APPLICATION OF ARTIFICIAL IMMUNE SYSTEM IN DESIGNING POWER
SYSTEMS STABILIZER
FREDDY PRASETIA BIN RIDHUAN
A project report submitted in partial fulfillment of the requirements
for the award of the degree of
Master of Engineering (Electrical – Mechatronics and Automatic Control)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
MAY, 2007
iii
Dedicated to my beloved parents, for their everlasting support and encouragement
to complete the course of this study.
iv
ACKNOWLEDGEMENT
Alhamdullillah, I am grateful to ALLAH SWT for His blessing and mercy
in
making this project successful.
I wish to express my sincere appreciation to my project supervisor Dr. Hj.
Mohd. Fauzi Othman for his effort, encouragement and guidance. In preparing this
project report, I did a lot of reading and research on past projects, thesis and
journals for my reference. They have given me tips and useful information in order
for me to complete my analysis and research.
To all the lecturers who have taught me, thank you for the lessons you have
delivered. I would also like to thank my friends, thank you for their useful ideas,
information and moral support during the course of study.
Last but not least, I would like to express my heartiest appreciation to my
parents, who are always there when it matters most.
v
Abstract
Biological Immune system is a control system that has strong robusticity
and self-adaptability in complex disturbance and indeterminacy environments. This
thesis proposes an appropriate artificial immune system algorithm to develop an
immune controller. The idea of immune controller is adept and derived from
biological vertebrate immune system. Mimicking and imitating of biological
immune system or better known as the artificial immune system is thus developed.
Applying and implementing of the algorithm of the artificial immune system is to
develop an immune controller. There are various model of artificial immune
controller but only the most suitable will be selected. The selected artificial
immune controller has the resemblance and similarity of a proportional integral
derivative controller. The selected immune controller is to be implemented into the
power systems stabilizer. The immune controller is to obtain and achieve system
goals in enhancing the performance and stability of power systems. The approach
is to prove that an immune controller using artificial immune system algorithm can
be used as a controller to obtain steady state output response.
vi
Abstrak
Sistem kekebalan biologi merupakan sistem kawalan yang mempunyai
kebolehgunaan dan penyesuaian diri yang kuat dalam menghadapi gangguan yang
kompleks dan persekitaran yang tidak diduga. Tesis ini mencadangkan algoritma
sistem kekebalan tiruan untuk membangunkan kawalan kekebalan. Idea kawalan
kekebalan diperolehi daripada sistem kekebalan biologi daripada haiwan vetebrata.
Meniru gaya sistem kekebalan biologi atau lebih dikenali sebagai sistem kekebalan
tiruan boleh dicipta. Menggunakan algoritma daripada sistem kekebalan tiruan
untuk membangun kawalan kekebalan. Terdapat pelbagai jenis kawalan kekebalan
tiruan tetapi hanya yang paling sesuai akan dipilih. Kawalan kekebalan tiruan yang
dipilih mempunyai ciri-ciri dan persamaan dengan kawalan pengkamilan,
pembezaan dan pendaraban. Kawalan kekebalan yang terpilih akan digunakan
kedalam sistem penstabilan kuasa. Kawalan kekebalan bertujuan untuk mencapai
matlamat dalam meningkatkan keupayaan dan menstabilkan sistem kuasa. Capaian
ini adalah untuk membuktikan bahawa kawalan kekebalan menggunakan algoritma
sistem kekebalan tiruan boleh digunakan sebagai kawalan untuk mencapai tindak
balas keluaran yang stabil.
vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF SYMBOLS
xv
LIST OF ABBREVIATIONS
xvi
INTRODUCTION
1
1.1 Introduction
1
1.2 Objectives
2
1.3
Scope of Work
3
1.4
Expected Contribution
4
viii
2
3
ARTIFICIAL IMMUNE SYSTEM
5
2.1 Introduction
5
2.2 Innate Versus Acquired Immunity
7
2.2.1 Innate Immunity
7
2.2.2 Acquired Immunity
7
2.3 Antigens
8
2.4 Immune Cells
9
2.5 B-Cells and Antibodies
9
2.6 T-Cells and Lymphokines
10
2.7 Macrophages
10
2.8 An Overview of the Immune System
10
2.8.1 Humoral Response
11
2.8.2 Cell Mediated Response
12
2.9 Analysis of Lines of Defense
13
2.10 Memory Cells
13
2.10.1 Memory T Cells
14
2.10.2 Memory Helper T Cells
14
2.10.3 Memory B Cells
15
LITERATURE REVIEW ON APPLICATION OF AIS
16
3.1 Introduction
16
3.2 Computer Security
19
3.3 Anomaly Detection in Time Series Data
20
3.4 Fault Diagnosis
22
3.5 Pattern Recognition
23
3.6 Autonomous Agents
25
ix
4
PROPOSITION OF ARTIFICIAL IMMUNE
28
CONTROLLER ALGORITHM
5
4.1 Introduction
28
4.2 Basic Varela Immune Network Model
29
4.3 Improved Varela Immune Network Model
31
4.4 Design and Analysis of Immune Controller
32
4.5 Sample Simulation Result
36
4.6 Analysis of IVINC parameters
38
POWER SYSTEMS STABILIZER BY AIS
40
5.1 Introduction
40
5.2 Fixed Parameter Controllers
41
5.3 Conventional PSS
41
5.4 Artificial Immune System PSS
43
5.5 The Two Area Test Systems
46
5.6 Result and Analysis
50
5.6.1 Delta w PSS Controller
51
5.6.2 Multi Band_PSS Controller
59
5.6.3 Comparison IVINC PSS with Delta w PSS
67
5.6.4 Comparison IVINC PSS With Multi Band PSS
85
5.6.5 Comparison IVINC PSS with Delta pa PSS
93
5.6.6 Analysis of IVINC PSS Controller
101
5.6.7 IVINC pa PSS Controller
109
5.6.8 Comparison IVINC pa PSS With No PSS
110
5.6.9 Comparison IVINC pa PSS With Delta w PSS
123
5.6.10 Comparison IVINC pa PSS With Multi Band PSS
139
5.6.11 Analysis of IVINC pa PSS Controller
147
5.7 Summary of The Analysis
162
x
6
CONCLUSION
159
6.1 Conclusion
159
6.2 Future Works and Recommendation
160
REFERENCES
161
xi
LIST OF TABLES
TABLE NUMBER
TITLE
PAGE
5.5.1
Parameter of Delta w PSS
51
5.5.2
Parameter of Multi Band PSS
59
5.5.3
Range of Gain K and Gain K3
67
5.5.4
Ideal value of gain k, 0.7<k<1.0
113
5.5.5
The effect of changing value k in IVINC pa PSS
150
5.6.1
Delta w and Multi Band PSS
162
5.6.2
The input of speed deviation with respect
163
of nominal (dw in pu)
5.6.3
The input of power acceleration with
respect to nominal (pa=pm-pe in pu)
164
xii
LIST OF FIGURES
FIGURE NUMBER
TITLE
PAGE
2.1
Antigen Antibody Interaction
8
2.2
Production of Antibodies
9
2.3
An Overview of an Immune System
11
2.4
Humoral Response
12
2.5
The cell Mediated Response
13
4.1
M(σI) is the mature function of the Bi cell
30
4.2
The M(σ) and P(σ) curve of actual control system
34
4.3
A biological immune controller
35
4.4
Artificial immune control system structure
36
4.5
Pulse Generator Input to the System
37
4.6
Simulation result of the output system response
38
4.7
Simulation result of the output system response when k3=50
39
4.8
Simulation result of the output system response when k=0.25
39
5.1
System Model Used In the PSS Simulation
42
5.2
Improved Varela Immune Network Controller
45
5.3
Implementation of IVINC into the PSS
46
5.4
Test Area System
47
5.5
Generator 1 and 2 of Area 1 and Generator 3 and 4 of Area 2
48
5.6
PSS Controllers
49
5.7
Delta W Controller
51
5.8
a), b), c) and d)
52,53
xiii
5.9
a), b), c) and d)
55,56
5.10 a), b), c) and d)
57,58
5.11 Multi Band Controller
59
5.12 a), b), c) and d)
60,61
5.13 a), b), c) and d)
63,64
5.14
a), b), c) and d)
65,66
5.15 a), b), c) and d)
68,69
5.16 Speed deviation Difference of Gen 1 and Gen 2
70
5.17 a), b), c) and d)
72,73
5.18 a), b), c) and d)
74,75
5.19 a), b), c) and d)
77,78
5.20 Speed deviation Difference of Gen 1 and Gen 2
79
5.21 a), b), c) and d)
81,82
5.22 a), b), c) and d)
83,84
5.23 a), b), c) and d)
86,87
5.24
a), b), c) and d)
89,90
5.25 a), b), c) and d)
91,92
5.26 a), b), c) and d)
94,95
5.27 a), b), c) and d)
97,98
5.28 a), b), c) and d)
99,100
5.29 a), b), c) and d)
102,103
5.30 a), b), c) and d)
105,106
5.31 a), b), c) and d)
101,102
5.32 a), b), c) and d)
111,112
5.33 a) and b)
114
5.34 a) and b)
115
5.35 a) and b)
116
5.36 a) and b)
117
5.37 a), b), c) and d)
119,120
5.38 a), b), c) and d)
121,122
5.39 a), b), c) and d)
124,125
5.40 a), b), c) and d)
127,128
5.41 a), b), c) and d)
129,130
5.42 a), b), c) and d)
132,133
xiv
5.43
a), b), c) and d)
135,136
5.44 a), b), c) and d)
137,138
5.45 a), b), c) and d)
140,141
5.46 a), b), c) and d)
143,144
5.47 a), b), c) and d)
145,146
5.48 a), b), c) and d)
148,149
5.49 a) and b)
151
5.50 a) and b)
152
5.51 a) and b)
153
5.52 a) and b)
154
5.53 a) and b)
155
5.54 a) and b)
156
5.55 a), b), c) and d)
158,159
5.56 a), b), c) and d)
160,161
xv
LIST OF SYMBOLS
Ti
-
quantity of the antibody
Bi
-
quantity of B cell
K1
-
mortality of the antibody which is caused by the antibody
interaction
K2
-
natural mortality of the antibody
K3
-
reproduction rate of antibody which is caused by the mature B cell
K4
-
mortality of the B cell
K5
-
reproduction rate of B cell which is caused by the B cell itself
K6
-
new reproduction rate of B cell which is caused by the bone marrow
M(σi) -
mature function of the Bi cell
P(σi) -
reproduction function of which the Bi cells reproduce the Ti
antibody
Q
-
reproduction rate of the antigen when the immune process doesn’t
exist
Ke
-
approximate rate of antigen’s being specially eliminate
Ag
-
the reproduction of antigen
e(t)
-
error of the control system
u(t)
-
output of the immune controller
f(e,u) -
immune controller
G(s)
-
object controlled by the immune controller
r(t)
-
input signal
y(t)
-
output response
xvi
LIST OF ABBREVIATIONS
AI
-
Artificial Intelligence
AIS
-
Artificial Immune System
ANNPSS
-
Artificial Neural Network Power Systems Stabilizer
APCs
-
Antigen Presenting Cells
APSS
-
Adaptive Power Systems Stabilizer
AVR
-
Automatic Voltage Regulator
BVINM
-
Basic Varela Immune Network Model
CPSS
-
Conventional power System Stabilizer
DARS
-
Distributed Autonomous Robotic System
FLCPSS
-
Fuzzy Logic Controller Power System Stabilizer
GA
-
Genetic Algorithm
NFPSS
-
Neuro Fuzzy Power Systems Stabilizer
PSS
-
Power Systems Stabilizer
IVINC
-
Improved Varela Immune Network Controller
IVINM
-
Improved Varela Immune Network Model
VINM
-
Varela Immune Network Model
1
CHAPTER 1
INTRODUCTION
1.1 Introduction
The successful operation of a power system depends largely on the
engineer’s ability to provide reliable and uninterrupted service to load. The
reliability of the power supply implies much more than merely being available.
Ideally, the loads must be fed at constant voltage and frequency at all times. In
practical terms this means that both voltage and frequency must be held within
close tolerances so that the consumer’s equipment may operate satisfactorily. For
example, a drop in voltage of 10-15% or a reduction of the system frequency of
only a few hertz may lead to stalling of the motor loads on the system. Thus it can
be accurately stated that the power system operator must maintain a very high
standard of continuous electrical service.
Electrical power systems are among the largest structural achievement of
man. Some transcend international boundaries, but others supply the local needs of
a ship or an aero-plane. The generators within an interconnected power system
usually produce alternating current and are synchronized to operate at the same
2
frequency. In a synchronized system, the power is naturally shared between
generators in the ratio of the rating of the generators, but this can be modified by
the operator. Systems which operate at different frequencies can also be
interconnected, either through a frequency converter or through a direct tie. A
direct current tie is also used between system that, while operating at the same
nominal frequency, have difficulty in remaining in synchronism if interconnected.
Conventional power systems stabilizers contain a phase lag/lead network
for phase compensation has played a very significant role in enhancing the stability
of power systems. There are various new approaches based on modern control and
artificial intelligence techniques to improve the performance of the power systems
stabilizer being proposed during the past 30 years. Although it is feasible to
develop a satisfactory stabilizer using any one of these techniques, each has its
unique strengths and drawbacks. One of the proposed techniques is the application
of artificial immune system to power system stabilizer. This paper proposes an
optimization algorithm imitating the immune system to design power systems
stabilizer in enhancing the stability of power and to improve damping of low
frequency oscillations using a suitable artificial immune algorithm.
1.2 Objectives
The objectives of this thesis are to study and analyze for the mathematical
model and algorithm of artificial immune system. Here are various types of
mathematical mode of immune algorithm can be found from books, journals, thesis
papers, internet etc. The artificial immune algorithm to be chosen in this analysis
must have the similarity or heuristic between the artificial immune controller and
the control system itself. By using a selected artificial immune algorithm an
immune controller is to be developed. The immune controller is then tested and
simulated using MATLAB Simulink to observe its output response and
performance. Once the desired immune controller is obtained,
the immune
3
controller is implemented to a power systems stabilizer. The application of this
immune controller is to design a power systems stabilizer which optimizes the
performance of power systems and enhances the stability of power. The main
objective of the immune controller is to enhance the quality of the control system
and the damping of low frequency oscillations in the power systems stabilizer.
From the various parameters of the IVINC controller, we can conduct
analysis from the simulation results to obtain steady state output response. These
parameters will be the guideline or reference for the implementation of further test
and analysis of IVINC controllers. The IVINC controller will be implemented into
the two area test system of the power systems stabilizer. The IVINC controller will
be pair with other conventional controller using various combinations to analyze
the systems output response. The purpose of the analysis is to compare between the
IVINC and the conventional controller in obtaining stable output response.
Different combinations of controllers produce different output response, stability,
settling time and peak.
1.3 Scope of Work
The scope of work is to study and analyze various mathematical model of
immune algorithm in order to design immune controller. The mathematical model
of the immune algorithm must have the quality or other relation or characteristics
of the control system. With a selected artificial immune system elements and
algorithm the purpose of the project is to design an artificial immune controller.
The controller then has to be tested and simulated using a MATLAB Simulink.
Once the appropriate immune algorithm has been obtained, we can use it to design
a power systems stabilizer. The artificial immune system algorithm technique can
be used to develop a satisfactory stabilizer so as to enhance the stability of the
power system. The immune controller is to be implemented into the power system
4
transfer function using MATLAB Simulink. From there we can observe the output
response. Improvement and adjustment of the immune controller variables need to
be conducted from time to time in order to obtain a good result and performance of
the output response of the power system.
