voltage stabilizer of generator output through
TRANSCRIPT
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
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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
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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
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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
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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)
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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.
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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].
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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.
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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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