FAULT DIAGNOSIS AND DETECTION IN POWER

SYSTEMS USING ARTIFICIAL NEURAL

NETWORKS

Dissertation submitted in partial fulfillment of the requirements for the award of the degree of

BACHELOR OF TECHNOLOGY

ELECTRICAL AND ELECTRONICS ENGINEERING

SUBMITTED BY

A.D. SRIKANTH 05841A0232

L.V.S. KARTHIK 05D91A0210

UNDER THE GUIDANCE OF

Mr. P. RAMA KRISHNA REDDY

AURORA’S TECHNOLOGICAL AND RESEARCH

INSTITUTE

(Affiliated to Jawaharlal Nehru Technological University)

2009

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CERTIFICATE

This is to certify that the dissertation entitled FAULT DIAGNOSIS AND

DETECTION IN POWER SYSTEM USING ARTIFICIAL NEURAL NETWORKS

that is being submitted by Mr. A.D SRIKANTH (05841A0232) and Mr. L.V.S

KARTHIK (05D91A0210) in partial fulfillment for the award of the Degree of

Bachelor of Technology in ELECTRICAL AND ELECTRONICS ENGINEERING

to the Jawaharlal Nehru Technological University is a record of bonafied work carried

out by him/her under our guidance and supervision. The results embodied in this project

report have not been submitted to any other University or Institute for the award of any

degree or diploma.

SIGNATURE OF THE

INTERNAL GUIDE

NAME: Mr. RAMA KRISHNA REDDY

DESIGNATION: ASST. PROFESSOR, HOD EEE Dept.

(OFFICE SEAL)

(..…Signature of HOD ……)

(OFFICE SEAL)

Date:

2

ACKNOWLEDGEMENT

We would like to express our sincere thanks to Mr. M.RAMESH BABU, Chairman,

Aurora’s Technological and Research Institute for his kind Patronage.

We express our profound thanks to Dr. ALKA MAHAJAN, Director, Aurora’s

technological and research institute for providing us all the necessary facilities.

We are thankful to Asst. Prof. Mr. P RAMA KRISHNA REDDY, Head of the Department,

Electrical and Electronics department, Aurora’s technological and research institute for his

valuable suggestions in accomplishing this project.

We express our sincere thanks and acknowledge with deep sense of gratitude for the guidance

and encouragement rendered by him for sparing his valuable time at every stage.

Finally we would like to thank all the people who directly and indirectly co-operated us in

completing our project successfully.

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ABSTRACT

In power distribution system it is essential to minimize transients, line voltage dips and

spikes which are present due to the occurrence of fault. As a fault occurs in the power system,

the necessary steps will be taken to remove the fault using relays & circuit breakers

conventionally. But, if this fault occurrence is predicted in advance, then we can maintain a

better voltage profile and quality power to consumers.

We introduce a neural network in the power system, which can learn and therefore be trained

to find solutions, recognize patterns, classify data and forecast future events. In general, we

use a control methodology in Artificial Neural Network (A.N.N) which can classify & predict

the future events. It will be shown that it is possible to predict with good accuracy, the

magnitude of control variables based on previously acquired samples and use these values to

recognize the type of abnormal event that is about to occur on the network. This type of

computation performed by neural networks is termed as “Neuro Computing”.

It is one of fastest growing areas of research in the fields of Artificial Intelligence and pattern

recognition. A neural network design and simulation environment for real time fault diagnosis

and detection is presented. An analysis of the learning & generalization characteristics of

elements in power system is presented using Neural Network toolbox in Matlab.

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INDEX

Table of Contents Page Number

Certificate

Acknowledgements

Abstract

Index

List of Tables

List of Figures

CHAPTER-1

1. INTRODUCTION TO PROBLEMS AND ITS CAUSES IN POWER

DISTRIBUTION SYSTEM1.1. Introduction

1.2. System monitoring and control

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1.3. Artificial neural networks

1.4. Fault detection using neural networks

1.5. Area of thesis

1.6. Aim of thesis

1.7. Modeling and simulation

1.8. Project description

CHAPTER-2

2. Artificial neural networks2.1. Neural networks

2.1.1. Functions of neural networks

2.2. ANN technology

2.3. The Biological model

2.3.1. Neuron model

2.3.2. Neuron with vector input

2.3.3. Network architectures

2.3.4. Multiple layer of neurons

2.3.5. The mathematical model

2.3.6. Activation functions

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2.4 Neural network topologies

2.5 Training of artificial neural networks

2.6 Types of neural network controllers

2.6.1 Model predictive controller

2.6.2 Narma-L2 control

2.6.3 Model reference control

2.7 NN predictive controller

2.7.1 system identification

2.7.2 predictive control

CHAPTER-3

3. Elements of Power System3.1. Classification of modern power system

3.1.1.1. Generating station

3.1.1.2. Transmission system

3.1.1.3. Transmission and sub-transmission

3.1.1.4. Distribution network

3.1.1.5. Protection or switchgear

3.1.1.6. Loads

3.1.1.7. System protection

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3.1.1.8. Computer analysis

3.2 Distribution systems

3.2.1 Influence of voltage on the size of a feeder and a distributor

3.2.2 Types of distributors

3.3 Protection of equipment from fault and disturbances

CHAPTER-4

4. Fault Analysis in Power System4.1 Fault studies

4.2 Fault and its classification

4.2.1 Balanced three-phase fault

4.2.2 Unbalanced fault

4.3 Faults in generator

4.4 Factors involved in occurrence of fault

CHAPTER-5

5. PI Controller5.1 Conventional PI controller

5.2 Tuning of PI controllers

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CHAPTER-6

6. Simulation Circuits and Results6.1 Fault at generator unit

6.1.1 Fault sequence

6.1.2 Generator sub-sequence

6.1.3 Generator unit 100MVA- Speed and voltage control sub system

6.1.4 Step up transformer subsystem

6.2 Fault in distribution system

6.3 Neural network

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LIST OF FIGURES

2(a) Block Diagram of Neural Network

2(b) Approach of Neural Computing

2(c) Biological Neuron Model

2(d) Neuron Model

2(e) Neuron with Vector Input

2(f) Layer of Neurons

2(g) Composite Layer of Neurons

2(h) Multiple Layers of Neurons

2(i) Mathematical Model of Neurons

2(j) Non-Linear Functions used for Synaptic Inhibition

2(k) Supervised Learning Algorithm

2(l) System Identification

2(m) Structure of the Neural Network Plant Model

2(n) Model Indicating Predictive Control Process

2(o) Demo for Predictive Controller

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2(p) Demo of the NN Predictive Controller

2(q) Parameters for Neural Predictive Control

2(r) Plant Identification

2(s) Plant Input-Output Data

2(t) Training Data for NN Predictive Model

3(a) General Power System Layout

3(b) Typical Generator Diagram

3(c) Distribution System Layout

4 Comparison Table for Fault and Type of Protection Used

5(a) Simulation Circuit for Fault at Generation Unit

5(b) Output Waveforms (Voltage and Current) for Fault at Generation Unit

5(c) Fault Sequence

5(d) Generator Sub-System

5(e) Speed and Voltage Control Sub-System

5(f) Step-Up Transformer Sub-System

5(g) Parameters for Three Phase Fault Block

5(h) Fault Applied at the Distribution System

5(i) Output Voltage and Current Observed for a Two-Phase Fault

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Chapter -1

INTRODUCTION TO PROBLEMS AND ITS CAUSES IN

POWER DISTRIBUTION SYSTEM

1.1 Introduction

In the early development stages of electric power distribution systems, the

issue of supply quality was not addressed with the due importance. Today this aspect is

gaining increasing importance owing to the fact that more sensitive loads are connected

to the electrical system, and represents the aim of the modern Custom Power concept.

