The performance of the designed D-STATCOM, as shown in Figure 3.1 is evaluated using Matlab/Simulink. Table 3 shows the values system of parameters. DSTATCOM Test System comprises a 230 KV, 50 Hz generation system, represented by Thevenin equivalent, feeding two transmission lines through an 2-winding transformer connected in a Y/ ∆ 230/115 KV. Such transmission lines feed one distribution system through two transformers connected in Y/ ∆, 115/11 KV, PI Controller (proportional-integral controller) is a close loop controller which drives the plant to be controlled with a weighted sum of error and integral that value. PI Controller has the benefit of Steady-state error to be zero for a step input.

Figure 3.1 Simulink Model for D-STATCOM Table 3 System Parameters 1

Voltage Source

230Kv

2

Frequency

50Hz

3

Line impedance

0.001Ω, 0.005H

4 5

DC voltage commit to user Capacitor

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3.2 Simulink Model for D-STATCOM using FIS The system is composed by a 230 kW, 50 Hz generation system, represented by Thevenin equivalent, feeding two transmission lines through an 2-winding transformer connected in Y/ ∆ 230/115 KV. Such transmission lines feed one distribution system through two transformers connected in Y/ ∆, 115/11 KV. as shown in Figure 3.2

Figure 3.2 Simulink Model for D-STATCOM using FIS The MATLAB/SIMULINK with its Fuzzy Logic Toolbox software was used to control D-STATCOM which is used for the enhancement of power quality in distribution system based on FIS to alleviation voltage sag. FIS has two inputs and one output, the input consisting of 5 members and output fuzzy consists of 5 members. Where the input variables in the range [-5 5], while the output variable in the range [-10 10]. In this simulation study, the error and error rate are defined by linguistic variables such as negative big (NB), negative medium (NM), negative small (NM), zero (Z), positive small (PS), positive medium (PM) and positive big (PB). There are 49 rules for FIS. The output membership function for each rule is given by the Min (minimum) operator. The Max operator is used to get the combined FIS output from the set of outputs of Min operator.The output is produced by the fuzzy sets and fuzzy logic operations by evaluating all the commit to user rules.

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3.3 Genetic Algorithm (GA) To enhance the performance of the distribution system, D-STATCOM is to be connected to the distribution system. D-STATCOM is designed using MATLAB, Simulink version R2012b as shown in figure 3.1. The system is composed by a 230 kW, 50 Hz generation system, represented by Thevenin equivalent, feeding two transmission lines through an 2-winding transformer connected in a Y/ ∆ 230/115 KV. Such transmission lines feed one distribution system through two transformers connected in Y/ ∆, 115/11 KV,. as shown in Figure 3.3

Figure 3.3 Simulink Model for D-STATCOM using GA

The system was designed based on D-STATCOM as a controlled reactive source which includes a voltage source converter (VSC) and a DC link capacitor connected in shunt, capable of generating and/or absorbing reactive power. Indeed, it was used to compensate the with its Optimization Algorithms Toolbox software was used

to

control

D-STATCOM

power

system

disturbances.

The

MATLAB/SIMULINK with GA which is used for the enhancement of power quality to uservoltage sag. in distribution system based on GA commit to alleviation

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3.4 Flow Chart of the Research Methodology

Start

Literature Study

Do Simulation of D-STATCOM in MATLAB

Applications

D-STATCOM with PI

D-STATCOM with FIS

Determining Injection of Current & Time Respond Comparison

Analysis

END

Figure 3.4 Flow chart of the thesis. commit to user

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The Research was started following by theliterature review. Afterwards, the D-STATCOM in MATLAB was simulated. Subsequently, as a part of application one controlling method and two algorithms such as FIS and GA were applied. Therefore, injection of current was determined and timely respond was compared simultaneously. At last, the outcome of the simulation was analyzed by a significant ending.

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CHAPTER IV DATA AND ANALYSIS

This section was described about the simulation results of the proposed In a power system, switching of large inductive loads, causes a drop in the voltage at the load bus due to increase in VAR demand which lead to voltage instability. In order to keep the voltage within a limit, reactive power compensation device such as DSTATCOM are used FACTS devices help in increasing the operational efficiency of the power system without affecting the reliability of supply. The use of these devices depends on the type of application and the response time required for controlling the voltage profile of the load bus. D-STATCOM has been at the power center of attention and the subject of research for many years. D-ASTACOM can enhance the power transmission capability and thus extend the steady state stability limit. DSTATCOM can also introduce damping during power system transients. For reactive power compensation the D-STATCOM is a kind of custom power device which has the capability of reactive power compensation as well as balancing and harmonic elimination. 4.1 3-Dimensions Surface of FIS Rule base was designed based on the FIS algorithm. Positive error means the voltage array on the left side of the D-STATCOM, so it must increase the value of output voltage, while negative error means the voltage array on the right side of the D-STATCOM and it must be decreased. Figure 4.1 is a surface plot of Ali base. It shows that negative error (E) dan negative hange of error will result in negative change of output du). It is a case when the voltage array is on the right side of the DSTATCOM. While positive error (E) and positive change of error (DE), a case when voltage array is on the left side of the D-STATCOM, will result in positive change of output.

