perpustakaan.uns.ac.id digilib.uns.ac.id Design and

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

2014

perpustakaan.uns.ac.id digilib.uns.ac.id

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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


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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 three-

phase 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 ................................................................................................... iii

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

ABSTRACT ........................................................................................................ v

PREFACE ............................................................................................................. vi

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

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

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

CHAPTER I INTRODUCTION .................................................................... 1

1.1 Background ..................................................................... 1

1.2 Problems statements ........................................................ 2

1.3 Objective ......................................................................... 2

1.4 Benefit ............................................................................. 2

1.5 Research Limitation ........................................................ 3

1.6 Contribution .................................................................... 3

CHAPTHER II THEORY .................................................................................. 4

2.1 Defined of Power Quality ............................................... 4

2.2 Power Quality Standards ................................................. 4

2.3 Power Quality Problems Nature and Solutions ............... 4

2.4 Voltage sag ...................................................................... 6

2.5 Distribution Static Compensator (D-STATCOM) .......... 7

2.6 Operating Principle of the D-STATCOM ....................... 7

2.7 Proportional Integrative (PI) CONTROLLER ................ 10

2.8 Fuzzy Inference System (FIS) ........................................ 11

2.8.1 Fuzzification .......................................................... 12

2.8.2 Rule Evaluation ...................................................... 12

2.8.3 Inference Engine .................................................... 12


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2.8.4 Defuzzification ....................................................... 12

2.8.5 Output .................................................................... 13

2.9 Operation of FIS .............................................................. 13

2.10 Genetic Algorithm (GA) ................................................. 16

2.11 Genetic Algorithm Basics ............................................... 16

CHAPTER III RESEARCH METHODOLOGY ............................................. 20

3.1 Simulink Model for D-STATCOM using PI ................... 20

3.2 Simulink Model for D-STATCOM using FIS ................ 21

3.3 Genetic Algorithm (GA) ................................................. 22

3.2 Flow Chart of the Research Methodology ...................... 23

CHAPTER IV RESULT AND ANALYSIS ..................................................... 25

4.1. 3-Dimensions Surface of FIS .......................................... 25

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

CHAPTER V CONCLUSION AND SUGGESTION ..................................... 32

5.1 Conclusion ....................................................................... 32

5.2 Suggestion ....................................................................... 32

REFERENCE ....................................................................................................... 33


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TABLE LIST

Table 1 A Summary of Power Quality Problems Nature and Solutions ......... 4

Table 2 Fuzzy Rules ....................................................................................... 16

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 .................................................................................................. 6

Figure 2.2 Schematic Representation of D-STATCOM........................................ 7

Figure 2.3 Basic Diagram of The DSTATCOM ................................................... 8

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 D-

STATCOM, 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

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 D-

STATCOM, 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

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

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 Characteristic Solution

Voltage Sags 0.1 — 0.9 Decrease in Per

unit RMS Value at 0.5

cycles to 1 mm.

DSTATCOMs, UPS,

Ferroresonant

Transformers, and Backup

Generators.

Voltage Swells 1.1 — 1.8 Increase in Per

unit RMS Value at 0.5

cycles to 1 mm.

DSTATCOMs, UPS,

Ferroresonant Transformers,

and Backup Generators.

Impulsive Transients Sudden change in voltage

and current signals in

steady state.

Filters, Isolation

Transformers, and Surge

Arresters.

Oscillatory Transients Sudden change in voltage

and current signals at (< 5

to > 500 kHlz).

Filters, Isolation

Transformers, and Surge

Arresters.


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Over/Undervoltages > 110% Increase and

<90% Decrease in RMS

voltage for > 1 mm.

Voltage Regulators and

Ferroresonant

Transformers.

Harmonic Distortion Refer to IEEE Std 519 —

1992 for allowed THD

(±5%) and TDD (± 10%).

DSTATCOM as an Active

Filter, Passive Filters and

Ferroresonant

Transformers.

Voltage Flicker Variation in Magnitude

with frequency.

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 (T2-

T1) and its magnitude is Vsag (Davis, et. al., 1998)


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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. D-

STATCOM 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 of a controllable Ac voltage source by a


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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


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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)

Where

Is = (VTh –VL)/ZTh (2.2)

Iout= IL-IS = IL- (VTh –VL)/ZTh (2.3)

The complex power injection expressed by the D-STATCOM is given by

Sout=VLI out (2.4)

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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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 low-

power 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


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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


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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.

Fuzzy

Rule Base

Inference

Engine

Fuzzifier

Defuzzifier Input Output


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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 (2.5)

Error rate = error (n) – error (n-1) (2.6)

Where is:

Vref is voltage References.

VS is voltage Source.

Error is Error supply.

Error rate is Error rate supply.


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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


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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

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 NB NB NB NM NM NS Z

NM NB NB NM NM NS Z PS

NS NB NM NM NS Z PS PM

Z NM NM NS Z PS PM PM

PS NM NS Z PS PM PM PB

PM NS Z PS PM PM PB 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

next generation, the probability of selection being related to the individual’s fitness.


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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


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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. D-

STATCOM 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 DC voltage 5Kv

5 Capacitor 750µF


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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

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

in distribution system based on GA to alleviation voltage sag.


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3.4 Flow Chart of the Research Methodology

Figure 3.4 Flow chart of the thesis.

Determining Injection of Current & Time

Respond Comparison

Literature Study

Analysis

Applications

Do Simulation of

D-STATCOM in

MATLAB

D-STATCOM with

PI

D-STATCOM with

FIS

D-STATCOM with

GA

END

Start


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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 D-

STATCOM 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. D-

STATCOM 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 D-

STATCOM. 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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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 with D-STATCOMand without D-STATCOM

Input of voltage

Ou

tpu

t o

f vo

ltag

e


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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.

Figure 4.3 Single phase fault scenario Result of comparison uses the PI Controller,

FIS andGA.

1.1

1

0.9

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

Vo

ltage p

.u

Time (Sec)

GA 98.50%FIS

PI 98.05%Without D-STATCOM 24.00%

1.2

1.3


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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.

Figure: 4.4Two phase fault scenario Result of comparison use the PI Controller, FIS

and GA.

Time (Sec)

1.1

1

0.9

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

GA 98,40% FIS 92.15% PI 85.80%

Volta

ge p

.u

1.2

1.3


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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




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.

Figure 4.5Three phase fault scenario result of comparison uses the PI Controller, FIS

and GA.

1.1

1

0.9

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

GA 94.15% FIS 90.75% PI 80.00%

Vo

ltage

p.u

Time (Sec)

1.2

1.3


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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




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 D-

STATCOM 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% 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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http://en.wikipedia.org/wiki/Iteration

http://en.wikipedia.org/wiki/Fitness_(biology)

http://en.wikipedia.org/wiki/Objective_function

http://en.wikipedia.org/wiki/Stochastics

http://en.wikipedia.org/wiki/Crossover_(genetic_algorithm)

http://en.wikipedia.org/wiki/Algorithm

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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, two-

phase, and three-phase within the range of 0.5-0.8 second, respectively. Similarly, D-

STATCOM 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 D-

STATCOM 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

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Integral-Proportional Controller in Static Synchronous Compensator Based

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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 [email protected]

Phone 085712662555


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