This term describes the value-added power that electric utilities will offer their

customers by the application of power electronic controllers to utility distribution

systems and/or at the supply end of many industrial and commercial customers.

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Nevertheless, no electric utility can be expected to provide perfect power supply to

customers insofar as disturbances may occur in the power system beyond its control. Of

these disturbances, voltage dips are becoming an increasing concern for process

industries due to increasing automation. Automated facilities are more difficult to

restart, and the electronic controllers used are sometimes more sensitive to voltage dips

than other loads.

For passive systems measures to reduce the effect of voltage dips on customer loads

should be taken based on locality and system connectivity, resorting to installation of

Static Var Compensator (SVC) at strategic points in the system or, for very sensitive

loads which cannot tolerate voltage dips of any duration, to use of on-line UPS

(Uninterruptable Power Supply) or Dynamic Voltage Restores (DVR). On the other

hand, for systems equipped with local sources interconnected with an electrical utility

system, sensitive loads could be efficiently protected by means of high speed protection

equipment. Consequently the industrial plant could be rapidly disconnected from the

utility network so that the duration of voltage dips, due to the intervention delay of the

interfacing device, is considerably reduced and the industrial process is not affected. In

any case, all systems that have to cope with voltage dips and over voltages are required

to intervene as quickly as possible to diagnosis the problem and to implement protective

measures.

For this purpose it is possible to resort to switching devices based on Solid-State circuit

Breakers (SSB), to be used as interface between the electrical utility system and the

private generating system with sensitive loads, ‘intelligently’ controlled by artificial

neural networks. They are software routines running on fast computers which play

nowadays an important role in solving Power System Engineering problems.

1.2 System Monitoring and Control

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For the aim they can be taught to predict different system states by monitoring the most

appropriate state variables; then, these predicted variables are processed to recognize the

event, using a particular diagnostic strategy. In this way the time required to detect the

fault can be reduced to a fraction of a millisecond, compared to the few tens of

milliseconds taken by modern protective relays. In fact, this control system does not

need to wait for the abnormal event to evolve to perceive its presence but, from the very

first samples of the monitored variables, it is already able to forecast how they will

behave and decide whether to intervene or not. This result is essential for avoiding that

solid-state breakers could be subjected to dangerous stresses and for minimizing voltage

dip transients before the protection devices intervene. Furthermore, implementing a

suitable control logic procedure, it is possible to realize a controlled interruption of the

energy flowing through the power line, enabling voltage spikes to be constrained within

acceptable limits [2,3].

It is important to underline the capability of the neural network to generalize its

prediction and detection processes to events that it has not learnt before, allowing the

control system to be more flexible and efficient than a standard classifier program.

1.3 Artificial Neural Networks

An important application of artificial intelligence (AI) is the diagnosis of faults of

mechanisms and systems in general. Traditional approaches to the problem of diagnosis

are to construct a heuristic, rule-based system which embodies a portion of the compiled

experience of a human expert. These systems perform diagnosis by mapping fault

symptoms to generated hypothesis to arrive at diagnostic conclusions. The knowledge

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acquisition and search process involved in expert systems is exhaustive and hence time

consuming. In addition, the simulation of models is usually too slow to be effectively

applied in a real-time environment.

Artificial Neural Networks (ANN) is found to be suitable for the above requirements.

They are massively parallel interconnected networks of simple adaptive elements and

their hierarchical organizations, which are intended to interact with the objects of the

real world in the same way as the biologic counterparts. Neural networks find wide

applications in parallel distributed processing and in real-time environments. Neural

Networks have considerable advantages over expert systems in terms of knowledge

acquisition, addition of new knowledge, performance and speed. Recently, interest in the

application of associative memories and neural networks to problems encountered in

diagnostic expert systems development has increased. Neural Networks appear to offer

features which coincide well with the requirements of pattern-based diagnosis.

An important feature of fault diagnosis using neural networks is that they can interpolate

the training data to give an appropriate response for cases described by neighboring or

noisy input data. This project describes the design and simulation of a neural network

for fault detection and diagnosis of power systems. In this fault diagnosis is

conceptualized as a pattern classification problem which involves the association of

patterns of input data representing the behavior of the power system to one or more fault

conditions. The neural network is trained off-line with different fault situations and used

on-line. The diagnostic system was able to detect and diagnose the faulted component

corresponding to the input pattern consisting of switching status of relays and circuit

breakers.

1.4 Fault Diagnosis and Detection using Neural Networks

Current techniques for fault detection and diagnosis rely on experts and expert

systems modeling using classical techniques in the time or frequency domain. Neural

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network classifiers can learn and adapt themselves to different statistical distribution and

non-linear mappings. The parallel structure of neural networks permits ‘INCIPIENT

FAULT DETECTION’ which is an indication of an increase in the lead time for

detecting faults. Neural Networks provide a greater degree of robustness or fault

tolerance than competitive fault detection methods because there are many more

processing nodes, each with primarily local connections. Damage to a few nodes or links

does not impair overall performance. In addition, neural networks are non- parametric

and can make weaker assumptions about the character of their probability distributions

of the sensor measurements. Artificial neural networks have the capacity to learn and

store information about fault occurrence via associative memory and thus have an

associative diagnostic ability with respect to the faults that occur in a power system.

1.5 Area of Thesis

In this project, there are a number of different tasks that needs to lead towards the

completion of this thesis project. These tasks are discussed briefly in the following

sections with more in depth information in later chapters as indicated.

1.6 Aim of the Thesis

The main aim of this project is to predict and classify the occurrence of fault in the

power system such that we can maintain a better voltage profile and quality to

consumers. We introduce a neural network which can learn and therefore be trained to

find solutions, recognize patterns, classify data and forecast future events.

1.7 Modeling and simulation

The modeling and simulation of this thesis helped to generate expected outcomes of the

project design. For simulation we need MATLAB software.

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More detail on the modeling and simulation design and results are given in further

chapters.

1.8 Project Description

As already explained this project mainly deals with prediction and classifying the

occurrence of fault in power distribution system. This report explains all the steps

involved in this project.

Chapter 2

Artificial Neural Networks

2.1 What are Neural Networks?

Neural networks are composed of simple elements operating in parallel. These elements are

inspired by biological nervous systems. As in nature, the connections between elements

largely determine the network function. You can train a neural network to perform a particular

function by adjusting the values of the connections (weights) between elements.

Typically, neural networks are adjusted, or trained, so that a particular input leads to a specific

target output. The next figure illustrates such a situation. There, the network is adjusted, based

on a comparison of the output and the target, until the network output matches the target.

Typically, many such input/target pairs are needed to train a network. Neural networks have

been trained to perform complex functions in various fields, including pattern recognition,

identification, classification, and speech, vision, and control systems.

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Figure 2(a) Block Diagram of Neural Network

Neural networks can also be trained to solve problems that are difficult for conventional

computers or human beings. The toolbox emphasizes the use of neural network paradigms that

build up to—or are themselves used in— engineering, financial, and other practical

applications.

An artificial neural network is a system based on the operation of biological neural networks,

in other words, is an emulation of biological neural system. Why would be necessary the

implementation of artificial neural networks? Although computing these days is truly

advanced, there are certain tasks that a program made for a common microprocessor is unable

to perform; even so a software implementation of a neural network can be made with their

advantages and disadvantages.

Advantages:

A neural network can perform tasks that a linear program can not.