1.4 Expected Contribution
The artificial immune controller is the first method to be implemented to the
two test area system of the power systems stabilizer. Through analysis and
simulation it is observed that IVINC controller can perform as well as other
controllers in achieving stability. The IVINC Controller is able to produce good
simulation result in damping low frequency oscillation in power systems just like
other
conventional
controllers.
Furthermore,
IVINC
controllers can
implemented and applied in other control system applications.
be
5
CHAPTER 2
INTRODUCTION TO ARTIFICIAL IMMUNE SYSTEM
2.1 Introduction
The main function of the immune system is to protect the body from
pathogens and cancer. Vertebrate immune systems are more complex than the
invertebrates. They are characterized by two important properties, which are
memory and specificity. In the case of invertebrate, the immune system consists
mainly of Phagocytes which are nonspecific. This means that it will not remember
any previous antigen, and will use the same attacking strategy each time.
Phagocytes has no receptors for specific pathogens, which means that these cells
will engulf and try to kill any pathogen. On the other hand, the vertebrate host has
evolved more specialized cells called Lymphocytes. These Lymphocytes are
pathogen specific, which means that they have distinct receptors to interact with
different pathogens. To combat antigens, nature has provided us with the immune
system. The blood, lymph nodes, and bone marrow act with the liver, spleen,
thymus, and tonsils to produce and deliver specialized cells, including Blymphocytes, T lymphocytes, and phagocytes. These cells limit the severity and
duration of colds, Fight infections in the nose and throat, help wounds to heal,
destroy some cancers, and much more.
6
There are two types of immune models [3,5]:
1) Immune model based on the immune system theory (mainly clones choice
theory nowadays).
a) The somatic theory describes that somatic recombination and
mutation contribute to increasing the diversity of antibody.
b) The network hypothesis describe that a mutual recognition network
among antibody contributes to control of the proliferation of clones.
2) Immune network model based on the immune network theory.
a) All the continuous immune network models at present are the
ordinary differential equation of time, which conforms to the real
control system.
b) The discrete immune network model is not the common discrete
model based on time control system, but it means that the immune
cells or molecules are separated among each others.
Figure 2.0: Types of Immune Models
7
2.2 Innate Versus Acquired Immunity
There are two types of immunity, innate immunity and adaptive or
acquired immunity. Also, the immune system response can be divided into
humoral immunity, and cell mediated response.
2.2.1 Innate Immunity
The innate immunity can be regarded as natural resistance of the host to
foreign pathogens. There are a number of external and internal lines of defenses in
the innate immunity. As an examples we find Lysozymes in tears, and skin
inflamation as a resistance to a peneterating pathogen. The innate immunity is the
first line of defense against the foreign pathogens, and it uses the non-specific
strategy while attacking it. Phagocytes engulf the foreign pathogen, and try to kill
it. Some examples on the same line of defense are Monocytes, Macrophages, and
Neutrophils. There are other types of cells that is called Natural killer cells NKcells that also use non-specific response to protect the host against the foreign
pathogen.
2.2.2 Acquired Immunity
In contrast to the innate immune system, the acquired immune system uses
a specific response to pathogens. The important advantage of the acquired
immunity is the use of memory through lymphocytes. After getting rid of the
foreign pathogen the lymphocytes change into memory cells. These memory cells
will recognize rapidly the same pathogen when it evades the host again, and
eliminate it before causing any damage. The two major types of lymphocytes are
T-cells, and B-cells. B-cells have direct contact with the antigen when interacting
8
with it. On the other hand, T-cell can bind to the antigen only after it is processed
and presented by other cells. B-cells are the basic building block of the humoral
immunity through the production of antibodies. Cell mediated immunity is
contributed by T-cells mediated response. Tcells have many forms like the helper
T-cell which helps either B-cells, or phagocytic macrophages. Another form that
the T-cell can be is the cytotoxic T-cells, which recognize cells infected by virus or
cancer, and eliminate them.
2.3 Antigens
An antigen (Ag) can be defined as a substance that triggers specific
immune response. In vertebrates, the host system does not respond to its own
proteins, and that is called tolerance. T-cells and B-cells that are capable of
recognizing self-cells are eliminated during maturation phase,. An antigen may
carry several epitops, and consequently this will trigger the production of several
antibodies, see Figure 2.1. Generally, T or B cells do not recognize all of these
epitopes, instead they recognize part of it. So, a single Ag may attract the attention
of several T or B cells. Also, two different antigens may carry the same
crossreactive epitopes, which means that an antibody produced for that antigen can
interact with another one .
Figure 2.1 Antigen Antibody Interactions
2.4 Immune Cells
9
Cells destined to become immune cells are produced in the bone marrow.
The descendants of some stem cells become lymphocytes, while others develop
into a second group of immune cells known as phagocytes. The two major classes
of lymphocytes are B cells and T cells. B cells complete their maturation in the
bone marrow. On the other hand, T cells migrate to the thymus; an organ that lies
high behind the breastbone. Each lymph node contains specialized compartments
that house a great number of B lymphocytes, T lymphocytes, capable of presenting
antigen to T cells. Thus, the lymph node brings together the several components
needed to start an immune response.
2.5 B-Cells and Antibodies
B-Cell is one of the major arms of the immune system mechanisms, and it
is responsible for the humoral response. The name humoral comes from these
fluids that circulate around the body known as humors. Each B cell is programmed
to make one specific antibody. When a B cell encounters its triggering antigen, it
produces many large plasma cells. Every plasma cell is a factory for producing
antibody. Each of the plasma cells descended from a given B cell produces
millions of identical antibody molecules and pours them into the bloodstream, see
Figure 2.2. A given antibody matches an antigen as a key matches a lock, and
marks it for destruction.
Figure 2.2 : Production of Antibodies
2.6 T-Cells and Lymphokines
10
T-Cells play two rolls in the immune system defense. B cells cannot make
antibody against most substances without regulatory T-cell help. On the other
hand, Cytotoxic Tcells, directly attack body cells that are infected. Another
important regulatory T cells are "helper" cells. Typically identifiable by the T4 cell
marker, helper T cells activate B cells and other T cells as well as natural killer
cells and macrophages. Another subset of T cells contributes by turning off or
"suppress" these cells. T cells work by secreting cytokines or, Lymphokines which
are considered to be chemical messagers.
2.7 Macrophages
Macrophages are responsible for carrying the initial attack against an
invasion launched by antigens. Macrophages are distributed throughout body
tissues , and they rid the body of worn-out cells and other debris. Foremost among
the cells that "present" antigen to T cells, having first digested and processed it,
macrophages play a crucial role in initiating the immune response. As secretory
cells, Monocytes and Macrophages are essintial to the regulation of immune
responses and the development of inflammation; they produce an array of powerful
chemical called Monokines including enzymes, complement proteins, and
regulatory factors such as interleukin-1. Sometimes antigens change themselves,
and that is why we continue to get sick.
2.8 An Overview of the Immune System
When foreign antigen enters the body, it triggers B-cells to produce
antibodies, which bind to the antigen and clear it from the body; this is called
Humoral immune response. The cell-mediated response involves helper T-cells
and T cytotoxic (CTL) cells. Helper T-cells (Th) can be divided into two sub
11
fields: Th1 and Th2. Th1 cells help B-cells, where Th1 cells activate macrophages.
CTL cells kill virtually infected or Cancer cells, see Figure 2.3.
Figure 2.3 : An Overview of an Immune System
2.8.1 Humoral Response
When the B-cell proliferates, all of its descendants will make this uniquely
rearranged set of antibodies. B-cells continue to multiply, various mutants arise;
these allow for the natural selection of antibodies that provide better and better
"fits" for antigen elimination. The result of this entire process is that a limited
number of B-cells can respond to an unlimited number of antigens. Antibodies are
triggered when a B-cell encounters its matching antigen, and digest it. Antigen
fragments are displayed on B-cell distinctive markers. The combination of antigen
12
fragments, and marker molecules attract the mature matching helping cells. T-Cells
secrete Lymphokines allow B-cells to multiply and mature into antibody producing
Plasma cell. Antibodies are released into the blood stream, and they lock into
matching antigens. These antigen-body complexes are soon overcome either by the
complement cascade, or by the liver and spleen, see Figure 2.4.
Figure 2.4 : Humoral Response
2.8.2 Cell Mediated Response
Machrophages initiate the cell mediated response, or by other antigenpresenting cell. The antigen-presenting cell digest the antigen, and then displays
antigen fragments on its own surface. Bound to the antigen fragment is an MHC
molecule. These fragments capture the T cell's attention. A T cell whose receptor
fits this antigen binds to it. This bond stimulates the antigen-presenting cell to
secrete Interleukins required for T cell activation and performance, see Figure 2.5.
13
Figure 2.5 : The cell Mediated Response
2.9 Analysis of Lines of Defense
The human immune system attempts to quickly control the spread of
antigens once they have been identified. There are several other lines of defense
against antigens besides the immune system. The first line of defense is the skin,
which prevents the invasion of most micro organisms. The proteins and acidity of
the saliva in the mouth and stomach digest harmless microorganisms. However, if
there is a cut in the skin, or fluid transmission occurs, pathogens can invade the
body. The second line of defense is the cell-mediated response of the immune
system. Macrophages are circulating throughout the body that destroy the invading
microorganisms by phagocytosis. The last line of defense is known as the humoral
immune response. Many types of immune cells are triggered to move into the
affected area, and a great deal of antibodies and phagocytes destroy the invading
antigen.
2.10 Memory Cells
Some of the lymphocytes activated during the primary immune response
remain dormant and keep circulating in the immune system for a long time. These
lymphocytes carry the memory of the encountered antigen, and therefore these
long-lived cells are called memory cells. Memory can also be maintained by longlived antigen (not necessarily by a population of long-lived distinct memory cells).
14
Whenever T cells and B cells are activated, some of the cells become "memory"
cells. Then, the next time that an individual encounters that same antigen, the
immune system is primed to destroy it quickly. The degree and duration of
immunity depend on the kind of antigen, its amount, and how it enters the body.
An immune response is also dictated by heredity; some individuals respond
strongly to a given antigen, others weakly, and some not at all.
2.10.1 Memory T Cells
Memory T cells are formed during an immune response. As the term
implies, memory T cells remember past attacks by antigens, and can respond with
increased strength during subsequent invasions by a particular pathogen. Memory
T cells are long lasting immune cells, and react to particular antigens. Unlike T
cells that recirculate in the blood and lymph, memory T cells often circulate
throughout the entire body, especially in the site they were originally activated.
Memory T cells rely on memory helper T cells for launching a global immune
response. Look below for links
2.10.2 Memory Helper T Cells
Memory helper T cells are also known as memory effector T cells. Memory
helper T cells are used by memory T cells to launch an immune response against
an attack by pathogens. Memory helper T cells react in much the same way as
helper T cells, except that they are stimulated by memory T cells. Memory helper
T cells can differentiate into a cytotoxic T cell that attacks abnormal cells, or into a
helper T cell that stimulates an immune response from B cells. Look below for
links to other immune cells.
15
2.10.3 Memory B Cells
Little is currently known about memory B cells. However, memory B cells
are probably similar to memory T cells in that they retain a strong affinity to low
concentrations of antigen, and are able to launch a strong immune response
following stimulation by a particular antigen that they are sensitive to. Like normal
B cells, memory B cells circulate throughout the entire body. However, they are
significantly longer lived, on scales of a few months to years. Look below for links
to other immune cells.
16
CHAPTER 3
LITERATURE REVIEW ON APPLICATION OF ARTIFICIAL IMMUNE
SYSTEM
3.1 Introduction
Recently researchers have begun to argue that intelligent behavior and
cognition are much more about effective interaction between agent and
environment, rather than an agent’s capability to handle abstract world models
internally. Based on these influences the field of behavior-oriented AI has
emerged, which unlike its traditional counter part, is mainly concerned with the
study of autonomous agents, situated in and interacting with an environment.
Typical criticisms of conventional artificial intelligent systems are that these
systems show brittleness for environmental changes, and required much computing
time for mapping complex sensory inputs into complex internal models before
action can be taken. Therefore, in recent years much attention has been focused on
the reactive planning systems (e.g., behavior-based Al), which have demonstrated
robustness and flexibility against dynamically changing world. On the other hand,
biological information processing systems have many interesting functions and are
expected to provide various feasible ideas to engineering fields, especially
robotics. Biological information processing systems in living organisms can be
mainly classified into the following four systems: (1) brain-nervous system, (2)
genetic system, (3) endocrine system, and (4) immune system. Nervous and genetic
systems have already been applied to engineering fields by modeling as neural
17
networks, and genetic algorithms [8], and they have been widely used in various
fields. Immune system, in particular, have various interesting features such as
immunological memory, immunological tolerance, micro-pattern recognition,
nonhierarchical distributed structure, and so on that can be applied to many
engineering fields. In the following lines we will brief some of the basic features of
the immune system.
• Recognition: The immune system can recognize and classify different patterns
and generate selective responses. Recognition is achieved by intercellular binding
the extent of this binding is determined by molecular shape and electrostatic
charge. Self-non-self discrimination is one of the main tasks of the immune system
deals with during the recognition process.
• Feature Extraction: Antigen Presenting Cells (APCs) interpret the antigenic
context and extract its features, by processing and presenting antigenic peptides on
its surface. These APC servers as a filter and a lens: a filter that destroy molecular
noise, and a lens that focuses the attention of the lymphocyte receptors.
• Diversity: It uses combinatorics, usually done by a genetic process for generating
a diverse set of lymphocyte receptors to ensure that at least some lymphocytes can
bind to any known or unknown antigen.
• Learning: It learns, by experience, the structure of a specific antigen. Changing
Lymphocyte concentration is the mechanism for learning and takes place during
the primary response of Ag interception. So the learning ability of the immune
system lies primarily in the mechanism which generates new immune cells on the
basis of the current state of the system (also called clonal selection mechanism).
• Memory: When lymphocytes are activated, a few of each kind become special
memory cells which are content-addressable, and continues to circulate in the
blood. The life time of immune memory cells is dynamic and requires stimulation
by antigens. The immune system keeps an ideal balance between economy and
performance in conserving a minimal but sufficient memory of the past, and this is
done normally by using short-term and long-term memory mechanisms.
18
Distributed Detection: The immune system is inherently distributed. The immune
cells, in particular lymphocytes, circulate through the blood, lymph, lymphoid
organs, and tissue spaces. As lymphocytes recirculate, if they encounter antigenic
attacks, they stimulate specific immune responses.
• Self-regulation: The basic mechanisms of immune responses are self-regulatory
in nature. There is no central organ that controls the functions of the immune
system. The regulation of immune responses can be either local or systemic,
depending on the route and property of the antigenic challenge.
• Co-stimulation: Activation of B cells are closely regulated through costimulation. The second signal coming from helper T cells helps to ensure
tolerance and judge the invader is dangerous, harmless, or false alarm.
• Dynamic protection: Clonal expansion and somatic hyper-mutation allow
generation of high-affinity immune cells which are called affinity maturation. This
process dynamically balances exploration versus exploitation in adaptive
immunity. Dynamic protection increases the coverage provided by the immune
system over time. There are other features like adaptability, specificity, selftolerance, differentiation etc., and they perform important functions in immune
response. All these remarkable information-processing properties of the immune
system can be utilized several important aspects in the field of computation. Recent
studies have clarified that the immune system does not only detect and eliminate
the non-self materials, but plays important roles to maintain its own system against
dynamically changing environments. Therefore, immune system would provide a
new paradigm that is suitable for dynamic problem dealing with unknown
environments rather than static problem. However, the immune system has little
been applied to engineering fields in spite of its productive characteristics. In the
following sections we will scan some of the applications in the literature on the
immune system. Then we will elaborate to our research, and its importance.