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Output of voltage

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Input of voltage

Figure 4.13-Dimensions Surface of FIS The figure 4.3below elucidates the simulation result in terms of PI Controller with D-STATCOM and without D-STATCOM. The X-axis and Y-axis have been represented by time (Sec) and voltage (p.u). Informed that D-STATCOM with PI has red color and a D-STATCOM without PI has black color.

Figure 4.2PI Controller withcommit D-STATCOMand without D-STATCOM to user

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From figure 4.3, it has been clarified the output voltage of the D-STATCOM at the first case the simulation was running without D- STATCOM its noted that in 0.5 to 0.8 Sec, voltage dropped to (24.00%) cause of voltage of consumer equipment and industrial equipment, in the next case was tested by using the D-STATCOM with PI controller which has better performance and improved dramatically when the PI injected into the system the voltage switched to (80.00%) voltage. Therefore, this research greatly supports for applying D- STATCOM approach for the mitigation of voltage sag. The figure 4.4 below illustrates the single fault scenario result of absence of D-STATCOM method and comparison among three other methods, namelythe PI controller, which has red color, FIS was indicted by the blue color, and GAwere indicated by green color. Here, The X-axis and y-axis are represented by time (Sec) and voltage (p.u) respectively. 1.3 1.2

GA FIS

PI

98.50%

Without D-STATCOM

98.05% 24.00%

1.1 1 0.9

Voltage p.u

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time (Sec)

Figure 4.3 Single phase fault scenario Result of comparison uses the PI Controller, commit to user FIS andGA.

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From above figure presented the output voltage of single phase The simulation result was started from 0 Sec to 1 Sec andtested with three methods to solve the voltage sag at 0.5 to 0.8 which was given 24% without D-STATCOM, and D_STATCOM with PI when injected into the system was 98.05%. In the two other methods FIS and GA have been done by the same simulationwith PI, it's noted that the D-STATCOM with FIS was raise the voltage sag to 98.15% which has better performance than PI controller. Moreover, by using the D-STATCOM with GA method found that the performance better than PI and FIS methods. The best execution was shown by GA methodthe voltage sag dramatically improvedto 98.50 %. Surprisingly, Without D-STATCOM, recovery of voltage per unit was found only(24.00%). Thus, it can be recommended that D-STATCOM applying the GA method is a very promising approach in the context of single phase fault scenario outcomes. The figure 4.4 below illustrates the single fault scenario result of absence of D-STATCOM method and comparison among three other methods, namelythe PI controller, which has red color, FIS was indicted by the blue color, and GAwere indicated by green color. Here, The X-axis and y-axis are represented by time (Sec) and voltage (p.u) respectively. 1.3 1.2

GA

98,40%

FIS

92.15%

PI

85.80%

1.1 1 0.9

Voltage p.u

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (Sec)

Figure: 4.4Two phase fault scenario Resulttoofuser comparison use the PI Controller, FIS commit and GA.

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From above figure presented the output voltage of two phases The simulation result was started from 0 sec to 1 sec and tested with three methods to solve the voltage sag

at 0.5 to 0.8 which was given 24.00% without D-STATCOM, and

D_STATCOM with PI when injected to the system was 85.80%. In the two other methods FIS and GA have been done by the same simulation with PI, it's noted that the D-STATCOM with FIS was raise the voltage sag to 92.15% which has better performance than PI controller. Moreover, by using the D-STATCOM with GA method found that the performance better than PI and FIS methods. The best execution was shown by GA method the voltage sag dramatically improved to 98.40%. The figure 4.6 below explains the three phase fault scenario result of comparison among three other methods, namely the PI controller, FIS, and GA. Here, The X-axis and y-axis are represented by time (sec) and voltage (p.u) respectively. 1.3 1.2

GA

94.15%

FIS

90.75%

PI

80.00%

1.1 1 0.9

Voltage p.u

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time (Sec)

Figure 4.5Three phase fault scenario result of comparison uses the PI Controller, FIS and GA. commit to user