When an element of the neural network fails, it can continue without any problem by

their parallel nature.

A neural network learns and does not need to be reprogrammed.

It can be implemented in any application.

It can be implemented without any problem.

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Disadvantages:

The neural network needs training to operate.

The architecture of a neural network is different from the architecture of

microprocessors therefore needs to be emulated.

Requires high processing time for large neural networks.

Another aspect of the artificial neural networks is that there are different architectures, which

consequently requires different types of algorithms, but despite to be an apparently complex

system, a neural network is relatively simple.

2.1.1 Functions of Neural networks

Artificial neural networks (ANN) are among the newest signal-processing technologies in the

engineer's toolbox. The field is highly interdisciplinary, but our approach will restrict the view

to the engineering perspective. In engineering, neural networks serve two important functions:

as pattern classifiers and as nonlinear adaptive filters. An Artificial Neural Network is an

adaptive, most often nonlinear system that learns to perform a function (an input/output map)

from data. Adaptive means that the system parameters are changed during operation, normally

called the training phase. After the training phase the Artificial Neural Network parameters are

fixed and the system is deployed to solve the problem at hand (the testing phase).

The Artificial Neural Network is built with a systematic step-by-step procedure to optimize a

performance criterion or to follow some implicit internal constraint, which is commonly

referred to as the learning rule. The input/output training data are fundamental in neural

network technology, because they convey the necessary information to "discover" the optimal

operating point. The nonlinear nature of the neural network processing elements (PEs)

provides the system with lots of flexibility to achieve practically any desired input/output map,

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i.e., some Artificial Neural Networks are universal mappers . There is a style in neural

computation that is worth describing.

Figure 2(b) Approach of Neural Computing

An input is presented to the neural network and a corresponding desired or target response set

at the output (when this is the case the training is called supervised). An error is composed

from the difference between the desired response and the system output. This error

information is fed back to the system and adjusts the system parameters in a systematic

fashion (the learning rule). The process is repeated until the performance is acceptable. It is

clear from this description that the performance hinges heavily on the data. If one does not

have data that cover a significant portion of the operating conditions or if they are noisy, then

neural network technology is probably not the right solution. On the other hand, if there is

plenty of data and the problem is poorly understood to derive an approximate model, then

neural network technology is a good choice.

2.2 ANN Technology

This operating procedure should be contrasted with the traditional engineering design, made of

exhaustive subsystem specifications and intercommunication protocols. In artificial neural

networks, the designer chooses the network topology, the performance function, the learning

rule, and the criterion to stop the training phase, but the system automatically adjusts the

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parameters. So, it is difficult to bring a priori information into the design, and when the system

does not work properly it is also hard to incrementally refine the solution. But ANN-based

solutions are extremely efficient in terms of development time and resources, and in many

difficult problems artificial neural networks provide performance that is difficult to match

with other technologies. Denker 10 years ago said that "artificial neural networks are the

second best way to implement a solution" motivated by the simplicity of their design and

because of their universality, only shadowed by the traditional design obtained by studying the

physics of the problem. At present, artificial neural networks are emerging as the technology

of choice for many applications, such as pattern recognition, prediction, system identification,

and control.

2.3 The Biological Model

Artificial neural networks emerged after the introduction of simplified neurons by McCulloch

and Pitts in 1943 (McCulloch & Pitts, 1943). These neurons were presented as models of

biological neurons and as conceptual components for circuits that could perform

computational tasks. The basic model of the neuron is founded upon the functionality of a

biological neuron. "Neurons are the basic signaling units of the nervous system" and "each

neuron is a discrete cell whose several processes arise from its cell body".

Figure 2(C) Biological Neuron Model

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The neuron has four main regions to its structure. The cell body, or soma, has two offshoots

from it, the dendrites, and the axon, which end in presynaptic terminals. The cell body is the

heart of the cell, containing the nucleus and maintaining protein synthesis. A neuron may have

many dendrites, which branch out in a treelike structure, and receive signals from other

neurons. A neuron usually only has one axon which grows out from a part of the cell body

called the axon hillock. The axon conducts electric signals generated at the axon hillock down

its length. These electric signals are called action potentials. The other end of the axon may

split into several branches, which end in a presynaptic terminal. Action potentials are the

electric signals that neurons use to convey information to the brain. All these signals are

identical. Therefore, the brain determines what type of information is being received based on

the path that the signal took. The brain analyzes the patterns of signals being sent and from

that information it can interpret the type of information being received. Myelin is the fatty

tissue that surrounds and insulates the axon. Often short axons do not need this insulation.

There are un-insulated parts of the axon. These areas are called Nodes of Ranvier. At these

nodes, the signal traveling down the axon is regenerated. This ensures that the signal traveling

down the axon travels fast and remains constant (i.e. very short propagation delay and no

weakening of the signal). The synapse is the area of contact between two neurons. The

neurons do not actually physically touch. They are separated by the synaptic cleft, and electric

signals are sent through chemical 13 interaction. The neuron sending the signal is called the

presynaptic cell and the neuron receiving the signal is called the postsynaptic cell. The signals

are generated by the membrane potential, which is based on the differences in concentration of

sodium and potassium ions inside and outside the cell membrane. Neurons can be classified by

their number of processes (or appendages), or by their function. If they are classified by the

number of processes, they fall into three categories.

Unipolar neurons have a single process (dendrites and axon are located on the same stem), and

are most common in invertebrates. In bipolar neurons, the dendrite and axon are the neuron's

two separate processes. Bipolar neurons have a subclass called pseudo-bipolar neurons, which

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are used to send sensory information to the spinal cord. Finally, multipolar neurons are most

common in mammals. Examples of these neurons are spinal motor neurons, pyramidal cells

and Purkinje cells (in the cerebellum). If classified by function, neurons again fall into three

separate categories. The first group is sensory, or afferent, neurons, which provide information

for perception and motor coordination. The second group provides information (or

instructions) to muscles and glands and is therefore called motor neurons. The last group,

interneuronal, contains all other neurons and has two subclasses. One group called relay or

projection interneurons have long axons and connect different parts of the brain. The other

group called local interneurons are only used in local circuits.

2.3.1 Neuron Model

Simple Neuron

A neuron with a single scalar input and no bias appears on the left below.

Figure 2(d) Neuron Model

The scalar input p is transmitted through a connection that multiplies its strength by the scalar

weight w to form the product wp, again a scalar. Here the weighted input wp is the only

argument of the transfer function f, which produces the scalar output a. The neuron on the

right has a scalar bias, b. You can view the bias as simply being added to the product wp as

shown by the summing junction or as shifting the function f to the left by an amount b. The

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bias is much like a weight, except that it has a constant input of 1. The transfer function net

input n, again a scalar, is the sum of the weighted input wp and the bias b. The central idea of

neural networks is that such parameters can be adjusted so that the network exhibits some

desired or interesting behavior. Thus, you can train the network to do a particular job by

adjusting the weight or bias parameters, or perhaps the network itself will adjust these

parameters to achieve some desired end. All the neurons in the Neural Network Toolbox™

software have provision for a bias, and a bias is used in many of the examples and is assumed

in most of this toolbox. However, you can omit a bias in a neuron if you want.

2.3.2 Neuron with Vector Input

A neuron with a single R-element input vector is shown below. Here the individual element

inputs are multiplied

and the weighted values are fed to the summing junction. Their sum is simply Wp, the dot

product of the (single row) matrix W and the vector p.

25

Figure 2(e) Neuron With Vector Input

The neuron has a bias b, which is summed with the weighted inputs to form the net input n.

