VOLTAGE STABILIZER OF GENERATOR OUTPUT

THROUGH FIELD CURRENT CONTROL USING FUZZY LOGIC

CLARA VALDEZ

A project report submitted in partial

Fulfillment of the requirement for the award of the

Degree of Master of Electrical & Electronic Engineering

Faculty of Electrical & Electronic Engineering

Universiti Tun Hussien Onn Malaysia

JANUARI 2013

iv

ABSTRACT

This project is about to design and implementation of a fuzzy logic controller for

regulating the output voltage of a generator through its field current. An automated

fuzzy logic-based control strategy has been designed for controlling the generator

voltage by varying the field current values. The fuzzy logic controller was

controlling the difference between the immediate output voltage and the rate voltage

of the generator as error variable. The controller make an intelligent decision on the

amount of field current that should be applied to the generator in order to keep the

output voltage at its rated value. This control algorithm was implemented by using

Mamdani method. This project is implemented using Simulink Matlab. Based on the

test results show the output voltage is in compliance with the voltage regulator

v

ABSTRAK

Projek ini adalah merekabentuk dan mengaplikasikan Pengawal Fuzzy Logik untuk

mengawal voltan keluaran penjana melalui pengawalan arus dalam litar. Pengawal

Fuzzy Logik secara automatik bertindak untuk mengawal voltan penjana walaupun

dibekalkan dengan jumlah arus yang sentiasa berubah. Pengawal Fuzzy Logik

mengawal perbezaan antara voltan output dan kadar voltan penjana dengan serta-

merta yang mana diandaikan sebagai pembolehubah kesilapan.Pengawal membuat

keputusan yang bijak mengenai jumlah arus medan yang harus digunakan untuk

penjana. Ini adalah untuk memastikan voltan keluaran adalah seperti voltan rujukan

yang dikehendaki. Algoritma kawalan telah dilaksanakan dengan menggunakan

kaedah Mamdani. Projek ini dilaksanakan dengan menggunakan Matlab Simulink.

Berdasarkan keputusan ujian menunjukkan voltan keluaran adalah memenuhi

kehendak pengatur voltan.