19
3.2 Computer Security
There are many problems encountered while trying to apply computer
security, such activities as detecting unauthorized use of computer facilities,
keeping the integrity of data files, and preventing the spread of computer viruses.
Forrest et al viewed these protection problems as instances of the more general
problem of distinguishing self as legitimate users, corrupted data, etc., and from
non-self as unauthorized users, viruses, etc. They introduce a change-detection
algorithm that is based on the way that natural immune systems distinguish self
from other. Mathematical analysis of the expected behavior of the algorithm allows
them to predict the conditions under which it is likely to perform reasonably.
Based on this analysis, they also reported preliminary results illustrating the
feasibility of the approach on the problem of detecting computer viruses. They
demonstrate that the algorithm can be practically applied remains an open problem,
and finally, they suggest that the general principles can be readily applied to other
computer security problems.
Kephart et al anticipated that with in the next few years, the Internet will
provide a rich medium for new breeds of computer viruses capable of spreading
faster than today’s viruses [8]. To counter this threat, They have developed an
immune system for computers that senses the presence of a previously unknown
virus, and within minutes automatically derives and deploys a prescription for
detecting and removing it to other PC's in the network. Their system was integrated
with a commercial anti-virus product, IBM Anti- Virus.
Their immune system algorithm consists of the following steps:
1) Discovering a previously unknown virus on a user’s computer.
2) Capturing a sample of the virus and sending it to a central computer.
3) Analyzing the virus automatically to derive a prescription for detecting and
4) removing it from any host object.
20
5) Delivering the prescription to the user’s computer, incorporating it into the
6) anti-virus data files, and running the anti-virus product to detect and remove
7) all occurrences of the virus.
8) Disseminating the prescription to other computers in the user’s locale and to
the
9) rest of the world.
Dasgupta et al conducted a research that focuses on investigating
immunological principles in designing a multi-agent system for intrusion detection
and response in networked computers [1]. In this approach, the immunity-based
agents roam around the machines (nodes or routers), and look for changes such as
malfunctions, faults, abnormalities, misuse, deviations, intrusions, etc. These
agents can mutually recognize each other's activities and can take appropriate
actions according to the underlying security policies. Their activities are
coordinated in a hierarchical fashion while sensing, communicating and generating
responses. Such an agent can learn and adapt to its environment dynamically and
can detect both known and unknown intrusions. Their research is the part of an
effort to develop a multi-agent detection system that can simultaneously monitor
networked computer's activities at different levels (such as user level, system level,
process level and packet level) in order to determine intrusions and anomalies.
Their proposed intrusion detection system is designed to be flexible, extendible,
and adaptable that can perform real-time monitoring in accordance with the needs
and preferences of network administrators.
3.3 Anomaly Detection in Time Series Data
Detecting anomalies in time series data is a problem of great practical
interest in many manufacturing and signal processing applications. Dasgupta et al
presented a novel detection algorithm inspired by the negative-selection
mechanism of the immune system, which discriminates between self and non-self
[1]. Self is defined to be normal data patterns and non-self is any deviation
21
exceeding an allowable variation. Experiments with this novelty detection
algorithm are reported for two data sets: simulated cutting dynamics in a milling
operation and a synthetic signal. The results of the experiments exhibiting the
performance of the algorithm in detecting novel patterns were reported.
Anomaly detection in a system or a process behavior is very important in
many real world applications such as manufacturing, monitoring, signal processing
etc. Dasgupta et al presented an anomaly detection algorithm inspired by the
negative-selection mechanism of the immune system, which discriminates between
self and other. Here self is defined to be normal data patterns and non-self is any
deviation exceeding an allowable variation. Experiments with this anomaly
detection algorithm are reported for two data sets: time series data, generated using
the Mackey-Glass equation and a simulated signal. Compared to existing methods,
this method has the advantage of not requiring prior knowledge about all possible
failure modes of the monitored system. Results are reported to display the
performance of the detection algorithm.
Ishida et el proposed a new information processing architecture which is
extracted from the immune system. By focusing on informational features of the
immune system (i.e. specificity, diversity, tolerance, and memory), an immune
algorithm is proposed. The algorithm proceeds in three steps: diversity generation,
establishment of self-tolerance, and memorizing non-self. The algorithm may be
used to model the system by distributing agents. In this case, the system (the self)
as well as the environment (the non-self) are unknown or cannot be modeled.
Agent-based architecture based on the local memory hypothesis and networkbased architecture based on the network hypothesis is discussed. Agent-based
architecture elaborated with the application to adaptive system where the
knowledge about environment is not available. Adaptive noise neutralizer is
formalized and simulated for a simple plant.
22
D’haeseleer et al presented a new achievements on a distributable changedetection method inspired by the natural immune system. A weakness in the
original algorithm was the exponential cost of generating detectors. Two detectorgenerating algorithms are introduced which run in linear time. The algorithms are
analyzed, heuristics are given for setting parameters based on the analysis, and the
presence of holes in detector space is examined. The analysis provides a basis for
assessing the practicality of the algorithms in specific settings, and some of the
implications are discussed.
3.4 Fault Diagnosis
The body’s immune system is impressively good at coping with external
and internal errors, usually known as bacteria and viruses. The body is able to
distinguish the hemoglobin found in blood from the insulin secreted by the
pancreas from the vitreous humor contained in the eye from everything else. It
must manage to repel innumerable different kinds of invading organisms and yet
not attack the body. Tyrell posed a question which is “can we mimic these
mechanisms in the design of our computer systems?”. He gave some details on
how the body actually performs this amazing feat and gives some suggestions as to
how this might inspire the design of computer systems increasing their reliability.
Braddly et al proposed a novel approach to hardware fault tolerance that
takes inspiration from the human immune system as a method of fault detection
and removal. The immune system has inspired work within the areas of virus
protection and pattern recognition yet its application to hardware fault tolerance is
untouched. Their paper introduces many of the ingenious methods provided by the
immune system to provide reliable operation and suggests how such concepts can
inspire novel methods of providing fault tolerance in the design of state machine
hardware systems. Through a process of self/non-self recognition the proposed
hardware immune system will learn to differentiate between acceptable and
23
abnormal states and transitions within the immunized system. Potential faults can
then be agged and suitable recovery methods are invoked to return the system to a
safe state.
A production line of semiconductor is a large scale and a complex system.
A control system of the line is considered to be difficult to control because there
exist lots of malfunctions such as maintenance of equipment, equipment break
down disturbance in the production of wafers in the semiconductor production
system. Fukuda et al have been exploited some methods and systems using
simulations or expert systems approach to solve these disturbances. The
semiconductor production systems had been large and complex and the
environments of the systems have been changing dynamically, so that it is hard to
exploit a perfect control system of semiconductor production by using only
conventional methods.
Research conducted by Ishiguro et al did focus on chemical and nuclear
plant. In these systems, once a certain device (unit) in a plant system becomes
faulty, its influence propagates through the whole system, and then causes a fatal
situation. To enhance safety and reliability of plant systems, an efficient fault
diagnosis technique is desired. On the other hand, biological systems such as
human beings can be said to be the ultimate information processing system, and
are expected to provide feasible ideas to engineering fields. Among the
information processing systems in biological systems, immune systems work as
on-line fault diagnosis systems by constructing large-scale networks, called
immune networks (idiotypic networks). In this study, the researchers tried to apply
these immune networks to fault diagnosis of plant systems, and the feasibility of
their proposed method is confirmed by simulations.
24
3.5 Pattern Recognition
Forrest et al described an immune system model based on binary strings.
The purpose of the model is to study the pattern recognition processes and learning
that take place at both individual and species levels in the immune system. Genetic
algorithm is a central component of their model. The paper reports simulation
experiments on two pattern recognition problems that are relevant to natural
immune systems. Finally, it reviews the relation between the model and explicit
fitness sharing techniques for genetic algorithms, showing that the immune system
model implements a form of implicit fitness sharing.
Dasgupta et al described a technique based on immunological principle, for
a novel pattern detection method. it is a probabilistic method that uses a negative
selection scheme, complement pattern space, to detect any change in the normal
behavior of monitored data patterns [1]. The technique is compared with a positive
selection approach, Implemented by an ART neural network, which uses the self
pattern apace for anomaly detection.
Hunt et al described an artificial immune system (AIS) which is based upon
models from the natural immune system. This natural system is an example of an
evolutionary learning mechanism which possesses a content addressable memory
and the ability to forget little used information. It is also an example of an adaptive
non-linear network in which control is decentralized and problem processing is
efficient and effective. As such, the immune system has the potential to offer novel
problem solving methods. The AIS is an example of a system developed around
the current understanding of the immune system. It illustrates how an artificial
immune system can capture the basic elements of the immune system and exhibit
some of its chief characteristics. They illustrate the potential of the AIS on a simple
pattern recognition problem. Then, they apply the AIS to a real world problem: the
recognition of promoters in DNA sequences. The results obtained are consistent
with other approaches, such as neural networks and are better than the nearest
25
neighbor algorithm. They concluded that the primary advantages of the AIS are
that it only requires positive examples, and the patterns it has learnt can be
explicitly examined. In addition, because it is self-organizing, it does not require
effort to optimize any system parameters.
Cooke et al have developed an artificial immune system AIS which is
based on the human immune system. The AIS possesses an adaptive learning
mechanism which enables antibodies to be used for classification tasks. In their
paper, they described how the AIS has been used to evolve antibodies which can
classify promoter containing and promoter negative DNA sequences. The DNA
sequences used for teaching were 57 nucleotides in length and contained
procaryotic promoters. Their system classified previously unseen DNA sequences
with an accuracy of approximately 90%.
3.6 Autonomous Agents
In recent years much attention has been focused on behavior-based
artificial intelligence, (Al) which has already demonstrated its robustness and
flexibility against dynamically changing world. Watanabe et al developed an
approach in which the followings problems have not yet been tackled:
1) How to construct an appropriate arbitration mechanism, and
2) How to prepare appropriate competence modules (behavior primitives). One of
the promising approaches to tackle the problems is a biologically inspired
approach. The Watanabe group focused on the immune system, since it is
dedicated to self-preservation under hostile environment, based on the fact that
autonomous mobile robots must cope with dynamically changing environment.
They constructed a new decentralized behavior arbitration mechanism inspired
by the biological immune system. Then, they applied it to the garbagecollecting problem of autonomous mobile robot that takes into account the
concept of self sufficiency. To verify the feasibility of their method, they
carried out some experiments using a real robot. In addition, they investigated
26
two types of adaptation mechanisms to construct an appropriate artificial
immune network without human intervention.
Immunized Computational Systems combine a priori knowledge with the
adapting capabilities of immune systems to provide a powerful alterative to
currently available techniques for intelligent control [8]. This was the basic idea
that Krishnakumar et al presented on various levels of intelligent control and relate
them to similar functioning in human immune systems. A technique for
implementing immunized computational systems as adaptive critics was presented
then applied to a flight path generator for level 2, non-linear, full-envelope,
intelligent aircraft control problem.
Conventional artificial intelligent (Al) systems have been criticized for their
brittleness under hostile /dynamic changing environments [6]. Therefore, recently
much attention has been focused on the reactive planning systems such as
behavior-based AI. However, in the behavior-based Al approaches, how to
construct a mechanism that realizes adequate arbitration among competence
modules is still an open question. Ishigura et al proposed a new decentralized
consensus-making system inspired from the biological immune system. They
applied their proposed method to a behavior arbitration of an autonomous mobile
robot as a practical example. To verify the feasibility of their method, we carry out
some simulations. In addition, they proposed an adaptation mechanism that can be
used to construct a suitable immune network for adequate action selection.
Lee et al proposed a method of cooperative control (T-cell modeling) and
selection of group behavior strategy (B -cell modeling) based on immune system in
distributed autonomous robotic system (DARS). The immune system is a living
body’s self protection and self-maintenance system. Thus these features can be
applied to decision making of optimal swarm behavior in dynamically changing
environment. For the purpose of applying immune system to DARS, a robot is
regarded as a B cell, each environmental condition as an antigen, a behavior
strategy as an antibody and control parameter as a T-cell respectively. The
executing process of the proposed method is as follows: When the environmental
27
condition changes, a robot selects an appropriate behavior strategy. Then, the
behavior strategy is stimulated and suppressed by other robot using
communication. Finally, much stimulated strategy is adopted as a swarm behavior
strategy. This control scheme is based on clonal selection and idiotopic network
hypothesis, and it is used for decision making of optimal swarm strategy. By TCell modeling, adaptation ability of robot is enhanced in dynamic environments.
Recently, strong demands of developing autonomous decentralized system
have been arisen since systems have been increasing in their scale and complexity.
On the other hand, biological system such as human beings can he said the ultimate
decentralized system, and is expected to provide feasible ideas to engineering
fields. Immune systems work as on-line fault diagnosis systems by constructing
self-non-self recognition networks. The aforementioned ideas were the base
prospect Ishiguro, and his group who tried to apply this immunological self-nonself recognition networks to a gait acquisition of 6-legged walking robot as a
practical example.
Meshref, and VanLandingham proposed a paper that applies an AIS technique
to a Distributed Autonomous Robotics System (DARS) problem. One of the
classic problems in DARS is the dog and sheep problem. In their paper they tried
to benefit from the features of the natural immune system in the development of
the dog and sheep problem. On the other hand, they found that Natural immune
systems are sophisticated information processors. They learn to recognize relevant
patterns, they remember patterns that have been seen previously, and they use
diversity to promote robustness. Furthermore, the individual cells and molecules
that comprise the immune system are distributed throughout the body, encoding
and controlling the system in parallel, with no central control mechanism. The
immune system uses several weapons to attack the foreign antigen. Abstractly,
these weapons are the helper T-cells, B-cells, and antibodies. We simulated the dog
as a B cell, the sheep as an antigen, the antibody as the dog behavior, the antigen
response as the sheep behavior, and the sheep-to-pen distance as a helper T cell.
The system interacts in an equivalent manner similar to the immune response
trying to restore the environment to its original state, which is the sheep inside the
pen.
28
CHAPTER 4
PROPOSITION OF ARTIFICIAL IMMUNE
CONTROLLER ALGORITHM
4.1 Introduction
The Biological immune system is a control system that has strong
robusticity and self-adaptability in complex disturbance and indeterminacy
environment [2]. The artificial immune algorithm has fundamental ability to
produce new types of antibody or to find the best fitted antibody which is able to
attack the antigen invading into the body. The principal function of the immune
system is to limit damage to the host organism by pathogens. Such organisms
generate an immune response, and are thus called antigens. Immune system has
fundamental ability to produce new types of antibody or to find the best fitted
antibody which able to attack the antigen invading into the body. Against the
unnumerable types of unknown antigen, the immune system produces a great many
types of antibody by trial and error. To realizes the diversity of antibody types is
essential adaptability against the foreign virus and bacteria in the environment.
Design of the controller aims to enhance the quality of the control system
and obtain requested control goal. It is the key for guaranteeing the quality and the
characteristic of the control system once the model of the object is determinate.
Therefore the design and the analysis of controller is a focal point which the whole
control domain pays attention to. There are two method for design traditional
controller: One is the classical control theory design method, including linear
29
method such as the method of the root-locus, the method of frequency domain, PID
adjustment and non-linear method such as phase plane, description function;
Another is the modern control theory design method, including the state feedback
controller, the auto-adapted adjustment controller, change the structure controller,
based on H. and so on. The design and realization of controller mentioned above
already had a series of relative more complete and strict theory methods, but still
some defaults left, For example, the object is often limited strictly to be linear, or
having been known at least. When the object is disturbed by the factors which
cannot be surveyed or cannot be estimated, the control capability will fall off
greatly.