This sum, n, is the argument of the transfer function f. This expression can, of course, be

written in MATLAB® code as

n = W*p + b

However, you will seldom be writing code at this level, for such code is already built into

functions to define and simulate entire networks.

2.3.3 Network Architectures

Two or more of the neurons shown earlier can be combined in a layer, and a particular

network could contain one or more such layers. First consider a single layer of neurons.

A Layer of Neurons

A one-layer network with R input elements and S neurons follow.

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Figure 2(f) Layer of Neurons

In this network, each element of the input vector p is connected to each neuron input through

the weight matrix W. The ith neuron has a summer that gathers its weighted inputs and bias to

form its own scalar output n(i). The various n(i) taken together form an S-element net input

vector n. Finally, the neuron layer outputs form a column vector a. The expression for a is

shown at the bottom of the figure.

A layer is not constrained to have the number of its inputs equal to the number of its neurons.

You can create a single (composite) layer of neurons having different transfer functions

simply by putting two of the networks shown earlier in parallel. Both networks would have the

same inputs, and each network would create some of the outputs.

Figure 2(g) Composite Layer of Neurons

27

Here p is an R length input vector, W is an SxR matrix, and a and b are S length vectors. As

defined previously, the neuron layer includes the weight matrix, the multiplication operations,

the bias vector b, the summer, and the transfer function boxes.

2.3.4 Multiple Layers of Neurons

A network can have several layers. Each layer has a weight matrix W, a bias vector b, and an

output vector a. To distinguish between the weight matrices, output vectors, etc., for each of

these layers in the figures, the number of the layer is appended as a superscript to the variable

of interest.

Figure(h) Multiple Layers of Neurons

The network shown above has R1 inputs, S1 neurons in the first layer, S2 neurons in the

second layer, etc. It is common for different layers to have different numbers of neurons. A

constant input 1 is fed to the bias for each neuron.

Note that the outputs of each intermediate layer are the inputs to the following layer. Thus

layer 2 can be analyzed as a one-layer network with S1 inputs, S2 neurons, and an S2xS1

weight matrix W2. The input to layer 2 is a1; the output is a2. Now that all the vectors and

28

matrices of layer 2 have been identified, it can be treated as a single-layer network on its own.

This approach can be taken with any layer of the network. The layers of a multilayer network

play different roles. A layer that produces the network output is called an output layer. All

other layers are called hidden layers. The three-layer network shown earlier has one output

layer (layer 3) and two hidden layers (layer 1 and layer 2).

2.3.5 The Mathematical Model

When creating a functional model of the biological neuron, there are three basic components

of importance. First, the synapses of the neuron are modeled as weights. The strength of the

connection between an input and a neuron is noted by the value of the weight. Negative

weight values reflect inhibitory connections, while positive values designate excitatory

connections [Haykin]. The next two components model the actual activity within the neuron

cell. An adder sums up all the inputs modified by their respective weights. This activity is

referred to as linear combination. Finally, an activation function controls the amplitude of the

output of the neuron. An acceptable range of output is usually between 0 and 1, or -1 and 1.

Mathematically, this process is described in the figure

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Figure(i) Mathematical Model of Neurons

From this model the interval activity of the neuron can be shown to be:

The output of the neuron, yk, would therefore be the outcome of some activation function on

the value of vk.

2.3.6 Activation functions

As mentioned previously, the activation function acts as a squashing function, such that the

output of a neuron in a neural network is between certain values (usually 0 and 1, or -1 and 1).

30

In general, there are three types of activation functions, denoted by Φ(.) . First, there is the

Threshold Function which takes on a value of 0 if the summed input is less than a certain

threshold value (v), and the value 1 if the summed input is greater than or equal to the

threshold value.

Secondly, there is the Piecewise-Linear function. This function again can take on the values of

0 or 1, but can also take on values between that depending on the amplification factor in a

certain region of linear operation.

Thirdly, there is the sigmoid function. This function can range between 0 and 1, but it is also

sometimes useful to use the -1 to 1 range. An example of the sigmoid function is the

hyperbolic tangent function

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Figure 2(j) Non-Linear Functions used for Synaptic Inhibiton

The artificial neural networks which we describe are all variations on the parallel distributed

processing (PDP) idea. The architecture of each neural network is based on very similar

building blocks which perform the processing.

2.4 Neural Network topologies

Feed-forward neural networks, where the data flow from input to output units is strictly

feed forward. The data processing can extend over multiple (layers of) units, but no feedback

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connections are present, that is, connections extending from outputs of units to inputs of units

in the same layer or previous layers.

Recurrent neural networks that do contain feedback connections, contrary to feed-

forward networks, the dynamical properties of the network are important. In some cases,

the activation values of the units undergo a relaxation process such that the neural network

will evolve to a stable state in which these activations do not change anymore. In other

applications, the change of the activation values of the output neurons is significant, such

that the dynamical behavior constitutes the output of the neural network.

2.5 Training of artificial neural networks

A neural network has to be configured such that the application of a set of inputs produces

(either 'direct' or via a relaxation process) the desired set of outputs. Various methods to set

the strengths of the connections exist. One way is to set the weights explicitly, using a priori

knowledge. Another way is to 'train' the neural network by feeding it teaching patterns and

letting it change its weights according to some learning rule.

We can categories the learning situations in two distinct sorts. These are:

Supervised learning or Associative learning in which the network is trained by

providing it with input and matching output patterns. These input-output pairs can be

provided by an external teacher, or by the system which contains the neural network

(self-supervised).

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Figure 2(k) Supervised Learning Algorithm

Unsupervised learning or Self-organization in which an (output) unit is trained to respond

to clusters of pattern within the input. In this paradigm the system is supposed to discover

statistically salient features of the input population. Unlike the supervised learning paradigm,

there is no a priori set of categories into which the patterns are to be classified; rather the

system must develop its own representation of the input stimuli.

Reinforcement Learning This type of learning may be considered as an intermediate

form of the above two types of learning. Here the learning machine does some action on

the environment and gets a feedback response from the environment. The learning system

grades its action good (rewarding) or bad (punishable) based on the environmental

response and accordingly adjusts its parameters. Generally, parameter adjustment is

continued until an equilibrium state occurs, following which there will be no more changes

in its parameters. The self organizing neural learning may be categorized under this type of

learning.

2.6 TYPES OF NEURAL NETWORK CONTROLLERS

Neural networks have been applied successfully in the identification and control of dynamic

systems. The universal approximation capabilities of the multilayer perceptron make it a

34

popular choice for modeling nonlinear systems and for implementing general-purpose

nonlinear controllers

Model Predictive Control

NARMA-L2 (or Feedback Linearization) control

Model Reference Control

There are typically two steps involved when using neural networks for control:

1 System identification

2 Control design

In the system identification stage, you develop a neural network model of the plant that you

want to control. In the control design stage, you use the neural network plant model to design

(or train) the controller. The control design stage, however, is different for each architecture.

•For model predictive control, the plant model is used to predict future behavior of the plant,

and an optimization algorithm is used to select th control input that optimizes future

performance.

•For NARMA-L2 control, the controller is simply a rearrangement of the plant model.

•For model reference control, the controller is a neural network that is trained to control a

plant so that it follows a reference model. The neural network plant model is used to assist in

the controller training.

2.6.1 Model Predictive Control

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This controller uses a neural network model to predict future plant responses to potential

control signals. An optimization algorithm then computes the control signals that optimize

future plant performance. The neural network plant model is trained offline, in batch form,

using any of the training algorithms. The controller, however, requires a significant amount of

online computation, because an optimization algorithm is performed at each sample time to

compute the optimal control input.