vi

TABLE OF CONTENTS

THESIS STATUS APPROVAL

EXAMINERS’ DECLARATION

TITLE

DECLARATION ii

ACKNOWLEDGEMENT iii

ABSTRACT iv

ABSTRAK v

CONTENTS vi

LIST OF TABLES ix

LIST OF FIGURES x

LIST OF SYMBOLS AND ABBREVIATIONS xii

CHAPTER 1 INTRODUCTION 1

1.1 Project Background 1

1.2 Problem Statements 2

1.3 Project Objectives 2

1.4 Project Scopes 3

vii

CHAPTER 2 LITERATURE REVIEW 4

2.0 Introduction 4

2.1 Fuzzy Logic 7

2.1.1 A Comparison of Classical Set and Fuzzy Set 8

2.2 Practical relevance of fuzzy modelling 11

2.2.1 Fuzzy Sets with a Continuous Universe 13

2.3 Fuzzy Set-Theoretic Operations 14

2.3.1 Containment or Subset 15

2.3.2 Union (Disjunction) 15

2.3.3 Intersection (Conjunction) 16

2.3.4 Complement (Negation) 17

2.4 Rule-Based Fuzzy Models 18

2.5 Electric Generator 18

2.5.1 Principles of Electric Generator 20

2.6 Voltage Regulator 22

CHAPTER 3 PROJECT DEVELOPMENT 25

3.1 Formulating Membership Functions 25

3.2 Membership Functions in Fuzzy Logic Toolbox Software 27

3.3 Fuzzy Logic Controller 33

3.3.1 Application Areas of Fuzzy Logic Controllers 33

3.4 Components of FLC 34

3.4.1 Fuzzification Block or Fuzzifier 34

3.4.2 Inference System 35

viii

3.4.3 Defuzzification Block or Defuzzifier 36

CHAPTER 4 METHODOLOGY 39

4.1 Fuzzy Logic Controller Design 39

4.2 Membership Function Design 40

4.3 Output Linguistic Variable 43

4.3.1 Rule Base Design for the Output 44

4.4 Variable Of Fuzzy Logic Control 45

4.5 Design of the Fuzzy Logic Controller using MATLAB 47

4.6 MATLAB Simulation 60

CHAPTER 5 RESULT, ANALYSIS AND DISCUSSION 62

5.1 Simulation Results 62

5.2 Analysis 64

5.3 Discussion 68

CHAPTER 6 CONCLUSION, DISCUSSION AND RECOMMENDATION 70

6.1 Conclusion 70

6.2 Recommendation 71

REFERENCES 72

ix

LIST OF TABLES

Table2.1: Different modelling paradigms. 7

Table 4.1: Fuzzy sets and the respective membership functions for voltage (v) 41

Table 4.2: Fuzzy sets and the respective membership functions for Change

in voltage (∆v) or derivative rate of change of voltage. 42

Table 4.3: Fuzzy sets and the respective membership functions for

Output voltage (ωsl) 43

Table 4.4: Fuzzy Rule Table for Output (ωsl ) 44

x

LIST OF FIGURES

Figure 2.1: Example of Classical Set and Fuzzy set 8

Figure 2.2: Evaluation of a crisp, interval and fuzzy function for crisp, interval and

fuzzy arguments 10

Figure 2.3: Membership Function on a Continuous Universe 13

Figure 2.4: Operations on Fuzzy sets - The concept of containment or subset 15

Figure 2.5: Operations on Fuzzy sets - Two Fuzzy sets A and B 16

Figure 2.6: Operations on Fuzzy sets - A B 17

Figure 2.7: Operations on Fuzzy sets - Fuzzy set A and its Complement Ā 17

Figure 2.8: Electric Generator 19

Figure 2.9: Two Types of Rotor Construction 22

Figure 3.1: Examples of four classes of parameterized MFs 26

Figure 3.2: Triangular & Trapezoidal Membership functions 29

Figure 3.3: Gaussian distribution curve 30

Figure 3.4: Sigmoidal MFs 31

Figure 3.5: Z, S, and Pi MFs 32

Figure 3.6: Fuzzy Logic Controller Structure 35

xi

Figure 3.7: Various Defuzzification schemes for obtaining crisp outputs 38

Figure 4.1: Block Diagram of Variables of the Fuzzy Logic Controller 45

Figure 4.2: Flow Chart for the Fuzzy Logic-Based Generator controller 46

Figure 4.3: FIS editor window in MATLAB 55

Figure 4.4: FIS editor: rules window in MATLAB 56

Figure 4.5: Membership function for the input Voltage (v) 57

Figure 4.6: Membership function for the input Change in Voltage (∆v) 58

Figure 4.7: Membership function for the output Change of voltage 59

Figure 4.8: Voltage Stabilizer of Generator using Fuzzy Logic Model

in SIMULINK 60

Figure 5.1: Voltage output (regulated) 63

Figure 5.2: Voltage output with voltage reference 64

Figure 5.3: Three dimensional plot of the control surface 65

Figure 5.4: Rule viewer for Output viewer with inputs v = 0 and ∆v = 0 66

Figure 5.5: Rule viewer for Output viewer with inputs v = 0.5 and ∆v = 0.5 67

xii

LIST OF SYMBOLS AND ABBREVIATIONS

element of

Mu

union

intersection

increment

greater than

alpha

beta

COA Centroid of Area

BOA Bisector of Area

MOM Mean of Maximum

SOM Smallest of Minimum

LOM Largest of Maximum

DC direct current

xiii

AC alternating current

TS Takagi–Sugeno

MFs membership functions

FLC Fuzzy Logic Control

Vref voltage reference

PL Positive Large

PM Positive Medium

PS Positive Small

NL Negative Large

NM Negative Medium

NS Negative Small

ZE Zero

1

CHAPTER 1

INTRODUCTION

1.1 Project Background

It is impossible to obtain optimal operating conditions through a fixed

excitation controller due to the dynamic un-stability of power systems. Power system

is a typical large dynamic system and its dynamic behavior has great influence on the

voltage stability. In order to get more realistic results, it is necessary to take the full

dynamic system model into account. One mechanism to obtain the power system

dynamic voltage stability is by minimizing oscillations of the state and network

variables [1]. Thus, a model of an automated fuzzy logic-based control strategy is

going to be used in this project. The Fuzzy Logic Control is using for controlling the

generators voltage.