4.2 Basic Varela Immune Network Model
The model that proposed by Varela and his confreres is called the second
generation network model. The model contains three important concepts: structure,
dynamics and metadynamics. The structure indicate the relation pattern of the each
part immune network. Usually the structure is expressed by the matrix. Dynamics
indicate the dynamic change of the density and affinity of immune factor. The
metadynamics indicate that the network composition may change. This change
denotes that new elements will appear in the network and old ones disappear at any
moment. The fundamental assumption of the BVINM is:
1) The BVINM only considered the B cell and the antibody produced by it. The
identical kind of the cell and the antibody are called the clone or the unique
feature. The antibody only can be produced by the mature B cell.
2) The effects of the different kinds of the clone are expressed by the matrix M.
The optional value of the matrix is 0 or 1.
3) The new B cells are produced and the old ones disappeared unceasingly. The
probability of the mature and the reproduction of the B cell depends on the
clone in the immune network.
30
The BVINM includes the two equations as follows:
`
Ti = –k1σiTi – k2Ti + k3M(σi)Bi
`
Bi = –k4 Bi + k5P(σi)Bi + k6
B
…………………(4.1)
In the formula, Ti expresses the quantity of the ith kind of the antibody. Bi
expresses the quantity of the ith kind of the B cell. The parameter k1 indicates the
mortality of the antibody which is caused by the antibodies interaction. K2
expresses the natural mortality of the antibody. K3 indicates the reproduction rate
of the antibody which is caused by the mature B cell. K4 expresses the mortality of
the B cell. K5 expresses the reproduction rate of the B cell which is caused by the
B cell itself. K6 expresses the new reproduction rate of the B cell which is caused
by the marrow. M(σi) is the mature function of the Bi cell.
P(σi) is the
reproduction function of which the Bi cells reproduce the Ti antibodies. The mature
function and the reproduction function have the "bell" function which is shown in
Figure 4.1.
Figure 4.1 : M(σI) is the mature function of the Bi cell. P(σi) is the reproduction
function of which the Bi cells reproduce the Ti antibodies. The mature function
M(σ) and the reproduction function P(σ) have the "bell" function.
σi express the network sensitivity of the ith kind of clone :
σi = j=1Σn mi,j Tj
…………………(4.2)
mi,j denotes the Boolean value of the affinity between ith and jth clone in the
formula. The Boolean value is 1 when the affinity exists, and the value is 0 when
the affinity disappears. n is the type of i B cell and i T antibody, i=1,2,... ... n. The
“bell" function implies the basic fact of the biological immune process: Insufficient
31
or the superfluous sensitivity can suppress the B cells’ reproduction and capability
of which B cells produce T antibody. The formula (1) and (2) denote the dynamic
process of the interaction between the B cell and the antibody in the biological
immune process to some extent. If the formula (1) and (2) are be used for
designing the immune controller in the control system, they have some
insufficiencies:
1) BVINM haven’t reflected the infection that antigen act on immune network,
which is adverse for the VINM transform to the controller model, for the
system error is often considered as the antigen when design the immune control
system. One of the final control effects is to eliminate or reduce the error of
control system as far as possible.
2) Formula (1) describes that the Bi cell can only promote the Ti antibody. In the
fact, B cell can excrete many kinds of immune antibodies. The reproduction of
the i B cell mainly depends on the B cell itself and the marrow. Moreover the
reproduction of the Bi cell radically is elicited by the antigen which have
intruded organism. (To be concise, the other factors are not considered).
4.3 Improved Varela Immune Network Model
After the antigen invaded organism, the organism had two different kinds
of responses. One is the self duplication of antigen. Another is the elimination of
the antigen caused by the phagocyte and the killing cell. That can be described
with the under dynamic equation [7]:
Ági = q`Ag – H(Ti)Agi …………………(4.3)
Among them, q' denote the reproduction rate of the antigen when the immune
process doesn’t exist, H(Ti) is the function of which antigen is eliminated by
antibody. H(Ti) can be shown as follows:
H(Ti) = h + KeTi …………………(4.4)
32
h denote the rate of the non-special killing. Ke denote the approximate rate of
antigen’s being specially eliminated. Take (4.4) into (4.3):
Ági = q`Ag – KeTiAgi …………………(4.5)
in the formula (4.5), q expresses the rate of the antigen reproduction. Ke expresses
the rate of antigen’s being eliminated. We suppose that the elimination rate of the
antigen mainly depends on the probability of the antibody meeting and uniting
with the antigen, while the probability is determined by the quantity of the
antibody and the antigen. The product of the antibody quantity and the antigen
quantity is use for expressing the probability of the antibody and the antigen
meeting each other [4,7], that is TiAgi . Considering (4.1) and (4.5), we can obtain
the IVINM as follows:
Ági = q`Ag – KeTiAgi
`
Ti = –k1σiTi – k2Ti + k3M(σi)Bi
`
Bi = –k4 Bi + k5P(σi)Bi + k6 + KAgAgi
B
…………………(4.6)
Ag , q and Ke in the formula (4.6) is the same as in the formula (4.5). And M(σi),
P(σi), σi in the formula (4.6) is the same as in the formula (4.1) and (4.2). Ag K
denotes the B cell reproduction rate which is caused by the antigen.
4.4 Design and Analysis of Immune Controller
We need a SISO controller for SISO system, and the formula (4.6) can be
shown as follows:
Ági = q`Ag – KeTAgi
`
Ti = –k1σiT – k2T + k3M(σ)B
`
Bi = –k4 B + k5P(σ)B + k6 + KAgAg
B
…………………(4.7)
When we use the IVINM (4.7) for constructing new immune controller, we
must clarify which are similar or heuristic between this IVINM and the control
system, we also must clarify which are different that needs to be improved. The
similarity is shown as follows: the first is that the IVINM (4.7) describe the
immune process between the B cell and the antibody after the antigen invaded
33
organism. That is similar to the relation of the error variable and the control
variable in control system, when the error e(t) replace the antigen Ag and the
control u(t) replace the B cell. The second is the IVINM (4.7) describes the
dynamic process between the B cell and the antibody. The B cell is the important
cell in recognizing and memorizing antigen as well as in secreting antibody. It is
already proved that the plasma-cell created by the B cell was one of the important
reasons why the immune system has the memory characteristic in the medicine.
Dissimilarity is shown as follows:
1) The changing rate of the antigen intruded organism is composed of the antigen
self-duplication and the rate of antigen being killed by the antibody in the
IVINM. The error of the control system cannot be divided into two parts like
that, for the error of the control system is unable to self-duplicate. Moreover
the control error relates widely to the object model, the external disturbance,
the control input as well as the controller model and so on. Therefore only the
rate of antigen being killed is considered in this paper.
2) The quantity of the antigen, the antibody and the B cell each is certainly bigger
than zero in biological immune system. But the error and control quantity may
be positive or negative in the control system. So it is necessary that the IVINM
should be improved and simplified for making the formula of the immune
controller.
We abandon the antigen self-duplication item in formula (4.7) basing on the
condition (1). Considering the condition (2), the function of the mature M(σ) as
follows is taken:
M(σ) = Km(ep1│σ│ – ep2│σ│)●sign(σ) …………………(4.8)
Km is a constant and Km > 0. in the formula (4.8). p2 and p1 are the constants and p2
< p1 < 0.sign(σ) is the mark function. The formula (4.8) is appropriate for the
error that may be positive or negative in the control system. The formula (4.8) has
the curve which is shown in Figure 4.2, it’s upper part of the curve is similar to the
34
"bell" shape in Figure 4.1. The reproduction function P(σ) also adopts formula
(4.8).
Figure 4.2 : The M(σ) and P(σ) curve of actual control system. They were both
expressed by formula (4.8).
We combine the first item with the second one in the formula (4.7). e(t) replace Ag
, u(t) replace B. We can obtain the control model based on the IVINM as follows:
ė(t) = –KeT(t)e(t)
`
T(t) = –kTT(t) + K3M(σ)u(t)
ù(t) = –k4U(t) + k5P(σ)U(t) + k6 + KAge(t)
…………………(4.9)
kT = k1σ + k2. It is too complex that the formula (4.9) is used for the controller.
Therefore the formula (4.9) will be further simplified: 1) because T k is too small,
we suppose kT = 0 2) we neglect the self-duplication item in the equation (4.9); 3)
we suppose the independent variable of M(σ) is u(t) ,that is M(u) . We suppose the
independent variable of P(σ) is u(t) & , that is P(u) & . 4) Get one order derivative
of the third formula in formulary (9), and omit the complex non-linear item, and
take the first formula into the third formula, and then we obtain the formula (4.10)
as follows:
`
T(t) = k3M(e(t)U(t)
ü(t) = – (k4 – k5P(e(t)))ù(t) – kT(t)e(t)
…………………(4.10)
k = KAgKe, the formula (4.10) is an immune controller model based on the IVINM
in this thesis. This immune controller model is called an improved Varela immune
network controller (IVINC).The structure of the IVINC is shown in Figure 4.3.
35
The IVINC shown in Figure 4.3 is a non-linear controller. It has the characteristics
as follows:
Figure 4.3 A biological immune controller based on improved Varela immune
network , e(t) is an error of the control system, and it is an equivalence of the Ag
antibody in the biological immune system. u(t) is the output of the immune
controller, it is an equivalence of the B cell concentration in the biological immune
system. The waves of M(•) function and P(•) function are shown in Figure 4.2.
1) If k4 and k5 P(u) are chosen reasonably, the inner feedback in the IVINC brings
the positive feedback when u(t) belongs to the appropriate spectrum. When the
inner feedback is the positive feedback, u(t) will increase fast, and the larger
u(t) will cause the negative feedback. That accord with the biological immune
feedback mechanism (the Ding immortal article).
2) When the choosing of parameter is reasonable, it can be ensured that if the
error e(t)<>0 then T (t)<>0 at the same time, so the larger T (t) is good for the
controller to response more sensitively to the small error e(t). When the control
system repeatedly respond to the series of same input signal, the second
reaction speed of the system will be accelerated due to increasing the antibody
density. That accords with the memory mechanism of the biological immune
response.
36
3) When u(t) is too large, k3 M(u)u(t) approximate to zero due to M(u) . That is
quite effective to control the excessively increasing of T(t). If KT takes a very
small value in the formula (9), that is advantageous to the system stability, but
that also will sacrifice the memory characteristic of the immune response to
some extent.
4.5 Sample of Simulation Result
The structure of the control system which includes the IVINC is shown in Figure
4.4.
1
s
Integrator2
k
Pulse
Generator
Gain1
Product
-KGain3
cos
Trigonometric
Function
1
1
s
1
s
0.1s+1
Integrator
Integrator1
Transfer Fcn
G(s)
f(e,u)
Scope1
-KGain4
0
Gain5
Figure 4.4 : Artificial immune control system structure. f(e,u) is an immune
controller, while G(s) is an object controlled by the immune controller.
The demonstration of the simulation is to control the object with the following
parameters, k = 0.7, k3 = 10, k4 = 50 and k5 = 0. The input to the system is pulse
generator with amplitude of 1 as shown in Figure 4.5 and the object to be
controlled is in first order which was given in the equation below.
37
2
1.5
1
0.5
0
-0.5
-1
0
100
200
300
400
500
Figure 4.5 : Pulse Generator Input to the System
The output of the simulation result is shown in Figure 4.6. The simulation shows
that the output of the system follows of the input of the pulse generator and
maintain it steady state response. Moreover, it is observed from the simulation that
the IVINC ensure the control system to track the constant input with the error
approximating to zero.
G ( s) =
1
0.1S + 1
38
2
1.5
1
0.5
0
-0.5
-1
0
100
200
300
400
500
Figure 4.6 : Simulation result of the output system response
4.6 Analysis of IVINC parameters
Figure 4.7 shows that when k3 = 50 the output seems to overshoot and does not
follow the shape of the input of the pulse generator. This can be correct by
reducing the value of gain k as shown in figure 4.8. When k=0.25 the simulation
output that overshoot tend to reduce. Observed from the simulation result that
when k3 is high k must be small or when k is high k3 must be small in order to
produce a stable output response.
39
2
1.5
1
0.5
0
-0.5
-1
0
100
200
300
400
500
600
700
800
900
Figure 4.7 : Simulation result of the output system response when k3=50
2
1.5
1
0.5
0
-0.5
-1
0
100
200
300
400
500
600
700
Figure 4.8 : Simulation result of the output system response when k=0.25
40
CHAPTER 5
POWER SYSTEMS STABILIZER BY AIS
5.1 Introduction
Improvement of power system stability by controlling the field excitation
of a synchronous generator has been an important topic of investigation since
1940s. Introduction of the high-gain, continuously acting automatic voltage
regulator (AVR) helps improve the dynamic limits of power networks [10].
However, AVRs could also introduce negative damping, particularly in large,
weakly coupled systems, and consequently make the system unstable. To
overcome this problem, supplementary stabilizing signals are introduced in the
excitation system. The supplementary stabilizing signal enhances system damping
by producing a torque in phase with the speed of the synchronous generator. In a
conventional arrangement, the stabilizing signal is usually derived by processing
any one of a number of possible signals, e.g., speed, acceleration, power, or
frequency, through a suitable phase lag/lead circuit, called the power systems
stabilizer (PSS), to obtain the desired phase relationship. The output of the PSS,
i.e. the stabilizing signal, is introduced into the excitation system at the input to the
AVR/exciter along with the voltage error. The effectiveness of damping produced
by the excitation control has been demonstrated by simulation, field and tests. The
evolution and development of the PSS from fixed parameter analog type to that
using adaptive control and artificial-intelligence (AI) based algorithms have been
made consequently and much progress and improvement have been achieved.
Therefore a new method of stabilizing signals and enhance system damping will be
41
proposed in this thesis. The proposed is called the application of artificial immune
system in designing the power system stabilizer by implementing a suitable
artificial immune algorithm to the PSS.
5.2 Fixed Parameter Controllers
A common feature of the fixed parameter controllers is that their design is
done off-line. Using the state and or output feedback. Optimal structure and gains
of the controller that minimize a certain performance index or meet design
specifications are determined. Various approaches proposed to design fixed
parameter PSS are extensively reported in the relevant literature.
5.3 Conventional PSS
A conventional PSS (CPSS) is based on the use of a linear transfer function
designed by applying the linear control theory to the system model linearized at a
preassigned operating point. An IEEE type PSS1A CPSS has a transfer function of
It contains a network to compensate for the phase difference from the excitation
controller input to the damping torque output, i.e., the gain and phase
characteristics of the excitation system, the generator, and the power system, which
collectively determine the open-loop transfer function. Algorithms are available to
calculate the parameters of the CPSS [10].
By appropriate tuning the phase and gain characteristics of the
compensation network during the simulation studies at the design stage and further
during commissioning, it is possible to set the desired damping ration. Various
42
tuning techniques have been introduced to effectively tune the CPSS parameters.
CPSSs are in wide use, and they have played an important role in improving the
dynamic stability of power systems.
The CPSS is designed for a particular operating point for which the
linearized model of the generator is obtained. Power systems are non-linear and
operate over a wide range. For example, the gain of the plant increases with
generator load. Also, the phase lag of the plant increases as the AC system
becomes stronger. Due to non-linear characteristics, wide operating conditions, and
unpredictability of perturbations in a power system, the CPSS, a linear controller,
generally cannot maintain the same quality of performance under all conditions of
operation.
The parameter setting of a CPSS is a compromise that provides acceptable,
though not minimal, performance over the full range of operating conditions.