2.6.2 NARMA-L2 Control

This controller requires the least computation of these three architectures. The controller is

simply a rearrangement of the neural network plant model, which is trained offline, in batch

form. The only online computation is a forward pass through the neural network controller.

The drawback of this method is that the plant must either be in companion form, or be capable

of approximation by a companion form model.

2.6.3 Model Reference Control

The online computation of this controller, like NARMA-L2, is minimal. However, unlike

NARMA-L2, the model reference architecture requires that a separate neural network

controller be trained offline, in addition to the neural network plant model. The controller

training is computationally expensive, because it requires the use of dynamic back

propagation. On the positive side, model reference control applies to a larger class of plant

than does NARMA-L2 control.

2.7 NN Predictive Control

The neural network predictive controller that is implemented in the Neural Network

Toolbox™ software uses a neural network model of a nonlinear plant to predict future plant

performance. The controller then calculates the control input that will optimize plant

performance over a specified future time horizon. The first step in model predictive control is

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to determine the neural network plant model (system identification). Next, the plant model is

used by the controller to predict future performance. Finally, it discusses how to use the model

predictive controller block that is implemented in the Simulink® environment.

2.7.1 System Identification

The first stage of model predictive control is to train a neural network to represent the forward

dynamics of the plant. The prediction error between the plant output and the neural network

output is used as the neural network training signal. The process is represented by the

following figure:

Figure 2(l) System Identification

The neural network plant model uses previous inputs and previous plant outputs to predict

future values of the plant output. The structure of the neural network plant model is given in

the following figure. This network can be trained offline in batch mode, using data collected

from the operation of the plant.

37

Figure 2(m) Structure of the Neural Network Plant Model

.

2.7.2 Predictive Control

The model predictive control method is based on the receding horizon technique. The neural

network model predicts the plant response over a specified time horizon. The predictions are

used by a numerical optimization program to determine the control signal that minimizes the

following performance criterion over the specified horizon.

Where N1, N2, and Nu and define the horizons over which the tracking error and the control

increments are evaluated. The u' variable is the tentative control signal, yr is the desired

response, and ym is the network model response. The p value determines the contribution that

the sum of the squares of the control increments has on the performance index. The following

block diagram illustrates the model predictive control process. The controller consists of the

neural network plant model and the optimization block.

38

Figure 2(n) Model Indicating Predictive Control Process

Using the NN Predictive Controller Block

This section demonstrates how the NN Predictive Controller block is used. The first step is to

copy the NN Predictive Controller block from the Neural Network Toolbox block set to your

model window. A demo model is provided with the Neural Network Toolbox software to

demonstrate the predictive controller. This demo uses a catalytic Continuous Stirred Tank

Reactor (CSTR). A diagram of the process is shown in the following figure

Figure 2(o) Demo for Predictive Controller

The dynamic model of the system is

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Where h(t) is the liquid level, Cb(t) is the product concentration at the output of the process,

w1(t) is the flow rate of the concentrated feed Cb1, and w2(t) is the flow rate of the diluted

feed Cb2. The input concentrations are set to Cb1 = 24.9 and Cb2 = 0.1. The constants

associated with the rate of consumption are k1 = 1 and k2 = 1. The objective of the controller

is to maintain the product concentration by adjusting the flow w1(t). To simplify the

demonstration, set w2(t) = 0.1. The level of the tank h(t) is not controlled for this experiment.

To run this demo, follow these steps:

1 Start MATLAB®.

2 Run the demo model by typing pred_cstr in the MATLAB Command Window. This

command starts Simulink and creates the following model window. The NN Predictive

Controller block is already in the model.

3. Double-click the NN Predictive Controller block. This brings up the following window for

designing the model predictive controller. This window enables you to change the controller

horizons N2 and Nu. (N1 is fixed at 1.) The weighting parameter p, described earlier, is also

defined in this window. The parameter is used to control the optimization. It determines how

much reduction in performance is required for a successful optimization step. You can select

which linear minimization routine is used by the optimization algorithm, and you can decide

how many iterations of the optimization algorithm are performed at each sample time

40

Figure 2(p) Demo of the NN Predictive Controller

.

Figure 2(q) Parameters for Neural Predictive Control

41

4. Select Plant Identification. This opens the following window. You must develop the

neural network plant model before you can use the controller. The plant model predicts future

plant outputs. The optimization algorithm uses these predictions to determine the control

inputs that optimize future performance. The plant model neural network has one hidden layer,

as shown earlier. You select the size of that layer, the number of delayed inputs and delayed

outputs, and the training function in this window.

Figure 2(r) Plant Identification

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5. Select the Generate Training Data button. The program generates training data by

applying a series of random step inputs to the Simulink plant model.

The potential training data is then displayed in a figure similar to the following.

Figure 2(s) Plant Input-Output Data

6. Select Accept Data, and then select Train Network from the Plant Identification window.

Plant model training begins. The training proceeds according to the training algorithm (trainlm

43

in this case) you selected. After the training is complete, the response of the resulting plant

model is displayed, as in the following figure.

Figure 2(t)) Training Data for NN Predictive Model

You can then continue training with the same data set by selecting Train Network again, you

can Erase Generated Data and generate a new data set, or you can accept the current plant

model and begin simulating the closed loop system. For this demonstration, begin the

simulation, as shown in the following steps.

7. Select OK in the Plant Identification window. This loads the trained neural network plant

model into the NN Predictive Controller block.

8. Select OK in the Neural Network Predictive Control window. This loads the controller

parameters into the NN Predictive Controller block.

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9. Return to the Simulink model and start the simulation by choosing the Start command from

the Simulation menu. As the simulation runs, the plant output and the reference signal are

displayed.

CHAPTER-3

ELEMENTS OF POWER SYSTEM

Introduction Power system:-

3.1 Classification of Modern Power system

1. Generating stations

2. Transmission systems

3. Transmission and Sub Transmission

4. Distribution network

5. Protection system or switchgear

6. Loads

7. System Protection

8. Computer Analysis

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Figure 3(a) General Power System Layout

3.1.1 Generating station:

The electric energy is generated in this station. Electricity generation is the process of

converting non-electrical energy to electricity. For electric utilities, it is the first process in the

delivery of electricity to consumers.

One of the easiest ways to think about electric power generation is to think about it as the

opposite of electric power use -- kind of like a motor running backwards. Motors convert

electricity into power and motion. Generators convert motion and power into electricity.

Figure 3(b) Typical Generator Diagram

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A typical generator has a large electromagnet spinning inside a stationary coil of wire. As the

magnetic field produced by the ends of the magnet moves across the turns of wire in the

stationary coil, an electric current is set up in the wire. Increasing the number of turns of wire

in a ring or doughnut configuration increases the additive current in the wires.

3.1.2 Transmission system:

An electrical transmission system is comprised of equipment which transmits generated power

over long distances.

Transmission lines connect power generation plants to substations, other power generating

plants, and other utilities. These lines also run to other cities, or areas, where consumption

requirements are high.

A transmission line ends when it reaches a transmission substation or a distribution substation.

3.1.3 Transmission and Sub-transmission

The purpose of an overhead transmission network is to transfer electric energy from

generating units at various locations to the distribution system which ultimately supplies the

load. Transmission lines also inter connect neighboring utilities which permits not only

economic dispatch of power which regions during normal conditions, but also the transfer of

power between regions between emergencies.