2

1.2 Problem Statements

A number of research studies on the design of the excitation controller of

synchronous generator have been successfully carried out in order to improve the

damping characteristics of a power system over a wide range of operating points and

to enhance the dynamic stability of power systems. When the load is changing, the

operation point of a power system will be varies. When there is a large disturbance,

there are considerable changes in the operating conditions of the power system.

Therefore, it is impossible to obtain optimal operating conditions through a fixed

excitation controller. To overcome this problem in this project is designed a voltage

stabilizer of generator output through its field current control by applying a Fuzzy

Logic Controller.

1.3 Project Objectives

The major objectives of this research are:

a) To derive the generator model based on field current control.

b) To develop fuzzy logic control for a regulated the generator field current.

c) To apply fuzzy logic control for controlling the field current of generator.

d) To test the performances of the controller develop.

3

1.4 Project Scopes

The scopes of this project are:

a) Type of generator going to studied is an AC Generator.

b) The model is based on field current model.

c) The controller apply is Fuzzy Logic

d) The controller algorithm is implemented using ‘Mamdani’ Fuzzy Inference.

4

CHAPTER 2

LITERATURE REVIEW

2.0 Introduction

Developing mathematical models of real systems is a central topic in many

disciplines of engineering and science. Models can be used for simulations, analysis

of the system’s behaviour, better understanding of the underlying mechanisms in the

system, design of new processes, or design of controllers.

Traditionally, modelling is seen as a conjunction of a thorough understanding

of the system’s nature and behaviour, and of a suitable mathematical treatment that

leads to a usable model.

This approach is usually termed “white-box” (physical, mechanistic, first-

principle) modelling. However, the requirement for a good understanding of the

physical background of the problem at hand proves to be a severe limiting factor in

practice, when complex and poorly understood systems are considered.

5

Difficulties encountered in conventional “white-box” modeling can arise, for

instance, from poor understanding of the underlying phenomena, inaccurate values of

various process parameters, or from the complexity of the resulting model. A complete

understanding of the underlying mechanisms is virtually impossible for a majority of real

systems. However, gathering an acceptable degree of knowledge needed for physical

modeling may be a very difficult, time-consuming and expensive or even impossible task.

Even if the structure of the model is determined, a major problem of obtaining accurate

values for the parameters remains. It is the task of system identification to estimate the

parameters from data measured on the system. Identification methods are currently

developed to a mature level for linear systems only. Most real processes are, however,

nonlinear and can be approximated by linear models only locally.

A different approach assumes that the process under study can be

approximated by using some sufficiently general “black-box” structure used as a

general function approximated. The modelling problem then reduces to postulating

an appropriate structure of the approximated, in order to correctly capture the

dynamics and nonlinearity of the system. In black-box modelling, the structure of the

model is hardly related to the structure of the real system.

The identification problem consists of estimating the parameters of the

model. If representative process data are available, black-box models usually can be

developed quite easily, without requiring process-specific knowledge. A severe

drawback of this approach is that the structure and parameters of these models

usually do not have any physical significance. Such models cannot be used for

analyzing the system’s behavior otherwise than by numerical simulation, cannot be

scaled up or down when moving from one process scale to another, and therefore are

less useful for industrial practice. There is a range of modeling techniques that

attempt to combine the advantages of the white-box and black-box approaches, such

6

that the known parts of the system are modeled using physical knowledge, and the

unknown or less certain parts are approximated in a black-box manner, using process

data and black-box modeling structures with suitable approximation properties.

These methods are often denoted as hybrid, semi-mechanistic or “gray-box”

modeling.

A common drawback of most standard modeling approaches is that they

cannot make effective use of extra information, such as the knowledge and

experience of engineers and operators, which is often imprecise and qualitative in its

nature. The fact that humans are often able to manage complex tasks under

significant uncertainty has stimulated the search for alternative modelling and control

paradigms. So-called “intelligent” modelling and control methodologies, which

employ techniques motivated by biological systems and human intelligence to

develop models and controllers for dynamic systems, have been introduced. These

techniques explore alternative representation schemes, using, for instance, natural

language, rules, semantic networks or qualitative models, and possess formal

methods to incorporate extra relevant information. Fuzzy modelling and control are

typical examples of techniques that make use of human knowledge and deductive

processes. Artificial neural networks, on the other hand, realize learning and

adaptation capabilities by imitating the functioning of biological neural systems on a

simplified level. The different modeling paradigms are summarized in Table 1.