Figure 5.1 shows a synchronous generator connected to a constant voltage bus
through a double circuit transmission line using supplementary excitation control
signal such as APSS, FLPSS, and NFPSS [11]. The power system supply consists
of numerous machines, lines and loads showing the elements of a model of a
mechanical generator. The components of the power system that influence the
electrical and mechanical torques of the machines are:
Figure 5.1 System Model Used In the PSS Simulation
43
1) The network.
2) The loads and their characteristics.
3) The parameters of the synchronous machines.
4) The excitation systems of the synchronous machines.
5) The mechanical turbine and speed governor.
6) Other important components of the power plant.
7) Other supplementary control.
There are two types of models of the conventional Power Systems Stabilizer used
in this simulation analysis, namely they are the delta w PSS and Multi Band PSS.
The details of these controllers are discussed in the next chapter.
5.4 Artificial Immune System PSS
The artificial Immune System method is a new method to be implemented
in the Power Systems Stabilizer. The idea of AIS derived from biological
vertebrate immune system. Based on the immune system network some
mathematical immune algorithm is obtained. The mathematical model of the
immune algorithm is used to develop an artificial immune controller. The
properties of the immune algorithm required to develop immune controller must
have the quality of the control system to obtain requested control goals.
The model of the artificial immune controller proposed is the Varela
Immune Network which was developed by Varela and his confreres. The
mathematical immune algorithm and fundamental assumption of Varela Immune
Network is :
`
Ti = –k1σiTi – k2Ti + k3M(σi)Bi ............................... (5.1)
Bi = –k4 Bi + k5P(σi)Bi + k6 ............................... (5.2)
`
B
44
However the Varela Immune Network is not merely perfect. The Varela Immune
Network only considers the B cell and the antibody produced by itself. Some
improvement has been made to the Varela Immune Network. The improved Varela
Immune Network model has been presented by appending the antigen. The
Improved Varela Immune Network consist of additional mathematical immune
algorithm which is the production of antigen given by the equation of :
Ági = q`Ag – KeTiAgi
............................... (5.3)
The immune controller can be constructed using the above equation. When
constructing the immune controller we must clarify which are similar or heuristic
between this IVINM and the control system. Combine the first item (5.1) with the
second one (5.2) then e(t) replace Ag, u(t) replace B. We can obtain the control
model based on IVINM as follows:
ė(t) = –KeT(t)e(t)
`
T(t) = –kTT(t) + K3M(σ)u(t)
ù(t) = –k4U(t) + k5P(σ)U(t) + k6 + KAge(t)
………………. (5.4)
The above equation and then can be simplified by derivation one order of the third
formula in equation (5.4) and take the first formula into the third formula and then
we obtain the formula as follows:
`
T(t) = k3M(e(t)U(t)
ü(t) = – (k4 – k5P(e(t)))ù(t) – kT(t)e(t)
…………………(5.5)
The formula (5.5) is an immune controller model base don the IVINM. This
immune controller model is called an improved Varela immune network controller
(IVINC). The structure and the configuration of the transfer function of the IVINC
are shown in Figure 5.2.
45
Figure 5.2: Improved Varela Immune Network Controller From the formula (5.5)
shown above it can be learned that when “ù” whose equivalent is the reproductivity
of B cell is too little or too large, (k4 – k5P(ù))>0, the inner feedback of the system
is negative feedback. Only when “ù” changes in a special range, (k4 – k5P(ù))<0,
the inner feedback of the system is positive.
The simplicity of the IVINC controller concept makes it easy to implement
into the power systems stabilizer. Some of the major features of the IVINC are this
method does not require the exact mathematical model of the plant or the object to
be control. IVINC offers ways to implement simple but robust solutions that cover
a wide range of system parameters and can cope with major disturbances and fault.
The control strategy of the IVINC controller mimics the vertebrate biological
immune defense system.
The implementation of IVINC controller into the power systems stabilizer
is shown in Figure 5.3. The input of the IVINC controller can be the speed
deviation with respect to nominal (dw in pu) or the power acceleration with respect
to nominal (pa=pm-pe) as shown in the figure below. The parameters of the IVINC
controller can be changed by clicking on the IVINC subsystem. The method to
obtain a steady state output response in this analysis is trial and error. Finding the
right parameters of the IVINC controller can be time consuming. A global
optimization method can be used to help in the optimization and tuning of the
IVINC controller.
46
Figure 5.3: Implementation of IVINC into the PSS
5.5 The Two Area Test Systems
The test system consists of two fully symmetrical areas linked together by
two 230 kV lines of 220 km length as shown in Figure 5.4. These areas are area 1
which extends 110 km of line 1a connected to breaker Brk1 and area 2 which
extends 110 km of line 1b connected to breaker Brk2 respectively. The test system
is specially designed to study low frequency electromechanical oscillations in large
interconnected power systems [14,15]. Despite its small size, it mimics very
closely the behavior of typical systems in actual operations.
47
Phasors
413 MW
---->
A
aA
B
C
Area 1
bB
cC
A
a
A
a
B
b
B
b
C
c
C
c
Brk1
Line 1a
(110 km)
B1
A
Line 1b
(110 km)
Brk2
aA
A
bB
B
cC
C
B2
Area 2
B
C
Fault
Line 2
(220 km)
d_theta
w
Pa
Vt
d_theta v s M4 (deg)
Vps
w (pu)
P_B1->B2
Pa (pu)
Vt(pu)
Machines
Stop
Machine
Signals
Pos. Seq.
V_B1 & V_B2 (pu)
STOP
System
Data
Activ e Power f rom
B1 to B2 (MW)
System
Select a specific PSS model by typing:
0 (IVINC PSS)
1 (MB-PSS)
2 (Delta w PSS from Kundur)
3 (Delta Pa PSS)
yellow=M1, magenta=M2,
cyan=M3, red=M4
Stop Simulation
if loss of synchronism
Figure 5.4: Test Area System
Inside areas 1 and 2, each is equipped with two identical round rotor
generators rated 20 kV/900MVA as shown in Figure 5.5. These generators are
named generators 1 and 2 inside area 1 and generators 3 and 4 inside area 2
respectively. The synchronous machines for each of the generators have identical
parameters [14,15]. Thermal plants having identical speed regulators are further
assumed at all location. The load is represented as constant impedances and split
between the areas in such a way that area 1 is exporting 413MW to area 2.
48
Area 1 / Area 2
A
A
a
B
B
b
C
C
c
T1: 900MVA
20 kV-230 kV
M1 900 MVA
Pm
Vf
A
B
C
25km Area
0.7778
Vref
Pref1
1
2
3
A
B
C
10 km Area 1
m
PSS
Pm
Vf _
A
A
a
B
B
b
C
C
c
M2 900 MVA
1.0+.05
Vref
967MW
100MVAR
-187MVAR
-200MVAR
T2: 900MVA
20 kV/230 kV
m
1.0
Pm
0.7777
Pref
0
Timer
A
B
C
m
Pref
M1: Turbine &
Regulators
Vref1
A
B
C
A
B
C
1
A
B
C
Vf _
==> to Bus B1
m
A
B
C
Pm
PSS model1
0 = IVINC PSS
1 = MB-PSS
2 = Delta w (Kundur)
3 = Delta Pa
Vref
1
PSS
Vref2
Vf
Pref2
M2: Turbine &
Regulators
2
PSS model
Figure 5.5: Generator 1 and 2 Inside Area 1 and Generator 3 and 4 Inside Area 2
Inside the generators are PSS controllers. Namely these controllers are
IVINC PSS, Multi Band PSS, Delta w PSS and Delta Pa PSS controllers as shown
in Figure 5.6 below. The input to the controllers is the synchronous machine speed
deviation with respect to nominal (dw in pu) or the electrical power with respect to
nominal (Pm-Pe in pu). The type of the controller to be used in the PSS test system
can be selected by inserting the respective number of the PSS model as shown in
Figure 2.4 above. The parameters of the PSS controller can be easily changed by
double clicking on the respective PSS models. Combination of different PSS
controllers in the test system area can be achieved by inserting the model of the
PSS controller. The simulation results can be observed by clicking on the system
scopes on the main diagram as shown in Figure 5.4 above.
49
wref
MACHINE 1
1
wref
dw_5-2
Pref
Tr5-2
wm
gate
v s_qd
2
d_theta
Pref
wm
1
m
Pe
m
d_theta
1
Pm
dw
STG
[theta1]
theta
[Pe1]
Machine 1
Measurement
Demux
[w1]
4
3
1
PSS
Pm
v ref
[EFD1]
Vref
vd
em
2
Vf
vq
In
Vf
Out
v stab
IVINC PSS
dw
[pss1]
EXCITATION
Vstab
Re
MB-PSS
In
Im
Vstab
Delta w PSS
(Kundur)
Pm-Pe
In
Vstab
Delta Pa PSS
[Pa1]
Figure 5.6: PSS Controllers
|u|
[Vt1]
50
5.6 Result and Analysis
The test system has been explained in the previous chapter. In this chapter
analysis and comparison of the controllers will be presented using the simulation
results obtained from the system scopes. The simulation results to be compared are
the speed deviation, power acceleration and difference of dw etc in this modal
analysis. A modal analysis of acceleration powers of the four machines shows
three dominant modes:
1) An inter-area mode involving the whole area 1 against area 2.
2) Local mode of area 1, involved in this area is generators 1 against 2.
3) Local mode of area 2, involved in this area is generators 3 and 4.
The two local modes and the inter-area mode are the three fundamental modes of
oscillation of the two test area systems. They are due to the electromechanical
torques which keeps the generators in synchronism. The frequency of the
oscillations depends on the strength of the system and on the moments of inertia of
the generators.
Transient simulation is normally carried out to investigate whether or not
an interconnected power system can survive a fault. To round of this examination
of power system oscillation the test area of line 1a and 1b are changed respectively.
The tie voltage response will be analyzed using several combinations and a pair of
IVINC controller with other conventional controller which are shown in every
case. The analysis of the two test area system is to simulate the system output
response in interconnected power systems. By looking of several combinations of
controllers and with different line length, the analysis shows examples of different
types of oscillations that can occur. The analysis is to perform a considerable
number at 20 seconds nonlinear simulations. It is apparent that in larger systems
the use of transient simulation for the analysis of system oscillation could be very
time consuming. To study inter-area oscillations, it is often necessary to run
simulations of larger than 10 seconds [13]. The analysis conducted in this
simulation is all set to 20 seconds.
51
5.6.1 Delta w PSS Controller
The input to the Delta W PSS is the synchronous machine speed deviation
with respect to nominal (dw in pu). The block diagram of Delta w PSS is shown in
Figure 5.7. The test conducted is with a line distance from area 1 to area 2 as 220
km and both are in equal length with 110 km each. Using the parameters shown in
Table 5.5.1 below, the output of the system shows that the generator has made it
steady state at approximately 6.5 seconds as shown in
Figure 5.8. Generators 1
and 2 are both identical in area 1, while in area 2 generators 3 and 4 are also
identical to each other. The parameters for the Delta w PSS shown below is the
ideal setting which shows good result of the system output response. These
parameters will be used for the analysis and comparison with the IVINC_PSS
controller.
Parameter
Sensor
1500
Overall Gain 30
Wash-out
10
Lead-lag#1
5000,2000
Lead-lag#2
3,5.4
limiter
-0.15,0.15
Table 5.5.1: Parameter of Delta w PSS
Figure 5.7: Delta W Controller
52
Fault distance is set equally 110km at area1 of line 1a and 110km of area2 line 1b.
pu
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0.999
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.8a) : Generator 1 Delta w PSS
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
Figure 5.8b) : Generator 2 Delta w PSS
53
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.8c) : Generator 3 Delta w PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.8d) : Generator 4 Delta w PSS
Figure 5.8: a), b), c) and d)
54
The next analysis of the Delta W PSS is to conduct a system test where the
length distance of areas 1 and 2 are changed. The length of area 1 is changed to 50
km of line 1a, while area 2 to 170 km of line 1b. The distance between areas 1 and
2 still maintained at 220km. These are to test the system against fault. The fault
will be detected near to area 1. The simulation result shows that the system is
stable and the generator is able to recover from the fault at about approximately 6
seconds as shown in Figure 5.9.
A further test is conducted by changing the distance of area 1 in length of
170 km of line 1a and area 2 in length of 50 km of line 1b. This time the fault
location is changed the other way around while the distance among them is still at
220 km. The simulation result shows that the system was unable to recover from
the fault. When the distance of the fault is far away from area 1 the system is
unable to reach steady state. The analysis stops at approximately 2.8 seconds as
shown in Figure 5.10.
55
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.9a) : Generator 1 Delta w PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.9b) : Generator 2 Delta w PSS
56
pu
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.9c) : Generator 3 Delta w PSS
pu
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.9d) : Generator 4 Delta w PSS
Figure 5.9: a), b), c) and d)
57
Changing the fault distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.10a) : Generator 1 Delta w PSS
pu
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
Figure 5.10b) : Generator 2 Delta w PSS
58
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.10c) : Generator 3 Delta w PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
2
Figure 5.10d) : Generator 4 Delta w PSS
Figure 5.10 : a), b), c) and d)
59
5.6.2 Multi Band_PSS Controller
The input to the Multi Band PSS is the synchronous machine speed
deviation with respect to nominal (dw in pu). The structure and block diagram of
Multi band PSS is shown in Figure 5.11. The conducted is with a line distance
from areas 1 to 2 as 220 km and both are in equal length with 110 km each. Using
the parameters show in Table 5.5.2 below the output of the system shows that the
generator has made its steady state at approximately 8 seconds. These are shown in
Figure 5.12. Generators 1 and 2, are both identical in area 1 while in area 2,
generators 3 and 4 are also identical with each other. The parameters for the Multi
Band PSS shown below are the ideal setting for the analysis and comparison with
the IVINC_PSS controller.
Parameter
Global Gain
1.0
Low Frequency Band [FL,KL]
[0.2,30]
Intermediate Frequency Band [FI,KI]
[1.25,40]
High Frequency Band [FH,KH]
[12.0160]
Signal Limits [Vlmax,Vimax,Vhmax,Vsmax]
[0.075,0.15,0.15,0.15]
Table 5.5.2: Parameter of Multi Band PSS
Multi-band Power System Stabilizer
(IEEE Type PSS4b)
Innocent Kamwa, Robert Grondin
IREQ, Hydro-Quebec
Detailed
On
KL1
TL1.s+KL11
LFpos
LFpos
KL
TL2.s+1
LFneg LFneg
Kg
Transfer function
for detailed model
(LF)
-1.759e-3s+1
1.2739e-4s2 +1.7823e-2s+1
LF and IF speed sensor
KL2
TL7.s+KL17
TL8.s+1
1
dw
On
KI1
TI1.s+KI11
IFpos
IFpos
KI
TI2.s+1
IFneg LFneg
Transfer function
for detailed model
(IF)
KI2
TI7.s+KI17
TI8.s+1
On
KH1
TH1.s+KH11
HFpos
HFpos
TH2.s+1
KH
HFneg HFneg
Transfer function
for detailed model
(HF)
80s2
s3 +82s2 +161s+80
HF speed sensor
KH2
1
VS
(for simplified
settings only)
TH7.s+KH17
TH8.s+1
Note: Speed deviation is derived from the machine positive-sequence terminal voltages and currents.
Low and intermediate speed deviation is based on the internal voltage phasors while
the high frequency speed deviation is based on the electrical power.
Figure 5.11: Multi Band Controller
60
Fault distance is set equally 110km at area1 of line 1a and 110km of area2 line 1b.