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In figure 4.6 presented the output voltage of three phases The simulation result was started from 0 sec to 1 sec and tested with three methods to solve the voltage sag at 0.5 to 0.8 which was given 24.00% without D-STATCOM, and D_STATCOM with PI when injected to the system was 80.00%. In the two other methods FIS and GA have been done by the same simulation with PI, it's noted that the D-STATCOM with FIS was raise the voltage sag to 90.75% which has better performance than PI controller. Moreover, by using the D-STATCOM with GA method found that the performance better than PI and FIS methods. The best execution was shown by GA method the voltage sag dramatically improvedto 94.15%.In a nutshell, it can be attained that in case of all phases such as single, two and three phase scenario results, GA method linked with D-STATCOM has been incarnated as a best method among the applied three methods. As a result, GA method integrated with D-STATCOM can be supported using this approach widely for the mitigation of voltage sag problem.

4.2 Comparison of in D-STATCOM with PI, FIS and GA The results in Table 4 showed that Genetic Algorithms were capable to overcome the voltage by 98.50%, 98.40%, and 94.15% single-phase, two-phase, and three-phase within the time extent of 0.5-0.8 Sec, respectively. On the other hand, Fuzzy Inference System was competent to outplay the voltage by 98.15%, 92.15%, and 90.75% in single-phase, two-phase, and three-phase and proportional Integrative werepotent to outfight the voltage by 98.05%, 85.80%, and 80.00% in single-phase, two-phase, and three-phase within the time extent of 0.5-0.8 Sec,respectively.The comparison between D-STATCOM with PI, D-STATCOM with FIS and DSTATCOM with GA. Table 4 Comparison of D-STATCOM with PI, FIS and GA Model

Single phase

Two phases

Three phases

D-STATCOM with PI

98.05%

85.80%

80.00%

D-STATCOM with FIS

98.15%

92.15%

90.75%

D-STATCOM with GA

98.50% commit to user 98.40%

94.15%

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In brief, it can be explained about the GA method that in a genetic algorithm, a population of candidate solutions (called individuals, creatures or phenotypes) to an optimization problem is evolving toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other coding are also possible. Besides, the evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population ineach iteration called a generation. In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population, and each individual's genome is modified (recombined and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations have been produced, or a satisfactory fitness level has been reached for the population. In this study chromosome is voltage as it is earlier mentioned that chromosome is a set of properties which can be mutated or altered due to system fault. From the Table 4, it is obvious that the performance of the GA is the highest among the three methods, because it can bring the voltage to 98.50 p.u. of the rated voltage. Consequently, It can be said that although the time respond is higher in GA method than other methods such as FIS, PI, however, due to the optimization of current or voltage

perfecting the GA method is superior than other methods.

Moreover, Genetic algorithm work on the chromosome (voltage), which is encoded version of potential solutions’ parameters, rather the parameters themselves. Even in terms of fuzzy logic system outside the set and rule based system, it does not control the problem, whereas in case of GA no need to set and rule based system.

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CHAPTER V CLOSING

5.1 Conclusion To sum up., a promising device (D-STATCOM) using three methods such as Genetic Algorithm (GA), Fuzzy Inference Systems (FIS), and Proportional Integral (PI) was designed this research to compensate the voltage sag. The results showed that D-STATCOM with GAwas 98.50%, 98.40%, and 94.15% in single-phase, twophase, and three-phase within the range of 0.5-0.8 second, respectively. Similarly, DSTATCOM with FIS was competent to outplay the voltage around 98.15%, 92.15%, and 90.75% of single-phase, two-phase, and three-phase and the potential of DSTATCOM with PI to compensate the voltage was 98.05%, 85.80%, and 80.00% in single-phase, two-phase, and three-phase within the same range like others 0.5-0.8 second, respectively. Subsequently, the findings revealed that GA was the best among the three controlling approaches. It is being obtained as GA is one of the easiest methods and it does not need a set and rules based system as like as FIS System.

5.2

Suggestion This research was dealt with only the controller portion of D-STATCOM

device. Consequently, it can be suggested that for further improvement of this device as well as increasing usability of all three method, namely PI, FIS and GA to solve the voltage sag problem along with other problems as for instance voltage swell, flicker, other parts of D-STATCOM device such as modification of converter system can be practiced for future time.