7

Modeling Source of Method of

approach information acquisition

Mechanistic Formal knowleadge Mathematical differential cannot use 'soft'

(white-box) and data (Lagrange eq.) equations knowledge

optimization regression, cannot at all

(learning) neural network use knowledge

various knowleadge knowledge- rule based curse' of

and data based + learning model dimensionality

Example Deficiency

black-box

fuzzy

data

Table 2.1: Different modelling paradigms.

2.1. Fuzzy Logic

Fuzzy Logic was initiated in 1965 [2] by Lotfi A. Zadeh, professor for computer

science at the University of California in Berkeley. Basically, Fuzzy Logic (FL) is a

multi-valued logic that allows intermediate values to be defined between

conventional evaluations like true/false, yes/no, high/low and more. Notions like

rather tall or very fast can be formulated mathematically and processed by

computers, in order to apply a more human-like way of thinking in the programming

of computers [3].

Fuzzy system is an alternative to traditional notions of set membership and

logic that has its origins in ancient Greek philosophy. Fuzzy Logic has emerged as a

profitable tool for the controlling and steering of systems and complex industrial

processes, as well as for household and entertainment electronics, as well as for other

expert systems and applications.

8

2.1.1 A Comparison of Classical Set and Fuzzy Set

Let X be a space of objects (called universe of discourse or universal set) and

x be a generic element of X.

A classical set A (A is a subset of X) is defined as a collection of elements or

objects x X, such that each x can either belong or not belong to the set A. By

defining a characteristic function for each element in X, the classical set A by a set of

ordered pairs (x,0 ) or (x,1 ) can represented which indicates x or x A,

respectively.

Figure 2.1: Example of Classical Set and Fuzzy set

In spite of being an important tool for the engineering sciences, classical sets

fail to replicate the nature of human conceptions, which tend to be abstract and

vague. A fuzzy set [4] conveys the degree to which an element belongs to a set. In

other words, if X is a collection of objects denoted generically by x, then a fuzzy set

A in X is defined as a set of ordered pairs as below:

} (2.1)

9

Where µA(x) is known as the membership function for the fuzzy set A. MF

serves the purpose of mapping each element of X to a membership grade (or

membership value) between 0 and 1.Clearly, if the value of µA(x) is restricted to

either 0 or 1, then A is reduced to a classical set and µA(x) is the characteristic

function of A.

Fuzzy systems can be regarded as a generalization of interval-valued systems,

which are in turn a generalization of crisp systems. This is depicted in Figure2.2

which gives an example of a function and its interval and fuzzy forms. The

evaluation of the function for crisp, interval and fuzzy data is schematically depicted

as well. Note that a function can be regarded as a subset of the Cartesian

product X x Y, as a relation. The evaluation of the function for a given input proceeds

in three steps:

i. Extend the given input into the product space Y (vertical dashed lines in

Figure 2. 2),

ii. Find the intersection of this extension with the relation,

iii. Project this intersection onto Y (horizontal dashed lines in Figure 2.2).

The evaluation includes both function and the data (crisp, interval, fuzzy).

Remember this view of function evaluation, as will help to understand the use of

fuzzy relations for inference in fuzzy modelling).

10

Figure 2.2: Evaluation of a crisp, interval and fuzzy function for crisp, interval and

fuzzy arguments

Most common are fuzzy systems defined by means of if-then rules: rule-

based fuzzy systems. Fuzzy systems can serve different purposes, such as modelling,

data analysis, prediction or control.

11

2.2 Practical relevance of fuzzy modelling

1. Incomplete or vague knowledge about systems

Conventional system theory relies on crisp mathematical models of systems,

such as algebraic and differential or difference equations. For some systems, such as

electro-mechanical systems, mathematical models can be obtained. This is because

the physical laws governing the systems are well understood. For a large number of

practical problems, however, the gathering of an acceptable degree of knowledge

needed for physical modelling is a difficult, time-consuming and expensive or even

impossible task.

In the majority of systems, the underlying phenomena are understood only

partially and crisp mathematical models cannot be derived or are too complex to be

useful. Examples of such systems can be found in the chemical or food industries,

biotechnology, ecology, finance, sociology, etc. A significant portion of information

about these systems is available as the knowledge of human experts, process

operators and designers. This knowledge may be too vague and uncertain to be

expressed by mathematical functions. It is, however, often possible to describe the

functioning of systems by means of natural language, in the form of if-then rules.