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.12a) : Generator 1 MB_PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
Figure 5.12b) : Generator 2 MB_PSS
61
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.12c) : Generator 3 MB PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
2
4
6
8
10
12
14
Figure 5.12d) : Generator 4 MB_PSS
Figure 5.12: a), b), c) and d)
62
The next analysis of the Multi Band PSS is to conduct a system test where
the length distance of areas 1 and 2 are changed. The length of area 1 is changed to
50 km while area 2 to 170 km. The distance between area 1 and 2 are still
maintained at 220km. This is to test the system against fault. The fault will be
detected near to area 1. The simulation result shows that the system is stable and
the generator is able to recover from the fault at approximately 11 seconds as
shown in Figure 5.13.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time, the fault location is changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
Similar to Delta W PSS the simulation result shows that the system is unable to
recover from the fault. When the distance of the fault is far away from area 1 the
system is unable to reach it steady state. The analysis stops at approximately 2.6
seconds as shown in Figure 5.14.
63
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.13a) : Generator 1 MB_PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.13b) : Generator 2 MB_PSS
64
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.13c) : Generator 3 MB_PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.13d) : Generator 4 MB_PSS
Figure 5.13: a), b), c) and d)
65
Changing the fault distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.14a) : Generator 1 MB_PSS
pu
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
0.5
1
1.5
2
Figure 5.14b) : Generator 2 MB_PSS
66
pu
1.005
1
0.995
0.99
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.14c) : Generator 3 MB_PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0
0.5
1
1.5
2
Figure 5.14d) : Generator 4 MB_PSS
Figure 5.14: a), b), c) and d)
67
5.6.3 Comparison IVINC PSS with Delta w PSS
Case 1
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Delta w PSS
Area 2
Generator 3 =Delta w PSS
Generator 4 =Delta w PSS
The input to the IVINC PSS is speed deviation, dw in pu.
The IVINC PSS is set with the following parameters:
K=5
K3=100
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine speed deviation
with respect to nominal (dw in pu). The test consist of IVINC PSS paired with
Delta w PSS in area 1. The test will be conducted with the line distance from area
1 to area 2 is 220 km and both are in equal length with 110 km each. Using the
parameters above the output of the system shows that the generator has found its
steady state at approximately 10 seconds. These are shown in
Figure 5.15.
Generators 1 and 2, are both identical in area 1 while in area 2, generators 3 and
4 are also identical to each other.
Table 5.5.3 below shows the range of the parameters gain k and k3. Given
the value of gain k the minimum and maximum parameter of gain k3
obtained
within the range of the output response are in stable condition. Beyond the range of
minimum and maximum value of k3 will produce an unstable system.
Gain k min =1.5
Gain k max = 20
Gain k3 min
Gain k3 max
50
500
5
50
Table 5.5.3: Range of Gain K and Gain K3
68
Case_1a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.15a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
Figure 5.15b) : Generator 2 Delta w PSS
69
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.15c) : Generator 3 Delta w PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.15d) : Generator 4 Delta w PSS
Figure 5.15: a), b), c) and d)
70
-3
1.5
x 10
1
pu
0.5
0
-0.5
-1
-1.5
0
2
4
6
8
10
12
14
16
18
Figure 5.16: Speed deviation Difference of Gen 1 and Gen 2
Figure 5.16 shows the difference of speed deviation between Generator 1 of
IVINC PSS and Generator 2 of Delta w PSS. The plotted graph shows that the
speed deviation difference is 0 at approximately 16 seconds. The maximum peak
difference is approximately 0.0015 pu at 1.6 seconds.
20
71
The next analysis of the paired IVINC PSS and Delta w PSS in area 1 is to
conduct a system test where the length distance of areas 1 and 2 is changed. The
length of area 1 is changed to 50 km while area 2 to 170 km. The distance between
area 1 and 2 is still maintained at 220km. This is to test the system against fault.
The fault will be detected near to area 1. The simulation result shows that the
system is stable and the generator is able to recover from the fault at about
approximately 6 seconds as shown in Figure 5.17.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location is changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
Similar to Delta W PSS and MB_PSS the simulation result shows that the system
is unable to recover from the fault. When the distance of the fault is far away from
area 1 the system was unable to reach its steady state. The analysis stops at
approximately 2.5 seconds as shown in Figure 5.18.
72
Case_1b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.17a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.17b) : Generator 2 Delta w PSS
73
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.17c) : Generator 3 Delta w PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.17d) : Generator 4 Delta w PSS
Figure 5.17: a), b), c) and d)
74
Case_1c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.18a) : Generator 1 IVINC PSS
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
Figure 5.18b) : Generator 2 Delta w PSS
75
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.18c) : Generator 3 Delta w PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
2
Figure 5.18d) : Generator 4 Delta w PSS
Figure 5.18: a), b), c) and d)
76
Case 2
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Delta w PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 =Delta w PSS
The input to the IVINC PSS is speed deviation, dw in pu.
The IVINC PSS is set with the following parameters:
K=5
K3=100
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine speed deviation
with respect to nominal (dw in pu). The test consists of IVINC PSS paired with
Delta w PSS in area 1 and area 2 respectively. The test is conducted with the line
distance from area 1 to area 2 as 220 km and both are in equal length with 110 km
each. Using the parameters above the output of the system shows that the generator
has found it steady state at approximately 10 seconds as shown in Figure 5.19.
Generators 1 and 2, are both identical in area 1 while in area 2, generators 3 and
4 are also identical to each other with a very small difference of speed deviation
dw.
77
Case_2a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0.999
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.19a) : Generator 1 IVINC PSS
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
Figure 5.19b) : Generator 2 Delta w PSS
78
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.19c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.19d) : Generator 4 Delta w PSS
Figure 5.19: a), b), c) and d)
79
-3
1.5
x 10
1
pu
0.5
0
-0.5
-1
0
2
4
6
8
10
12
14
16
18
Figure 5.20: Speed deviation Difference of Gen 1 and Gen 2
Figure 5.20 shows the difference of speed deviation between Generators 1
of IVINC PSS and
2 of Delta w PSS. The plotted graph shows that the speed
deviation difference is 0 at approximately 14 seconds. The maximum peak
difference is approximately 0.00148 pu at 1.6 seconds.
20
80
The next analysis of the paired IVINC PSS and Delta w PSS in area 1 and
area 2 is to conduct a system test where the length distance of area 1 and area 2
were changed. The length of area 1 is change to 50 km while area 2 to 170 km. The
distance between area 1 and 2 are still maintained as 220km. This is to test the
system against fault. The fault will be detected near to area 1. The simulation result
shows that the system is stable and the generator is able to recover from the fault at
approximately 7 seconds as shown in Figure 5.21.
A further test is conducted by changing the distance of areas 1 in length of
170 km and 2 in length of 50 km. This time the fault location is changed the other
way around while still maintaining the distance of area 1 and 2 as 220 km. From
the graph of Figure 5.22, unlike the previous analysis the test shows that the
system is able to recover from the fault and has obtained stable system. The system
reaches it steady state at approximately 12 seconds. The IVINC PSS and Delta w
PSS paired in areas 1 and
2 is a good combination of reaching steady state and
stable output response when the line distance is changed both ways.
81
Case_2b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b. pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.21a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.21b) : Generator 2 Delta w PSS
82
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.21c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
Figure 5.21d) : Generator 4 Delta w PSS
Figure 5.21: a), b), c) and d)
83
Case_2c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0.99
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.22a) : Generator 1 IVINC PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0.99
0
2
4
6
8
10
12
14
Figure 5.22b) : Generator 2 Delta w PSS
84
pu
1.01
1.005
1
0.995
0.99
0.985
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.22c) : Generator 3 IVINC PSS
pu
1.01
1.005
1
0.995
0.99
0.985
0
2
4
6
8
10
12
14
Figure 5.22d) : Generator 4 Delta w PSS
Figure 5.22: a), b), c) and d)
85
5.6.4 Comparison IVINC PSS With Multi Band PSS
Case_3
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Multi Band PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 = Multi Band PSS
The input to the IVINC PSS is speed deviation, dw in pu.
The IVINC PSS is set with the following parameters:
K=5
K3=100
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine speed deviation
with respect to nominal (dw in pu). The test consists of IVINC PSS paired with
Multi Band PSS in areas 1 and 2 respectively. The test is conducted with the line
distance from areas 1 and 2 as 220 km and both are in equal length with 110 km to
each other. Using the parameters above the output of the system shows that the
generator has found it steady state at approximately 14 seconds. These are shown
in Figure 5.23. Generators 1 and
2 are both identical in area 1 while in area 2,
generators 3 and 4 are also identical to each other.
86
Case_3a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.23a) : Generator 1 IVINC PSS
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
Figure 5.23b) : Generator 2 MB_PSS
87
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.23c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.23d) : Generator 4 MB_PSS
Figure 5.23: a), b), c) and d)
88
The next analysis of the paired IVINC PSS and Multi Band PSS in areas 1
and 2 respectively is to conduct a system test where the length distance of areas 1
and 2 are changed. The length of area 1 is change to 50 km while area 2 to 170
km. The distance between area 1 and 2 are still maintained at 220km. This is to test
the system against fault. The fault will be detected near to area 1. The simulation
result shows that the system is able to reach stable system and the system is able to
recover from the fault at approximately 7 seconds as shown in Figure 5.24.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location is changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
Similar to Delta W PSS, the MB_PSS simulation result shows that the system is
unable to recover from the fault. When the distance of the fault is far away from
area 1 the system is unable to reach it steady state. The analysis stops at
approximately 3.6 seconds as shown in Figure 5.25.
89
Case_3b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b. pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.24a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.24b) : Generator 2 MB_PSS
90
pu
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0.999
0.9985
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.24c) : Generator 3 IVINC PSS
pu
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0.999
0.9985
0.998
0.9975
0
2
4
6
8
10
12
14
Figure 5.24d) : Generator 4 MB_PSS
Figure 5.24: a), b), c) and d)
91
Case_3c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
2.5
3
3.5
4
3.5
4
Figure 5.25a) : Generator 1 IVINC PSS
pu
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
2.5
Figure 5.25b) : Generator 2 MB_PSS
3
92
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0
0.5
1
1.5
2
2.5
3
3.5
4
3.5
4
Figure 5.25c) : Generator 3 IVINC PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
2
2.5
Figure 5.25d) : Generator 4 MB_PSS
Figure 5.25: a), b), c) and d)
3
93
5.6.5 Comparison IVINC PSS with Delta pa PSS
Case 4
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Delta pa PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 =Delta pa PSS
The input to the IVINC PSS is speed deviation, dw in pu.
The IVINC PSS is set with the following parameters:
K=5
K3=100
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine speed deviation
with respect to nominal (dw in pu). The test consist of IVINC PSS paired with
Delta pa PSS in areas 1 and
2 respectively. The test is conducted with line
distance from area 1 to area 2 as 220 km and both are in equal length with 110 km
each. Using the parameters above the output of the system shows that the generator
has found it steady state at approximately 13 seconds even though it is oscillating
but the output waveform oscillate at the same pattern as shown in Figure 5.26 a)
and c) for the IVINC controller and figure 5.26 b) and d) for the Delta pa
Controller. Where generators 1 and 3 are both identical, controlled by IVINC and
generators 2 and 4 shows similar output waveform controlled by Delta pa PSS.
94
Case_4a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
1.008
1.006
1.004
pu
1.002
1
0.998
0.996
0.994
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.26a) : Generator 1 IVINC PSS
1.01
1.008
1.006
pu
1.004
1.002
1
0.998
0.996
0.994
0
2
4
6
8
10
12
14
Figure 5.26b) : Generator 2 Delta pa
95
1.008
1.006
1.004
pu
1.002
1
0.998
0.996
0.994
0.992
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.26c) : Generator 3 IVINC PSS
1.01
1.008
1.006
1.004
pu
1.002
1
0.998
0.996
0.994
0.992
0
2
4
6
8
10
12
14
Figure 5.26d) : Generator 4 Delta pa
Figure 5.26: a), b), c) and d)
96
The analysis of the paired IVINC PSS and Delta pa in area 1 and 2
respectively is to conduct a system test where the length distance of area 1 and 2
are changed. The length of area 1 is changed to 50 km while area 2 to 170 km. The
distance between area 1 and 2 are still maintained at 220km. This is to test the
system against fault. The fault will be detected near to area 1. The simulation result
shows that the system is able to reach stable system and the system is able to
recover from the fault at about approximately 10 seconds. The simulation output
response shows that the four waveforms oscillate at the same pattern as shown in
Figure 5.27.
The test is conducted by changing the distance of area 1 in length of 170
km and area 2 in length of 50 km. This time the fault location was changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
When the distance of the fault is far away from area 1 the system is unable to reach
it steady state. The analysis stops at approximately 2.6 seconds as shown in Figure
5.28.
97
Case_4b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
1.005
1.004
1.003
pu
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.27a) : Generator 1 IVINC PSS
1.005
1.004
1.003
pu
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.27b) : Generator 4 Delta pa
98
1.005
1.004
1.003
pu
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.27c) : Generator 3 IVINC PSS
1.005
1.004
1.003
pu
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.27d) : Generator 4 Delta pa
Figure 5.27: a), b), c) and d)
99
Case_4c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
1.009
1.008
1.007
1.006
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.28a) : Generator 1 IVINC PSS
1.009
1.008
1.007
1.006
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
Figure 5.28b) : Generator 4 Delta pa
100
1.008
1.006
1.004
pu
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.28c) : Generator 3 IVINC PSS
1.005
1.004
1.003
1.002
pu
1.001
1
0.999
0.998
0.997
0.996
0.995
0
0.5
1
1.5
2
Figure 5.28d) : Generator 4 Delta pa
Figure 5.28: a), b), c) and d)
101
5.6.6 Analysis of IVINC PSS Controller
Case_5
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = IVINC PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 = IVINC PSS
The input to the IVINC PSS is speed deviation, dw in pu.
The IVINC PSS is set with the following parameters:
K=5
K3=100
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine speed deviation
with respect to nominal (dw in pu). The test system is controlled by IVINC PSS for
all of the generators. The test is to be conducted with the line distance from area 1
to area 2 as 220 km and both are in equal length with 110 km each. Using the
parameters above the output of the system shows that the generators are unable to
reach steady state response as shown in the Figure 5.29 and the simulation stops at
approximately 6.2 seconds. As shown in the figure that all the four waveforms
displayed straight line at the beginning, then peak to 1.05pu at approximately 11
seconds before suddenly dropping tremendously to 0.6 pu then tend to rise up after
16 seconds. The test results show that the IVINC PSS controller with speed
deviation dw in pu as input cannot works independently. It requires other PSS
controller to support and cope with the IVINC PSS as they compensate among
each other.
102
Case_5a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.29a) : Generator 1 IVINC PSS
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0
2
4
6
8
10
12
14
Figure 5.29b) : Generator 2 IVINC PSS
103
1.3
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0.4
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.29c) : Generator 3 IVINC PSS
1.3
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0.4
0
2
4
6
8
10
12
14
Figure 5.29d) : Generator 4 IVINC PSS
Figure 5.29: a), b), c) and d)
104
The next analysis of the IVINC PSS is to conduct a system test where the
length distance of area 1 and area 2 are changed. The length of area 1 is changed to
50 km while area 2 to 170 km. The distance between area 1 and 2 are still
maintained at 220km. This is to test the system against fault. The fault will be
detected near to area 1. Similar to the first analysis the waveforms displayed
straight line at the beginning, as explained in case 5a. The simulation result shows
that the system is unable to reach stable system and the system is unable to recover
from the fault, the simulation stop at about approximately 7.8 seconds as shown in
Figure 5.30.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location was changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
Similar to the pervious cases 5a and 5b the simulation result shows that the system
is unable to recover from the fault. When the distance of the fault is far away from
area 1 the system is unable to reach it steady state. The analysis stops at
approximately 4.55 seconds as shown in Figure 5.31.