Standard transmission voltages are established by the American National Standards Institute

(ANSI). Transmission voltage lines operating at more than 60KV are standardized at 69KV,

115KV, 138KV, 161KV, 230KV, 345KV, 500KV, and 765KV line-to-line. Transmission line

voltages above 230KV are usually referred to as extra-high voltage (EHV).

High voltage transmission lines are terminated in substations, which are called high-voltage

substations, receiving substations, or primary substations. The function of some substations is

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switching circuits in and out of service; they are referred to as switching stations. At the

primary substations, the voltage is stepped down more suitable for the next part of the journey

towards the load. Very large industrial customers may be served from the transmission system.

The portion of the transmission system that connects the high voltage substations through

step-down transformers to the distribution substations is called the sub-transmission network.

3.1.4 Distribution network

Electricity distribution is the penultimate stage in the delivery (before retail) of

electricity to end users. The purpose of the distribution system is to distribute the electricity to

each customer's residence, business, or industrial plant. It is primarily composed of the

distribution substation and distribution feeders, but also contains many other pieces of

equipment including reclosers, sectionalizers, fuses and capacitors.

Figure 3(c) Distribution System Layout

Electricity is "stepped down" from a high to low voltage by transformers located at the

distribution substation. These transformers are just the reverse of those which increased the

voltage at the generating station. Electricity enters the primary side coil with the larger number

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of windings and leaves from the secondary coil with the smaller number of windings. The

electricity is reduced to a lower distribution level voltage, usually less than 39,000 volts, and

distributed on three phase lines. There are a wide variety of three phase distribution line types

and voltages supplied by electric utilities across the country. A very common three phase

distribution line voltage is 12,000 volts or 12 kV.

The distribution line supplies the final step down transformer at the customer location where

the voltage is stepped down or lowered to the service voltage for the customer's electrical

system. Then the electricity flows through the service drop to the electrical meter at the

service to be measured for billing purposes.

3.1.5 Protection or Switchgear

The term switchgear, used in association with the electric power system, or grid, refers to the

combination of electrical disconnects, fuses and/or circuit breakers used to isolate electrical

equipment. Switchgear is used both to de-energize equipment to allow work to be done and to

clear faults downstream.

Switchgears are located anywhere that isolation and protection may be required. These

locations include generators, motors, transformers and substations.

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3.1.6 Loads

Loads of power systems are divided into industrial, commercial and residential. Very large

industrial loads may be served from the transmission system. Large industrial loads are served

directly from the sub-transmission network, and small industrial loads are served from the

primary distribution network. The industrial loads are composite loads, and induction motors

form a high proportion of these load. These composite loads are functions of voltage and

frequency and form a major part of system load. Commercial and residential loads consist

largely of lighting, heating and cooling. These loads are independent of frequency and

consume negligibly small reactive power.

The real power of loads is expressed in KW’s or MW’s. The magnitude of load varies

throughout the day, and power must be available to consumers on demand. The daily-load

curve of a utility is a composite of demands made by various classes of users. The greatest

value of load during 24-hr period is called the peak or maximum demand. The load factor is

the ratio of average load over a designated period of time to the peak load occurring in that

period. Load factors may be given for a day, a month, or a year. The yearly or annual load

factor is the most useful since a year represents a full cycle of time.

3.1.7 System Protection

In addition to generators, transformers, and transmission lines, other devices are required for

the satisfactory operation and protection of a power system. Some of the protective devices are

directly connected to circuits are called switch gear. They include instrument transformers,

circuit breakers, disconnect switches, fuses and lightning arresters. These devices are

necessary to deenergize either for normal operation or on the occurrence of faults. The

associated control equipment and protective relays are placed on switchboard in control

houses.

3.1.8 Computer analysis

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For a power system to be practical it must be safe, reliable, and economical. Thus many

analyses must be performed to design and operate an electrical system. Before going into

system analysis we have to model all the components of electrical power systems. Therefore,

after reviewing, we will calculate the parameters of a multi-circuit transmission line. We

model the transmission line and look at the performance of the transmission line. Since the

transformers and generators are part of the system, we model these devices. Design of a power

system, its operation and expansion requires much analysis. This presents methods of power

system analysis with the aid of a personal computer and the use of MATLAB. The MATLAB

environment permits a nearly direct transition from mathematical expression to simulation.

Some of the basic analysis:

Evaluation of transmission line parameters

Transmission line performance and compensation

Power flow analysis

Economic scheduling of generation

Synchronous machine transient analysis

Balanced fault

Symmetrical components and unbalanced fault

Stability studies

Power system control

3. 2 Distribution Systems

Electrical power can be generated by several methods from various energy sources. There are

several advantages of having ac generation. Normally, the generating stations are very far

from the load centers. Generated power is transmitted over high-voltage long transmission

lines. However the utilization of power is restricted to low voltage because the high capital

cost of appliances at high voltage, difficulties in maintenance, safety, etc. The power supply

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required by the various appliances may be dc or ac depending upon the use. However, ac

distribution of supply is common.

3.2.1 Influence of Voltage on the Size of a Feeder and a Distributor

The electric energy is supplied to consumers through a distribution system. A distribution

system can be subdivided into three distinct parts: feeders, distributor and server mains .

Feeders and distributors are explained in the previous sections. The service mains are small

conductors, which deliver power to Consumer’s premises up to the metering point.

3.2.2 Types of Distributors

If a distributor is connected to the supply system from one end only, it is called radial system

of distribution. This is also true for the feeders i.e., if a feeders i.e. if a feeder is connected to

the supply system from end only that feeder is called radial feeder.

A ring main is the name given to a distributor or feeder, which is arranged to form a closed

loop. It may have one or more feeding points.

There are three basic types of distribution system designs: Radial, Loop, or Network.

As you might expect, you can use combinations of these three

systems, and this is frequently done. The Radial distribution

system is the cheapest to build, and is widely used in sparsely

populated areas. A radial system has only one power source for a group of customers. A

power failure, short-circuit, or a downed power line would interrupt power in the entire line

which must be fixed before power can be restored.

A loop system, as the name implies, loops through the service area and

returns to the original point. The loop is usually tied into an alternate power

source. By placing switches in strategic locations, the utility can supply

power to the customer from either direction.

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If one source of power fails, switches are thrown (automatically or manually), and power can

be fed to customers from the other source.

The loop system provides better continuity of service than the radial system, with only short

interruptions for switching. In the event of power failures due to faults on the line, the utility

has only to find the fault and switch around it to restore service. The fault itself can then be

repaired with a minimum of customer interruptions.

The loop system is more expensive than the radial because more switches and conductors are

required, but the resultant improved system reliability is often worth the price.

Network systems are the most complicated and are interlocking loop systems. A given

customer can be supplied from two, three, four, or more different power supplies. Obviously,

the big advantage of such a system is added reliability. However, it is also the most expensive.

For this reason it is usually used only in congested, high load density municipal or downtown

areas.

3.3 Protection of Equipment from Faults and Disturbances

Electrical equipment has to be designed and constructed in such a way as to withstand the

foreseeable loading during its lifetime under normal and emergency conditions. Generally it is

economically not meaningful and technically not realistic to design equipment for all loadings

and disturbances. Among others factors, the following should be mentioned

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Unforeseeable site and ambient conditions such as flooding of basements where cables

are laid

External influences such as mechanical damage by construction work

Atmospheric influences such as lightning strokes in line conductors and structures

Aging and loss of dielectric strength of non - self - healing insulation

Example,

Oil - impregnated paper

– Internal influences such as short - circuits due to insulation failure.