Fuzzy rule-based systems can be used as knowledge-based models constructed by

using knowledge of experts in the given field of interest [5]. From this point of view,

fuzzy systems are similar to expert systems studied extensively in the “symbolic”

artificial intelligence [6].

12

2. Adequate processing of imprecise information.

Precise numerical computation with conventional mathematical models only

makes sense when the parameters and input data are accurately known. As this is

often not the case, a modelling framework is needed which can adequately process

not only the given data, but also the associated uncertainty. The stochastic approach

is a traditional way of dealing with uncertainty. However, it has been recognized that

not all types of uncertainty can be dealt with within the stochastic framework.

Various alternative approaches have been proposed [7], fuzzy logic and set theory

being one of them.

3. Transparent (gray-box) modelling and identification.

Identification of dynamic systems from input output measurements are an

important topic of scientific research with a wide range of practical applications.

Many real-world systems are inherently nonlinear and cannot be represented by

linear models used in conventional system identification [8]. Recently, there is a

strong focus on the development of methods for the identification of nonlinear

systems from measured data. Artificial neural networks and fuzzy models belong to

the most popular model structures used. From the input-output view, fuzzy systems

are flexible mathematical functions which can approximate other functions or just

data (measurements) with a desired accuracy. This property is called general function

approximation [9]. Compared to other well-known approximation techniques such as

artificial neural networks, fuzzy systems provide a more transparent representation of

the system under study, which is mainly due to the possible linguistic interpretation

in the form of rules. The logical structure of the rules facilitates the understanding

and analysis of the model in a semi-qualitative manner, close to the way human

reason about the real world.

13

2.2.1 Fuzzy Sets with a Continuous Universe

Figure 2.3: Membership Function on a Continuous Universe

Let X be the set of possible ages for human beings. Then the fuzzy set A =

“about 50 years old” may be expressed as

A (x))|x (2.2)

Where,

A (2.3)

The aforementioned example clearly expresses the dependence of the

construction of a fuzzy set on two things:

i. Identifying a suitable universe of discourse.

ii. Laying down a suitable membership function.

Mem

bers

hip

grad

e

0

.2

0.4

0.6

0.

8

1.0

1.

2

0 10 20 30 40 50 60 70 80 90 100 X = age

(x) = ___1___ (x – 50)4

1 + 10

14

At this point, it is imperative to state that the specification of membership

functions is subjective; meaning that membership functions stated for the same

notion by different persons will tend to vary noticeably. Subjectivity and non-

randomness differentiate the study of fuzzy sets from probability theory.

I. Crisp variable: A crisp variable is a physical variable that can be measured

through instruments and can be assigned a crisp or discrete value, such as a

temperature of 30 0Celsius, an output voltage of 8.55 Volt etc.

II. Linguistic variable: When the universe of discourse is a continuous space,

the common practice is to partition X into several fuzzy sets whose MFs

cover X in a more or less uniform manner. These fuzzy sets, which usually

carry names that conform to adjectives appearing in our daily linguistic

usage, such as “large”, “medium” or “small”, are called linguistic values.

Consequently, the universe of discourse X is often called the linguistic

variable.

2.3 Fuzzy Set-Theoretic Operations

The most elementary operations on classical sets include union, intersection

and complement. Analogous to these operations, fuzzy sets also have similar

operations which are explained below.

15

2.3.1 Containment or Subset

Fuzzy set A is contained in fuzzy set B (or, equivalently, A is a subset

of B) iff µA B (x) for all x. The following figure clarifies this concept.

Figure 2.4: Operations on Fuzzy sets - The concept of containment or subset

2.3.2 Union (Disjunction)

The union of two fuzzy sets A and B is a fuzzy set C, written as

, whose MF is related to those of A and B by

µc(x) = max (µA(x), µB(x)) = µA(x) µB(x) (2.4)

0 10 20 30 40 50 60 70 80 90 100

Mem

bers

hip

grad

e

0.2

0

.4

0.