105
Case_5b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0
2
4
6
8
10
12
14
16
18
20
18
20
Figure 5.30a) : Generator 1 IVINC PSS
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0
2
4
6
8
10
12
14
16
Figure 5.30b) : Generator 2 IVINC PSS
106
1.2
1.15
1.1
1.05
pu
1
0.95
0.9
0.85
0.8
0.75
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.30c) : Generator 3 IVINC PSS
1.2
1.15
1.1
1.05
pu
1
0.95
0.9
0.85
0.8
0.75
0
2
4
6
8
10
12
14
Figure 5.30d) : Generator 4 IVINC PSS
Figure 5.30: a), b), c) and d)
107
Case_5c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
1.3
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0.4
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.31a) : Generator 1 IVINC PSS
1.3
1.2
1.1
1
pu
0.9
0.8
0.7
0.6
0.5
0.4
0
2
4
6
8
10
12
14
Figure 5.31b) : Generator 2 IVINC PSS
108
1.4
1.3
1.2
1.1
pu
1
0.9
0.8
0.7
0.6
0.5
0.4
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.31c) : Generator 3 IVINC PSS
1.4
1.3
1.2
1.1
pu
1
0.9
0.8
0.7
0.6
0.5
0.4
0
2
4
6
8
10
12
14
Figure 5.31d) : Generator 4 IVINC PSS
Figure 5.31: a), b), c) and d)
109
5.6.7 IVINC pa PSS Controller
The next cases of IVINC analysis and comparison are to change the input
of the IVINC controller. The input to the IVINC controller this time around is
power acceleration with respect to nominal of pa=pm-pe. Using power acceleration
as the input is to observe and compare with the previous cases of the analysis
where the input to the IVINC controller is the speed deviation with respect to
nominal. The analysis and test conducted will be the same as the previous cases
which they are analyze based on of several combinations and pair of IVINC
controller with other conventional controllers. The test analysis is the same
procedure but with different input so as to observe whether the system is stable or
unstable using several different combinations of controllers. Fault analysis will
also be conduct in this test analysis by changing line 1a and line 1b respectively to
see whether the IVINC pa controller is able to overcome the fault and recover for
stability. The test is to study and compare from the previous analysis whether the
conventional controllers are able to cope with the IVINC pa controller to produce
stable output response.
110
5.6.8 Comparison IVINC pa PSS With No PSS
Case 6
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = No PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 =No PSS
The input to the IVINC PSS is the power acceleration, pa=pm-pe in pu.
The IVINC PSS is set with the following parameters:
K = 0.7
K3=10
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine power
acceleration, with respect to nominal (pa=pm-pe in pu). The test system is
controlled by IVINC PSS for generators 1 and
3 and without PSS controller at
generators 2 and 4. The test is conducted with line distance from area 1 to area 2
as 220 km and both are in equal length with 110 km each. Using the parameters
above the output of the system shows that the generator has found it steady state at
approximately 11 seconds for area 1 and approximately 13 seconds for area 2.
These are shown in the Figure 5.32 where generators 1 and 2, are both identical in
area 1 while in area 2, generators 3 and g 4 are also identical. Even though the
generators found it steady state output response in area 1 and 2 the machines in
area 1 and area 2 does not run in synchronism thus the whole system does not
produce stable output response. The machines in area 1 and area 2 run in
synchronism at about approximately 3.65 seconds before the simulation stop.
111
Case_6a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
1.035
1.03
1.025
pu
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.32a) : Generator 1 IVINC PSS
1.045
1.04
1.035
1.03
pu
1.025
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
Figure 5.32b) : Generator 2 No PSS
112
1.015
1.01
1.005
1
pu
0.995
0.99
0.985
0.98
0.975
0.97
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.32c) : Generator 3 IVINC PSS
1.015
1.01
1.005
1
pu
0.995
0.99
0.985
0.98
0.975
0.97
0.965
0
2
4
6
8
10
12
14
Figure 5.32d) : Generator 4 No PSS
Figure 5.32: a), b), c) and d)
113
From the simulation result in case 6a, observed that the generators 1 and 2
are identical in area 1, while generators 3 and 4 are identical in area 2 respectively.
The simulation shows that the generators in the local area mode produced stable
output response. In area 1 the system is stable where IVINC compensate with the
generators without the PSS controller to produce steady state output response. For
area 2 is the same result as area 1. However in the inter area mode the generators
are unable to synchronized among each other to obtain stable system. In this
analysis are to study the effect of changing the gain k in area 2 as shown in Table
5.5.4. When k=0.1 the system fail to obtain steady state output response as shown
in Figure 5.33, when gain k=0.5, k=1.0 and k=2.0 the system is able to produced
stable system as shown in Figure 5.34, 5.35 and 5.36. From the simulation in the
Figure below, observed that the ideal value of gain k is between 0.7 and 1.0
because it produced small ripple of steady state output waveform. The simulation
of the system will produce large output waveform, as the ripple output waveform
will be getting bigger when gain k value goes beyond the range of ideal gain k
between 0.7 and 1.0, these are shown in Figure 5.34 and Figure 5.36.
Parameters of IVINC in Area 2
1.
2.
3.
4.
k
0.1
0.5
1.0
2.0
K3
K4
K5
10
50
0
10
50
0
10
50
0
10
50
50
Table 5.5.4: ideal value of gain k, 0.7<k<1.0
Stability
Unstable
Stable
Stable
Stable
114
1.025
1.02
pu
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
Figure 5.33a) : Generator 1 IVINC in Area 1 where gain k=0.1
1.04
1.03
1.02
1.01
pu
1
0.99
0.98
0.97
0.96
0.95
0.94
0
2
4
6
8
10
12
14
16
18
Figure 5.33b) : Generator 3 IVINC in Area 2 where gain k=0.1
Figure 5.33: a) and b)
20
115
1.045
1.04
1.035
1.03
pu
1.025
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
Figure 5.34a) : Generator 1 IVINC in Area 1 where gain k=0.5
1.015
1.01
1.005
pu
1
0.995
0.99
0.985
0.98
0.975
0
2
4
6
8
10
12
14
16
18
Figure 5.34b) : Generator 3 IVINC in Area 1 where gain k=0.5
Figure 5.34: a) and b)
20
116
1.035
1.03
1.025
pu
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
Figure 5.35a) : Generator 1 IVINC in Area 1 where gain k=1.0
1.03
1.02
1.01
pu
1
0.99
0.98
0.97
0.96
0
2
4
6
8
10
12
14
16
18
Figure 5.35b) : Generator 3 IVINC in Area 2 where gain k=1.0
Figure 5.35: a) and b)
20
117
1.045
1.04
1.035
1.03
pu
1.025
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
Figure 5.36a) : Generator 1 IVINC in Area 1 where gain k=2.0
1.015
1.01
1.005
pu
1
0.995
0.99
0.985
0.98
0.975
0
2
4
6
8
10
12
14
16
18
Figure 5.36b) : Generator 3 IVINC in Area 2 where gain k=2.0
Figure 5.36: a) and b)
20
118
The next analysis of the paired IVINC PSS and No PSS in area 1 and 2 is
to conduct a system test where the length distance of area 1 and area 2 are changed.
The length of area 1 is changed to 50 km while area 2 to 170 km. The distance
between area 1 and 2 are still maintained at 220km. This is to test the system
against fault. The fault will be detected near to area 1. The simulation result shows
that the system is stable and the generator is able to recover from the fault. The
simulation reached steady state at
approximately 11 seconds as shown in figure
5.37. This is the only analysis of IVINC without the PSS controller that has
reached steady state condition where machines 1 and 2 in area 1, and machines 3
and 4 in area 2 are synchronized to each other.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location is changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
The simulation result shows that the system is unable to recover from the fault.
When the distance of the fault is far away from area 1 the system is unable to reach
it steady state. The analysis stops at approximately 2.5 seconds as shown in Figure
5.38 where IVINC fail to compensate with the No PSS in the system.
119
Case_6b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.37a) : Generator 1 IVINC PSS
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.37b) : Generator 2 No PSS
120
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.37c) : Generator 3 IVINC PSS
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
Figure 5.37d) : Generator 4 No PSS
Figure 5.37: a), b), c) and d)
121
Case_6c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
2.5
2
2.5
Figure 5.38a) : Generator 1 IVINC PSS
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
Figure 5.38b) : Generator 2 No PSS
122
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
0.5
1
1.5
2
2.5
2
2.5
Figure 5.38c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
0.5
1
1.5
Figure 5.38d) : Generator 4 No PSS
Figure 5.38: a), b), c) and d)
123
5.6.9 Comparison IVINC pa PSS With Delta w PSS
Case 7
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Delta w PSS
Area 2
Generator 3 = Delta w PSS
Generator 4 = Delta w PSS
The input to the IVINC PSS is the power acceleration, pa=pm-pe in pu.
The IVINC PSS is set with the following parameters:
K = 0.7
K3=10
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine power
acceleration, with respect to nominal (pa=pm-pe in pu). The test consist of IVINC
PSS paired with Delta w PSS in area 1. The test is conducted with the line distance
from area 1 to area 2 is 220 km and both are in equal length with 110 km each.
Using the parameters above the output of the system shows that the generator has
found it steady state at approximately 16 seconds. These are shown in Figure 5.39.
Generators 1 and 2, are both identical in area 1 with maximum peak of 1.0048pu
while in area 2, generators 3 and
1.005pu.
4 are both identical with maximum peak of
124
Case_7a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.39a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.39b) : Generator 2 Delta w PSS
125
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.39c) : Generator 3 Delta w PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0
2
4
6
8
10
12
14
Figure 5.39d) : Generator 4 Delta w PSS
Figure 5.39: a), b), c) and d)
126
The next analysis of the paired IVINC PSS and Delta w PSS in area 1 is to
conduct a system test where the length distance of area 1 and area 2 are changed.
The length of area 1is change to 50 km while area 2 to 170 km. The distance
between area 1 and 2 are still maintained at 220km. This is to test the system
against fault. The fault will be detected near to area 1. The simulation result shows
that the system is stable and the generator is able to recover from the fault at about
approximately 6 seconds as shown in Figure 5.40. The settling time of the system
output response as compared with case_7a is lesser, this is due because of the fault
distance is near to area 1 where the fault recovery occurs much faster.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location is changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
The simulation result shows that the system is unable to recover from the fault
when the distance of the fault is far away from area 1. The system is unable to
reach it steady state because machines in area 1 and
2 are unable to cope with
each other. The analysis stops at approximately 2.5 seconds as shown in Figure
5.41.
127
Case_7b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.40a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.40b) : Generator 2 Delta w PSS
128
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.40c) : Generator 3 Delta w PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.40d) : Generator 4 Delta w PSS
Figure 5.40: a), b), c) and d)
129
Case_7c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
2.5
2
2.5
Figure 5.41a) : Generator 1 IVINC PSS
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
Figure 5.41b) : Generator 2 Delta w PSS
130
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0
0.5
1
1.5
2
2.5
2
2.5
Figure 5.41c) : Generator 3 Delta w PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
Figure 5.41d) : Generator 4 Delta w PSS
Figure 5.41: a), b), c) and d)
131
Case 8
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Delta w PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 = Delta w PSS
The input to the IVINC PSS is the power acceleration, pa=pm-pe in pu.
The IVINC PSS is set with the following parameters:
K = 0.7
K3=10
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine power
acceleration, with respect to nominal (pa=pm-pe in pu). The test consist of IVINC
PSS paired with Delta w PSS in area 1 and area 2 respectively. The test is
conducted with the line distance from area 1 to area 2 is 220 km and both are in
equal length with 110 km each. Using the parameters above the output of the
system shows that the generator has found it steady state at approximately 7
seconds. These are shown in Figure 5.42. Where generators 1 and
2, are both
identical in area 1 while in area 2, generators 3 and 4 are both identical to each
other.
132
Case_8a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.42a) : Generator 1 IVINC PSS
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
Figure 5.42b) : Generator 2 Delta w PSS
133
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.42c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.42d) : Generator 4 Delta w PSS
Figure 5.42: a), b), c) and d)
134
The next analysis of the paired IVINC PSS and Delta w PSS in area 1 and 2
is to conduct a system test where the length distance of area 1 and area 2 are
changed. The length of area 1is change to 50 km while area 2 to 170 km. The
distance between area 1 and 2 are still maintained at 220km. This is to test the
system against fault. The fault will be detected near to area 1. The simulation result
shows that the system is stable and the generator is able to recover from the fault at
about approximately 6 seconds as shown in Figure 5.43.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location is changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
The simulation result shows that the system is unable to recover from the fault
when the distance of the fault is far away from area 1. The system is unable to
reach it steady state because machines in area 1 and
2 are unable to cope with
each other. The analysis stops at approximately 2.56 seconds as shown in Figure
5.44.
135
Case_8b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.43a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.43b) : Generator 2 Delta w PSS
136
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.43c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0
2
4
6
8
10
12
14
Figure 5.43d) : Generator 4 Delta w PSS
Figure 5.43: a), b), c) and d)
137
Case_8c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.44a) : Generator 1 IVINC PSS
pu
1.009
1.008
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
Figure 5.44b) : Generator 2 Delta w PSS
138
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.44c) : Generator 3 IVINC PSS
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0
0.5
1
1.5
2
Figure 5.44d) : Generator 4 Delta w PSS
Figure 5.44: a), b), c) and d)
139
5.6.10 Comparison IVINC pa PSS With Multi Band PSS
Case 9
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC PSS
Generator 2 = Multi Band PSS
Area 2
Generator 3 = IVINC PSS
Generator 4 = Multi Band PSS
The input to the IVINC PSS is the power acceleration, pa=pm-pe in pu.
The IVINC PSS is set with the following parameters:
K = 0.7
K3=10
K4=50
K5=0
The input to the IVINC PSS is the synchronous machine power
acceleration, with respect to nominal (pa=pm-pe in pu). The test consists of
IVINC PSS paired with Multi Band PSS in area 1 and area 2 respectively. The test
is conducted with the line distance from area 1 to area 2 is 220 km and both are in
equal length with 110 km each. Using the parameters above the output of the
system shows that the generator has found it steady state at approximately 8
seconds showing a curve waveform shape. These are shown in Figure 5.45.
Generators 1 and 2, are both identical in area 1 while in area 2, generators 3 and
4 are both identical to each other.
140
Case_9a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.45a) : Generator 1 IVINC PSS
pu
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
Figure 5.45b) : Generator 2 Multi Band PSS
141
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.45c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.45d) : Generator 4 Multi Band PSS
Figure 5.45: a), b), c) and d)
142
The analysis of the paired IVINC pa PSS and Multi Band PSS in area 1 and
2 respectively is to conduct a system test where the length distance of areas 1 and
2 are changed. The length of area 1 is change to 50 km while area 2 to 170 km. The
distance between area 1 and 2 are still maintained at 220km. This is to test the
system against fault. The fault will be detected near to area 1. The simulation result
shows that the system is able to reach stable system and the system is able to
recover from the fault at approximately 18 seconds with a curve shape waveform
as shown in Figure 5.46.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location is farther away
from area 1 but still maintaining the distance of area 1 to area 2 at 220 km. Similar
from the previous experiment with the same configuration shows that the systems
is unable to recover from the fault. The system is unable to reach it steady state
when it is far away from area 1. The analysis stops at approximately 2.6 seconds as
shown in Figure 5.47.