It is therefore necessary to install devices for the protection of equipment which Limit the

effects of unforeseeable faults and loading on the equipment and protect it against cascading

damage. These protection devices must be capable of differentiating between normal and

disturbed operating conditions and they must operate reliably to isolate the damaged or

endangered equipment as soon as possible from the power supply. It is not the task of the

protective devices to avoid errors and disturbances. This can be achieved only by careful

planning of the power system, by thorough project engineering of the equipment and by

appropriate operation. Protective devices are to fulfill the following four conditions (“Two S

criteria”)

Selectivity: Protective devices shall switch - off only that equipment affected by the

System fault or impermissible loading condition; the no faulted equipment shall remain

in operation.

Sensitivity: Protective devices must be able to distinguish clearly between normal and

impermissible operating conditions or faults. Permissible high loading of equipment

during emergency operation and small short - circuit currents are to be handled in a

different way.

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CHAPTER-4

FAULT ANALYSIS IN POWER SYSTEM

4.1 Fault studies

Fault studies form an important part of power system analysis. The problem consists of

determining bus voltages and line currents during various types of faults. Faults on power

systems are divided into three-phase balanced faults and unbalanced faults. Different types of

unbalanced faults are single line-to-ground fault, line-to-line fault, and double line-to-ground

fault. The information gained from fault studies are used to select and set phase relays, while

the line-ground fault information is used for ground relays. Fault studies are also used to

obtain the rating of the protective switchgears.

The magnitude of the fault currents depends on the internal impedance of the generators plus

the impedance of the intervening circuit. The reactance of a generator under short circuit

condition is not constant. For the purpose of fault studies, the generator behavior can be

divided into three periods: the sub-transient period, lasting only for the first few cycles; the

transient period, covering a relatively longer time; and finally the steady state period.

4.2 Fault and its classification

Fault in power system occur because of insulation failure in plant which may be caused by a

system over-voltage such as switching surge or a lighting stroke, or may be due to broken

insulators or conductors, and various other causes on the transmission line.

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Type Probability of failure

Single phase to ground faults 10%

Phase to phase faults 15%

Two-phases to ground faults 10%

Three-phase faults 5%

Such faults cause heavy currents, called short circuit currents, to flow in the system. The

determination of the values of such currents enables us to make proper selection of circuit

breakers, protective relays and also helps to ensure that the associated apparatus. E.g.: busbars,

connections etc.

Single phase to ground faults are the most common whereas the three phases short-circuit

faults are the most severe faults and also the most amendable to calculations since these

involve symmetrical conditions only.

Types of faults in power system

Symmetrical or Balanced fault

Asymmetrical or Unbalanced fault

4.2.1 Balanced Three-phase fault

This type of fault is defined as the simultaneous short circuit across three phases. It occurs

infrequently, but it is most severe type of fault encountered. Because the network is balanced,

it is solved on a per-phase basis. The other two phases carry identical currents except for phase

shift. The reactance of the synchronous generator under short circuit conditions is a time-

varying quantity, and for network analysis three reactances were defined. The sub-transient

reactance of the first few cycles has short circuit current, transient reactance in the next 30

cycles and the synchronous reactance there after. Since the duration of the short circuit current

depends on the time of operation of the protective system, it is not always easy to decide

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which reactance to use. Generally, the sub-transient reactance is used for determining the

interrupting capacity of the circuit breakers.

Fault studies required for relay setting and coordination, transient reactance is used. Also, in

typical transient stability studies, transient reactance is used. A fault represents a structural

network change equivalent with that caused by the addition of impedance at the place of fault.

If the fault impedance is zero, the fault is referred to as the bolted fault or the solid fault

4.2.2 Unbalanced Fault

Different types of unbalanced faults are the single-line-ground fault, line-to-line fault, and

double line-to-ground fault. The online diagram simplifies the solution of the balanced three

phase problems; the method of symmetrical components that resolve the solution of

unbalanced circuit into a solution of a number of balanced circuits used.

4.3 Faults in Generator

Generators are frequently subjected to high currents and voltages caused by electrical

disturbances in the power system. Faults in particular subject the generator to stresses beyond

its design limits and cause high temperature increase, amplify and distort air gap torques, and

create unbalanced flux densities.

Generators are frequently subjected to high currents and voltages caused by electrical

disturbances in the power system and these significantly contribute to the reduction of the

machine operating life. The most affected group by these disturbances are small-scale grid-

connected power plants (<20MWe) such as mini-hydro, micro-turbines fueled by natural gas

or landfill gas, and wind turbines because of their low-inertias. In general, they have lower

plant availabilities as compared with the larger plants connected to high voltage transmission

57

lines. Faults in particular, although transient in nature, subject the generator to stresses beyond

its design limits and cause high temperature increase that weakens the machine’s mechanical

strength and insulation, amplify and distort air gap torques and create unbalance flux densities

in the air gap [1-5]. The cumulative effect of these abnormalities leads to material fatigue,

insulation and structural failure and eventually to equipment breakdown. Mechanically, the

abnormal forces generated excite and amplify the rotor oscillatory motion and result in severe

machine vibration.

4.4 Factors involved in Occurrence of fault

Electrical faults are the most damaging among the disturbances that could possibly happen in

the power system. Although faults are transient in nature that occur in just a few cycles, they

subject the generator to mechanical and temperature stresses beyond its operating limits. The

more frequent the occurrences of these events in the power network, the faster will be the rate

of deterioration or wear of the machine. The system parameters that can influence the effect of

electrical faults to the generator are generator inertia, generator and line damping, line and

fault impedance, Transient reactance and fault critical clearing time.

Smaller machines are more susceptible to damage or fatigue and more unstable during faults

therefore will have lower CCTs. This means that the lighter the machine, the less will be its

tolerance against electrical disturbances thus making it more unstable compared with heavier

machines. Heavier machines have higher tolerance against faults primarily because of the

flywheel effect of inertia. The higher damping ability provided by the generator damper

windings and the line resistance, provides higher generator stability as unbalanced currents or

voltages are, to some extent absorbed by the damper windings and the system.

Large fault impedances provide more stability while for large line reactance’s; the effect is the

opposite as the magnitude of the fault is higher. There are three interrelated factors considered

that excite the rotor’s normal mode of oscillation during electrical faults:

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sudden loss of load

distorted and amplified air gap torques or magnetic fields

Unsymmetrical flux densities in the air gap.

The effect of sudden loss of load is the abrupt change in the electrical torque Te due to the

change in the fundamental frequency of the armature current that induces impact torques on

the shaft or torsional oscillations as in the magnitude of which is proportional to the change in

Te in relation to mechanical torque Tm (or DT). The higher the change, the higher will be the

machine vibration response. This happens in a few cycles prior to clearance and again after

clearance. At the instant of a fault, the power developed by the generator abruptly decreases to

zero and its terminal voltage will drop to almost zero in magnitude. Since the prime mover is

incapable of responding instantly, Tm will be greater than Te resulting in the increase of

engine speed. The generator over speed protection will normally operate once the frequency

exceeds a certain level.

The major faults in case of generator can be classified as:

(a) Failure of prime movers:

Whenever there is a fault on prime mover side the conversion of mechanical power to

electrical power stops. If this generator works in parallel with other generator sets it will start

working as a synchronous motor running at synchronous speed and the prime mover will act

as load on it. When generator starts running as a motor it is called as inverted operation. If the

fault is cleared the generator will automatically pick up generation.

(b) Failure of excitation:

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The turbo generator set under consideration works in parallel with other sets. If the field of the

generator is interrupted it will continue to operate as an induction generator-delivering load at

very low power factor. So the other generators will be overloaded, as they will have to supply

the load of this faulty generator. This may lead to decrease the supply voltage and the stability

of the system will be affected.