6

0.8

1

A

B

16

Equivalently, union is the smallest fuzzy set containing both A and B. Then

again, if D is any fuzzy set encompassing both A and B, then it also contains

Figure 2.5: Operations on Fuzzy sets - Two Fuzzy sets A and B

2.3.3 Intersection (Conjunction)

The intersection of two fuzzy sets A and B is a fuzzy set C, written

as whose MF is related to those of A and B by

µc(x) = min (µA(x), µB(x)) = µA(x) µB(x) (2.5)

Analogous to the definition of union, intersection of A and B is the largest

fuzzy set which is contained in both A and B.

0 10 20 30 40 50 60 70 80 90 100

Mem

bers

hip

grad

e

0.2

0

.4

0.

6

0.8

1 A

B

17

Figure 2.6: Operations on Fuzzy sets - A B

2.3.4 Complement (Negation)

The complement of fuzzy set A, designated by Ā ( ¬A, NOT A), is defined

as: µA(x) = 1- µA(x) . (2.5)

Figure 2.7: Operations on Fuzzy sets - Fuzzy set A and its Complement Ā

0 10 20 30 40 50 60 70 80 90 100

Mem

bers

hip

grad

e

0.2

0

.4

0.6

0.

8

1 A

B

Mem

bers

hip

(Per

cent

)

5

0

10

0

NOT COOL COOL NOT COOL

A

A

18

2.4 Rule-Based Fuzzy Models

In rule-based fuzzy systems, the relationships between variables are

represented by means of fuzzy if–then rules of the following general form:

If antecedent proposition then consequent proposition:

The antecedent proposition is always a fuzzy proposition of the type “ is A” where

is a linguistic variable and A is a linguistic constant (term). The proposition’s truth

value (a real number between zero and one) depends on the degree of match

(similarity) between and A. Depending on the form of the consequent two main

types of rule-based fuzzy models are distinguished:

i. Linguistic fuzzy model: both the antecedent and the consequent are fuzzy

propositions.

ii. Takagi–Sugeno (TS) fuzzy model: the antecedent is a fuzzy proposition; the

consequent is a crisp function.

2.5 Electric Generator

An electric generator is a device used to convert mechanical energy into

electrical energy [10]. The generator is based on the principle of "electromagnetic

19

induction" discovered in 1831 by Michael Faraday, a British scientist. Faraday

discovered that the above flow of electric charges could be induced by moving an

electrical conductor, such as a wire that contains electric charges, in a magnetic field.

This movement creates a voltage difference between the two ends of the wire or

electrical conductor, which in turn causes the electric charges to flow, thus

generating electric current.

Figure 2.8: Electric Generator

The common type of electric generator, such as a bicycle dynamo, uses the

principle of electromagnetic induction to convert mechanical energy into electrical

energy. These devices carry one or more coils surrounded by a magnetic field,

typically supplied by a permanent magnet or electromagnet. In a direct current (DC)

generator, a mechanical switch (or Commutator) causes the rotor current to reverse

every half an electrical cycle so that the output current remains unidirectional. In an

alternating current (AC) generator, the rotor is driven by a turbine and electric

currents are induced in the stator winding. Large alternators in modern power

20

stations are of this type and they provide the electric power for general transmission

and distribution.

It must be turned by a prime mover which can be an internal combustion

engine - driven, usually, by diesel oil or gasoline or can be a turbine, driven either by

superheated steam or by hydro-electric power generation.

Generators are useful appliances that supply electrical power during a power

outage and prevent discontinuity of daily activities or disruption of business

operations. Generators are available in different electrical and physical

configurations for use in different applications. For example, to produce dc power

from an ac service or to produce 3-phase ac power from a single-phase ac service.

2.5.1 Principles of Electric Generator

The operation of a generator is based on Faraday’s law of electromagnetic

induction. If a coil or winding is linked to a varying magnetic field, then

electromotive force or voltage is induced across the coil. Thus, a generator has two

essential parts: one that creates a magnetic field and the other where the energy is

induced. The magnetic field is typically generated by electromagnets.

These windings are called field winding or field circuits. The coils where the

electro motive force energies are induced are called armature windings or armature

circuits. With rare exceptions, the armature winding of a synchronous machine is on

the stator, and the field winding is on the rotor. The field winding is excited by direct

21

current conducted to it by means of carbon brushes bearing on slip rings or collector

rings.

The rotor of the synchronous generator may be cylindrical or salient

construction. The cylindrical type of rotor has one distributed winding and a uniform

air gap. These generators are driven by steam turbines and are designed for high

speed 3000 or 1500 r.p.m (two and four pole machines respectively) operation. The

rotor of these generators has a relatively large axial length and small diameter to

limit the centrifugal forces.