143
Case_9b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.0045
1.004
1.0035
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.46a) : Generator 1 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.46b) : Generator 2 Multi Band PSS
144
pu
1.003
1.0025
1.002
1.0015
1.001
1.0005
1
0.9995
0.999
0.9985
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.46c) : Generator 3 IVINC PSS
pu
1.003
1.002
1.001
1
0.999
0.998
0.997
0
2
4
6
8
10
12
14
Figure 5.46d) : Generator 4 Multi Band PSS
Figure 5.46: a), b), c) and d)
145
Case_9c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.47a) : Generator 1 IVINC PSS
pu
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
0.5
1
1.5
2
Figure 5.47b) : Generator 2 Multi Band PSS
146
pu
1.008
1.006
1.004
1.002
1
0.998
0.996
0.994
0.992
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.47c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
0.5
1
1.5
2
Figure 5.47d) : Generator 4 Multi Band PSS
Figure 5.47: a), b), c) and d)
147
5.6.11 Analysis of IVINC pa PSS Controller
Case 10
The test area configuration is set as follow:
Area 1
Generator 1 = IVINC pa PSS
Generator 2 = IVINC pa PSS
Area 2
Generator 3 = IVINC pa PSS
Generator 4 = IVINC pa PSS
The input to the IVINC PSS is the power acceleration, pa=pm-pe in pu.
The IVINC PSS is set with the following parameters:
K = 0.7
K3=10
K4=50
K5=0
The input to the IVINC pa PSS is the synchronous machine power
acceleration, with respect to nominal (pa=pm-pe in pu). The test consist of all
generators are controlled by IVINC PSS in area 1 and 2 respectively. The test is
conducted with the line distance from area 1 to area 2 is 220 km and both are in
equal length with 110 km each. Using the parameters above the output of the
system shows that the generator has found it steady state at approximately 10
seconds. The analysis shows that IVINC pa PSS is able to reach it stable state
independently even without pairing with other PSS controllers. Unlike the IVINC
w PSS it is unable to obtain steady state output response without combining with
other PSS controllers. Figure 5.48 show that Generators 1 and 2, are both identical
in area 1 while in area 2, generators 3 and generator 4 are also identical to each
other.
148
Case_10a
Fault distance is set equally 110km at area1 of line 1a and 110km of area 2 line 1b.
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.48a) : Generator 1 IVINC PSS
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
Figure 5.48b) : Generator 2 IVINC PSS
149
pu
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.48c) : Generator 3 IVINC PSS
pu
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
Figure 5.48d) : Generator 4 IVINC PSS
Figure 5.48: a), b), c) and d)
150
The analysis shown in Table 5.5.5 below is to see the difference of the
simulation results when the value of k and k3 is changed. We found out that the
ideal value of k and k3 to obtain steady state output response is 0.5 < k < 1.0 and 9
< k3 < 50 respectively. Presented in the simulation results shown below are several
different output waveforms generated by varying the value of k and k3. Thus we
can observe that:
Area 1
Area 2
Figure No.
k
k3
k4
k5
k
k3
k4
0.5
10
50
0
0.7
10
50
5.48
0.7
10
50
0
0.5
10
50
5.49
0.1
10
50
0
0.5
10
50
5.50
1.0
50
50
0
0.5
10
50
5.51
1.0
50
50
0
1.0
50
50
5.52
1.5
50
50
0
0.7
50
50
5.53
Table 5.5.5: The effect of changing value k in IVINC pa PSS
k5
0
0
0
0
0
0
Figure 5.49 and 5.51 shows that the system is stable in the local mode, area
1 showing the output waveform oscillate constantly with small ripple while in area
2 the output waveform oscillate with huge ripple pattern, the system fail to reaches
synchronization in the inter area mode. Figure 5.50 shows the system is stable with
gain k=0.7 and gain k=0.5 in area 1 and 2 respectively. The small differences of
gain k between area 1 and 2 do not affect much in the system. Figure 5.53 shows
that the system is synchronize between area 1 and 2 but fails to achieve stable
condition, this is because the value of gain k3 is large. This is also happens to the
simulation results shown in figure 5.52 and 5.54, the system is synchronizes and
stable at first, then it start to develop ripple output waveform at the end and the
system has become unstable.
151
1.035
1.03
1.025
pu
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.49a): Generator 1 – IVINC pa PSS
1.015
1.01
1.005
pu
1
0.995
0.99
0.985
0.98
0
2
4
6
8
10
12
14
Figure 5.49b): Generator 3 – IVINC pa PSS
Figure 5.49: a) and b)
152
1.012
1.01
1.008
pu
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.50a): Generator 1 – IVINC pa PSS
1.015
pu
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
Figure 5.50b): Generator 1 – IVINC pa PSS
Figure 5.50: a) and b)
153
1.045
1.04
1.035
1.03
pu
1.025
1.02
1.015
1.01
1.005
1
0.995
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.51a): Generator 1 – IVINC pa PSS
1.015
1.01
1.005
pu
1
0.995
0.99
0.985
0.98
0
2
4
6
8
10
12
14
Figure 5.51b): Generator 3 – IVINC pa PSS
Figure 5.51: a) and b)
154
1.003
1.0025
1.002
pu
1.0015
1.001
1.0005
1
0.9995
0.999
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.52a): Generator 1 – IVINC pa PSS
1.008
1.006
pu
1.004
1.002
1
0.998
0.996
0
2
4
6
8
10
12
14
Figure 5.52b): Generator 3 – IVINC pa PSS
Figure 5.52: a) and b)
155
1.015
1.01
1.005
1
pu
0.995
0.99
0.985
0.98
0.975
0.97
0.965
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.53a): Generator 1 – IVINC pa PSS
1.03
1.02
1.01
pu
1
0.99
0.98
0.97
0.96
0.95
0
2
4
6
8
10
12
14
Figure 5.53b): Generator 3 – IVINC pa PSS
Figure 5.53: a) and b)
156
1.003
1.002
1.001
1
pu
0.999
0.998
0.997
0.996
0.995
0.994
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.54a): Generator 1 – IVINC pa PSS
1.003
1.002
1.001
pu
1
0.999
0.998
0.997
0.996
0
2
4
6
8
10
12
14
Figure 5.54b): Generator 3 – IVINC pa PSS
Figure 5.54: a) and b)
157
The analysis of the IVINC pa PSS is to conduct a system test where the
length distance of area 1 and area 2 are changed. The length of area 1 is changed to
50 km while area 2 to 170 km. The distance between area 1 and 2 are still
maintained at 220km. This is to test the system against fault. The fault will be
detected near to area 1. From the simulation graph the systems has reached it
steady state at approximately 9 seconds. The system is able to recover from the
fault a little bit quicker as compared to case 10a because the fault location is near
to area 1. Figure 5.55 shows the simulation graph of the IVINC pa PSS at fault
location of 50km from area 1.
A further test is conducted by changing the distance of area 1 in length of
170 km and area 2 in length of 50 km. This time the fault location was changed the
other way around while still maintaining the distance of area 1 to area 2 at 220 km.
Similar to the pervious cases 5a and 5b the simulation result shows that the system
is unable to recover from the fault. When the distance of the fault is far away from
area 1 the system is unable to reach it steady state. The analysis stops at
approximately 2.55 seconds as shown in Figure 5.56.
158
Case_10b
Changing the fault distance of 50km at area 1 of line 1a and 170km of area 2 line
1b.
pu
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.55a) : Generator 1 IVINC PSS
pu
1.007
1.006
1.005
1.004
1.003
1.002
1.001
1
0.999
0
2
4
6
8
10
12
14
Figure 5.55b) : Generator 2 IVINC PSS
159
pu
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
16
18
20
16
18
20
Figure 5.55c) : Generator 3 IVINC PSS
pu
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
2
4
6
8
10
12
14
Figure 5.55d) : Generator 4 IVINC PSS
Figure 5.55: a), b), c) and d)
160
Case_10c
Changing the line distance of 170km at area 1 of line 1a and 50km of area 2 line
1b.
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.56a) : Generator 1 IVINC PSS
pu
1.014
1.012
1.01
1.008
1.006
1.004
1.002
1
0.998
0
0.5
1
1.5
2
Figure 5.56b) : Generator 2 IVINC PSS
161
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0.995
0
0.5
1
1.5
2
2.5
3
2.5
3
Figure 5.56c) : Generator 3 IVINC PSS
pu
1.005
1.004
1.003
1.002
1.001
1
0.999
0.998
0.997
0.996
0
0.5
1
1.5
2
Figure 5.56d) : Generator 4 IVINC PSS
Figure 5.56: a), b), c) and d)
162
5.7 Summary of the Analysis
Table 5.6.1 below shows the comparison between Delta w PSS and Multi
Band PSS controller. Both controllers show a stable system when the distance of
line 1a and line 1b are equal and, also the distance of line 1a is 50km and line 1b is
170km respectively. When the line is changed to 170km of line a 1 and 50 km of
line 1b both results show unstable condition.
1.
Combination of
Gen 1/Gen 2/Gen 3/Gen 4
Delta w / Delta w /Delta w / Delta w
2.
MB_PSS/MB_PSS
/MB_PSS
Distance of line 1a/1b km
50/170
110/110
170/50
Stable
Stable
Unstable
/
Stable
Stable
Unstable
MB_PSS
Table 5.6.1: Delta w and Multi Band PSS
Table 5.6.2 shown below present the comparison of IVINC PSS with Delta
w PSS, Multi Band PSS and with no PSS. The combination of IVINC PSS with no
PSS displays unstable condition for all of the analysis configurations. In this case
that IVINC PSS is unable to cope when there is no other conventional PSS to
support. It can also be seen from the table that when the test system uses IVINC
PSS, the system fails to produce a stable system. In conclusion IVINC PSS with
the input of speed deviation with respect to nominal of dw in pu is unable to
produce any stable system independently even by changing the gain parameters. It
depends and works with other conventional PSS to produce at least one
configuration of steady state output response.
163
1.
Combination of
Gen 1/Gen 2/Gen 3/Gen 4
IVINC / No_PSS / No_PSS / No_PSS
Distance of line 1a/1b km
50/170
110/110
170/50
Unstable Unstable Unstable
2.
IVINC / No_PSS / IVINC / No_PSS
Unstable
Unstable
Unstable
3.
IVINC / Delta w / Delta w / Delta w
Stable
Stable
Unstable
4.
IVINC / Delta w / IVINC / Delta w
Stable
Stable
Stable
5.
IVINC/ MB_PSS/ MB_PSS / MB_PSS
Stable
Unstable
Unstable
6.
IVINC / MB_PSS / IVINC / MB_PSS
Stable
Stable
Unstable
7.
IVINC / Delta pa / Delta pa / Delta pa
Unstable
Unstable
Unstable
8.
IVINC / Delta pa / IVINC / Delta pa
Stable
Stable
Unstable
9.
IVINC / IVINC / IVINC / IVINC
Unstable
Unstable
Unstable
Table 5.6.2: The input of speed deviation with respect of nominal (dw in pu).
The IVINC PSS using the input of power acceleration with respect to
nominal pa in pu shows that the IVINC pa PSS can produce a steady state output
response independently without paring or combining with other conventional
controllers. The IVINC pa PSS does produce a stable system even when not paired
with any PSS as shown in Table 5.6.3, however the IVINC PSS only produces a
stable system if line 1a line 1b is 50km and 170km respectively when paired with
no PSS in area 1 and area2 in the inter area mode.
164
1.
Combination of
Gen 1/Gen 2/Gen 3/Gen 4
IVINC / No_PSS / No_PSS / No_PSS
Distance of line 1a/1b km
50/170
110/110
170/50
Unstable Unstable Unstable
2.
IVINC / No_PSS / IVINC / No_PSS
Stable
Unstable
Unstable
3.
IVINC / Delta w / Delta w / Delta w
Stable
Stable
Unstable
4.
IVINC / Delta w / IVINC / Delta w
Stable
Stable
Unstable
5.
IVINC/ MB_PSS/ MB_PSS / MB_PSS
Unstable
Unstable
Unstable
6.
IVINC / MB_PSS / IVINC / MB_PSS
Stable
Stable
Unstable
7.
IVINC / Delta pa / Delta pa / Delta pa
Unstable
Unstable
Unstable
8.
IVINC / Delta pa / IVINC / Delta pa
Stable
Stable
Unstable
9.
IVINC /IVINC / IVINC / IVINC
Stable
Stable
Unstable
Table 5.6.3: The input of power acceleration with respect to
nominal (pa=pm-pe in pu).
There is one case where the entire possible configurations of tested
system analysis produce all steady state output response. This case is the
combination of inter area mode of IVINC PSS with Delta w PSS in area 1 and area
2 with the input of speed deviation with respect to nominal in pu as shown in table
5.6.2 above. This is the best combination where both shows compatibility and can
cope with each other to produce a stable output response.
Observe from the table above that most of the stable condition is
when line 1a is short at 50km and line 1b at 170km, then followed by an equal
length for line 1a and line 1b of 110km respectively. Most unstable result are that
produced by length line 1a and line 1b are 170km and 50km respectively where the
system is unable to tolerate when the fault is far from area 1 which area 1
supplying power to area 2. The most stable condition is when IVINC is paired with
another controller in area 1 and area 2. The machines inside the respectively area
are able to cope and compensate with each other to produce steady state output
response.
165
CHAPTER 6
CONCLUSION
6.1 Conclusion
This thesis has revisited an immune inspired algorithm called the Basic
Varela Immune Network Model (BVINM). A number of minor modifications to
the original system have been proposed, which, more accurately reflect the
intended and previously described system. The modified control system is called
Improved Varela Immune Network Model (IVINM) and thus is later on known as
Improved Varela Immune Network Controller (IVINC), the new immune
controller has the learning and memorizing characteristics. The result shows that
for the first order controlled object in the simulation example, the simulation
research which adopts the IVINC obtains good control results. Further research
and study need to be conducted to see how well the characteristics of the IVINC
can be implemented in various applications of control method to some extent.
The proposed immune controller IVINC can be applied to various control
systems. But it depends on the complexity of the plant or the object to be
controlled. Also to be determined is how well the immune controller can be
implemented into the object to be controlled to some extent. IVINC has proved
that it works well in power systems stabilizers to optimize the stability of power
166
and enhances the system’s performance. With the self-adaptability and memorizing
characteristics of IVINC controller it enhances the quality of the control system
while damping the low frequency oscillation.
The test analysis of IVINC PSS controller applied in the two area test
systems of power systems stabilizers is the first to be implemented and never being
done before. This is a new method of testing a power system stabilizer for stability
in the two area test system of Kundur’s four machines. Previously, the test was
conducted by only using conventional controllers such as Delta W PSS, Multi
Band PSS and Delta Pa PSS. From the simulation, it has been proved that the
IVINC PSS can perform and produce results as good as other controllers. In fact
with the combination of paired IVINC PSS controller and Conventional controllers
even better results are produced.
6.2 Future Works and Recommendation
Even though the IVINC PSS can perform well in the two area test systems,
the test analysis is quite time consuming. To obtain stable output response the
parameters of the IVINC PSS have to be changed using the trial and error method.
For future work it is recommended that some sort of method should be developed
to obtain the parameter of the gain of the IVINC controller. For example methods
to determine the parameters of gain k using global optimization method, can be
used to help in the optimization and tuning of the IVINC controller. There is much
to be understood on the analysis of IVINC for power systems stabilizers in this
thesis. Further research and analysis need to be conducted, much need to be
learned on the behavior of the IVINC controller.
There are various types of Immune Controller that can be adept into the
power system stabilizer in this thesis. The IVINC is one of them, maybe for future
work it is possible to conduct two test area system with several different types of
Artificial Immune Controllers [7]. Throughout research and reading new types of
167
artificial immune controller could be develop. The algorithm of the immune
controller is not just limited to one equation, but it can be configure to the type and
requirement of the controller. It can be configure with the specification for the
required works which resemble the similarity of a proportional integral derivative,
PID controller or artificial neural networks or fuzzy logic expert control system
and many more.
168
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