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REFERENCES

Davis, T. Beam, E, G. and Melhorn, J, C. 1998. Voltage sagstheir impact on the utility and industrial customers.IEEE Trans. Ind. Applicat., vol.34, pp. 549– 558. Deshmukh, S. and Dewani, B. 2012. Overview of Dynamic Voltage Restorer (DVR) for Power Quality Improvement. International Journal of Engineering Research and Applications,Vol. 2, pp.1372-1377. Golkar, M. Rani, P. and Sivakumar.R 2008. Power Quality in Electric Networks Monitoring, and Standards. International Journal of Innovative Research in Science, Engineering and Technology, vol.3, pp. 761–766. Grefenstette, J, J. 1986. Optimization of control parameters for genetic algorithms. SystemsMan and Cybernetics IEEE Transactions, vol. 16, pp.122–128. Hingorani, N. G. 1991. Facts - exible ac transmission systems, InternationalConference on AC and DC Power Transmission, pp. 1-7. Hussain, K. and Praveen, J. 2012. Voltage Sag Mitigation Using Distribution Static Compensator System. International Journal of Engineering and Technology, Vol. 2, pp.756-760. Kadam,A. Dhamdhere, S. and Bankar, D. 2012. Application of D-STATCOM for Improvement of Power Quality using MATLAB Simulation. International Journal of Science and Modern Engineering, Vol. 1, pp.9-13. Khalid, S. and Dwivedi, B. 2010. Power Quality An Important Aspect. International Journal of Engineering Science and Technology, vol. 2, pp. 6485-6490 Milanovic, V, D. and Zhang, Y. 2010. Global Minimization of Financial Losses Due to Voltage Sags With FACTS Based Devices, IEEE Transactions on Power Delivery, Vol. 25, pp. 298-306. Patil, D.and Madhale, K. 2007. Design And Simulation Studies of D-Statcom For Voltage Sag, Swell Mitigation, IRNet Transactions on Electrical and Electronics Engineering, pp.97-103. Prasad, T. Kumar,S. Prasanth, V,B. and Sankar, S, K. 2013. Fuzzy Logic Control of D-Statcom for Power Quality Improvement. Journal of Engineering Research and Applications, Vol. 3, pp.398-403. Rambabu, E. Praveena, E. and Kishore, P. 2011. Mitigation of Harmonics in Distribution System Using D-STATCOM, commit to userInternational Journal of Scientific & Engineering Research Vol. 2,pp.1-5.

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Ramesh, P. Ramesh,G. and Devi,P. 2013. Identification and Elimination of Faults Occurrence in subsystems by using Resistance Switching for Linear Loads through Distribution Statcom (D-STATCOM). International Journal of Advanced Science and Technology Vol. 57,pp.1-8. Ramnath,B. Elanchezhian,C. and Kesavan, R. 2010. Suitability Assessment of Lean Kitting Assembly through Fuzzy Based Simulation Model, International Journal of Computer Applications, Vol. 4, pp.25-31. Ravi Kumar, V, S. and Nagaraju, S. 2007. Simulation of D-STATCOM and DVR in Power Systems, Asian Research Publishing Network, Journal of Engineering and Applied Sciences, Vol. 2, pp.7-13. Reddy, K. and Laxmi, A. 2012. Implementation of Custom Power Product Dstatcom In Power Sector, International Journal of Advanced Research in Engineering and Applied Sciences, Vol. 1, pp.43-55. Singh, A. and Surjan, S, B. 2013. Power Quality Improvement Using FACTS Devices: A Review. International Journal of Engineering and Advanced Technology, vol. 3, pp. 383–390. Singh, A. Arora, P. and Singh, B. 2013. Voltage SAG Mitigation by Fuzzy Controlled DVR, International Journal of Advanced Electrical and Electronics Engineering Vol. 2, pp. 93-100. Sumalatha, V. Ramani, K. and Lakshmi, K. 2011. Fuzzy Inference System to Control PC Power Failures. International Journal of Computer Applications, Vol. 28, pp.10-17. Sumpavakup, C. and Kulworawanichpong, T.2008. Distribution Voltage Regulation Under Three Phase Fault by Using D-STATCOM, International Journal of Electrical and Electronics Engineering. Zanjani, A, M.Shahgholian,Gh.Poodeh, E, M. and Eshtehardiha, S. 2007. Adaptive Integral-Proportional Controller in Static Synchronous Compensator Based on Genetic Algorithm.7th WSEAS International Conference on Electric Power Systems, High Voltages, Electric Machines, pp.40-45.

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Curriculum Vitae

Name

HAMZA JABER MOHAMMED

Place/Date of birth

ALGATRON – 09/ 07 / 1987

Gender

Male

Marrital Status

Single

Religion

Islam

Citizenship

Libyan

Address

Libya -ALGATRON

E-mail Phone

[email protected] 085712662555

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Design and Simulation Studies of D-STATCOM for Mitigating Voltage

perpustakaan.uns.ac.id digilib.uns.ac.id Design and Simulation Studies of D-STATCOM for Mitigating Voltage Sag Problem by Using Genetic Algorithm, F...

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