The salient type of rotor has concentrated windings on the poles and non-

uniform air gaps. It has a relatively large numbers of poles, short axial length, and

large diameters. The generators in hydroelectric power stations are driven by

hydraulic turbines and they have salient pole rotor construction [11]. The cylindrical

and salient type rotors are shown in Figure 2.9. The rotor is also equipped with one

or more short-circuited windings known as damper windings. The damper windings

provide an additional stabilizing force for the machine during certain periods of

operation. When a synchronous generator supplies electric power to a load, the

armature current creates a magnetic flux wave in the air gap which rotates at

synchronous speed. This flux reacts with the flux created by the field current, and

electromagnetic torque results from the tendency of these two magnetic fields to

align. In a generator this torque opposes rotation and mechanical torque must be

applied from the prime mover to sub-stain rotation. As long as the stator field rotates

at the same speed as the rotor and no current is induced in the damper windings.

However, when the speed of the stator field and the rotor become different, currents

are induced in the damper windings. Currents generated in the damper windings

provide a counter torque. In this way the damper windings can keep the two speeds.

22

(a) Cylindrical Type

(b) Salient Type

Figure 2.9: Two Types of Rotor Construction:

Source: Hubret,C. (1991)

2.6 Voltage Regulator

Constant voltage at the generator terminals is essential for satisfactory main

power supply. The terminal voltage can be affected by various disturbing factors

(speed, load, power factor, and temperature rise) so that special regulating equipment

23

is required to keep the voltage constant, even when affected by these disturbing

factors. Power system operation considered so far was under condition of steady

load. However, both active and reactive power demands are never steady and they

continually change with the rising or falling trend.

The voltage regulator may be manually or automatically controlled. The

voltage can be regulated manually by tap-changing switches, a variable auto

transformer, and an induction regulator. In manual control, the output voltage is

sensed with a voltmeter connected at the output; the decision and correcting

operation is made by a human being. The manual control may not always be feasible

due to various factors and the accuracy, which can be obtained, depending on the

degree of instrument and giving much better performance so far as stability.

In modern large interconnected system, manual regulation is not feasible and

therefore automatic generation and voltage regulation equipment is installed on each

generator. Automatic Voltage Regulator (AVR) may be discontinuous or continuous

type. The discontinuous control type is simpler than the continuous type but it has a

dead zone where no single is given. Therefore, its response time is longer and less

accurate. Modern static continuous type automatic voltage regulator has the

advantage of providing extremely fast response times and high field ceiling voltages

for forcing rapid changes in the generator terminal voltage during system faults.

Electronic control circuit is now used for the field control circuit as the closed loop

system to obtain stable output voltage. Electronic control circuit is simple but the

simple is the best. By using this control circuit for the system, the system cost is

decreased and system reliability and design flexibility are increased.

A voltage regulator is an electricity regulator designed to automatically

maintain a constant voltage level. It may use an electromechanical mechanism, or

24

electronic components. Depending on the design, it may be used to regulate one or

more AC or DC voltages.

Electronic voltage regulators are found in devices such as computer power

supplies where they stabilize the DC voltages used by the processor and other

elements. In automobile alternators and central power station generator plants,

voltage regulators control the output of the plant. In an electric power distribution

system, voltage regulators may be installed at a substation or along distribution lines

so that all customers receive steady voltage independent of how much power is

drawn from the line.

There are numerous types of voltage regulators. These include positive

voltage regulators, which are used to convert positive supply voltages to different

positive voltage levels. There are also negative voltage regulators, which are used to

convert negative voltage levels to different negative voltage levels. Additionally

there are fixed and adjustable voltage regulators. Fixed voltage regulators will only

produce a specific voltage out for a given input voltage values. Adjustable voltage

regulators permit the output voltage of a regulator to be changed. This is often done

by connecting a variable resistor to the voltage regulator.

A voltage regulator feature, which is of major concern to electronic designers,

is the voltage regulator's ability to provide a constant voltage level out over a wide

range of input voltages. This feature is often referred to as the line regulation. An

ideal voltage regulator would always provide the same output voltage regardless of

the level of the input voltage. However, in real life, a voltage regulator's output

voltage will vary slightly with changes in input voltage.

72

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73

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