The fault generator can be switched off but for an automatic operation an under current relay

in conjunction with time lagged tripping and time lagged reinforcing relay as shown in fig 1

can be used.

The under current relay is a moving coil type instrument and is fed through a shunt in the field

circuit and its provided with a double trip circuit contact. When there is field circuit failure

under current relay is no longer energized and the action moves up the operating armature up

and in doing so it stops supply to time lagged reinforcing and trip relays, thus both of theses

relays are operated simultaneously.

(a) Failure of insulation in the stator or interconnecting cable can be further divided into:

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Interphase short circuits – These are accompanied by large fault currents, the values of

which are dependent upon the capacity of the system to which the alternator is

connected, the nature of inter connection to the system and the moment at which the

short circuit occurs.

Single phase to earth fault – This will lead top the burning of alternator winding when

leakage current exceeds even 5 amps. This current is dependent upon the nature of

neutral connection, i.e. whether its earthed or unearthed.

Inter turn faults – In this type of fault short circuit exists between the turns of the same

phase or between turns of parallel branches in the same phase. The magnitude of the

fault current depends on the number of turns shorted. This is a dangerous type of fault

Earth fault on the rotor – Shorting of the rotor at one point to earth in itself does not

cause any damage. But the appearance of another fault to earth may sharply disturb the

magnetic flux distribution, which will lead to unequal voltages being induced in the

stator and increased vibration in the alternator.

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62

Figure 4 Comparison Table for Fault and Type of Protection Used

Chapter 5

PI CONTROLLER

Conventional PI Controller:

Controller: Modify the error signal & achieve better control action.

Modify the transient response & the steady state error of the system.

The aim of a PI controller is to determine the stator voltage frequency that will

make the measured output (speed of the rotor) reach the reference.

A proportional (kp) term, which is equal to the product of the error signal by a

constant called the proportional gain.

The integral (Ki) term of the controller is used to eliminate small steady errors. The

‘I ‘term calculates a continuous running total of the error signal.

Setbacks: The drawbacks of conventional PI Controller are High Peak Overshoot and

Prolonged settling time.

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TUNING OF PI CONTROLLERS

The technique to be adapted for determining the proportional integral constants of the controller, called

Tuning, depends upon the dynamic response of the plant. In presenting the various tuning techniques

we shall assume the basic control configuration wherein the controller input is the error between the

desired output (command set point input) and the actual output. This error is manipulated by the

controller (PI) to produce a command signal for the plant according to the relationship U(s) = Kp (1+1/

τis)

Or in time domain, U (t) = Kp [e(t) + (1/τ i ) ∫ edt]

Where Kp = proportional gain

τ i = integral time constant

If this response is S-shaped as in, Ziegler-Nichols tuning method is applicable

Zeigler- Nichols Rules for tuning PI controllers:

First Rule: The S-shaped response is characterized by two constants, the dead time L and the time

constant T. These constants can be determined by drawing a tangent to the S-shaped curve at the

inflection point and state value of the output. From the response of this nature the plant can be

mathematically modeled as first order system with a time constant T and delay time L as shown in

block diagram. The gain K corresponds to the steady state value of the output Css. The value of Kp, Ti

and Td of the controllers can then be calculated.

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CHAPTER-6

SIMULATION CIRCUITS AND ITS RESULTS

6.1.1 Fault at Generator unit

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Figure 6(a) Simulation Circuit for Fault at Generation Unit

The repetitive sequence is given as a fault signal at the generator unit.

Here line faults are not taken into account. The Voltage and Current waveforms for the

Above circuit can be observed as follows,

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Figure 6(b) Output Waveforms (Voltage and Current) for Fault at Generation Unit

As it’s evident from the above output form, we observe a climb or increase in the current

when the voltage is tending towards zero. And, the current follows the voltage during normal

operation timings.

Subsystems

The subsystems included in the above main circuit as discussed below. Each block is

discussed with relevant explanation

6.1.2 Fault Sequence

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Figure 6(c) Fault Sequence

The fault is considered as a repeated sequence of pulses with respect to time as,

Time values:

[0 .03 .0300001 .08 .0800001 .1]

Output Values:

[2 2 1 1 2 2]

This repeated sequence is given as a fault signal at the Generator unit. It helps in analyzing the

performance of power system when a fault occurs at the Generator unit.

6.1.3 Generator Subsystem

Figure 6(d) Generator Sub-System

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The generator subsystem basically contains the speed and voltage control block, a

synchronous machine and a three phase circuit breaker. The trip signal is connected to ‘A’,

which is linked to the Step-Up transformer subsystem. The synchronous machine with rating

15KV, 10MVA is connected with a three phase series RLC load. The per unit(PU) values

extracted from the speed and voltage control block are converted to exact values and further

connected to the main system. The subsystem is linked to the main system via the three phase

circuit breaker, which is connected to the R, S & T terminals.

6.1.4 Generator Unit -10MVA: Speed and Voltage Control Sub System

Figure 6(e) Speed and Voltage Control Sub-System

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The inputs to the speed and voltage control system i.e. ‘Vref’, ‘Wref’ are connected to the Governor

and the Excitation systems respectively. Whereas, ‘m’ is connected to the Excitation system via a

Demux and is driven to ‘Vt’. The outputs ‘Pm’, ‘Vf’ and ‘Vt’ are further driven and connected to the

Synchronous machine (15KV, 10MVA) as inputs.

6.1.5 Step-Up Transformer Sub-System

Figure 6(e) Step-Up Transformer Sub-System

The step-up transformer (15Kv-132KV) is connected to a three phase circuit breaker.

The fault signal is given as an input to the com port of the circuit breaker, which is in-turn

connected to the three phase V-I measurement block. This sub-system is further linked to the

main system via Ro, So and to terminals.

6.1.6 Parameters of the Three Phase Fault Block

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Figure 6(f) Parameters for Three Phase Fault Block

The three phase fault block is used to apply faults between any phase and the ground. The

fault timing can also be assigned accordingly. Here, the fault timing is giving between 1/60

and 5/60 seconds, i.e. the fault persists in the time interval 0.0166 to 0.0833 seconds. Based on

the test case, this block can be modified for two-phase, single-phase or three-phase faults.

6.2 Fault in Distribution system

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Figure 6(g) Fault Applied at the Distribution System

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6.2.1 Output Voltage and Current Observed for a Two-Phase Fault

Figure 6(h) Output Voltage and Current Observed for a Two-Phase Fault

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6.2.2 Output Voltage and Current Observed for a Single-Phase Fault

Figure 6(i) Output Voltage and Current Observed for a Two-Phase Fault

6.3 Neural Network Predictive Control

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Figure 6(j) Neural Network Predictive Control

Neural Network Predictive Controller is connected to the Plant (main system) via the control

signal. A sinusoidal wave is taken as a reference and the plant output is connected as a

feedback to the NN Predictive Controller. The output of the plant can be observed

in the XY graph or by means of a scope.

6.3.1 Testing waveforms of neural network controller

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Figure 6(k) Testing waveforms of neural network controller

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6.3.2Training Waveforms of Neural Network Controller

Figure 6(k) Training waveforms of Neural Network Controller

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Figure 6(l) Training the data with TRAINLM

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6.3.3 Validation Waveforms of Neural Network Controller

Figure 6(m) Testing waveforms of neural network controller.

6.4 PI Controller Circuit

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Figure 6(n) PI Controller Circuit

6.4.1 PI Controller Output with respect to reference

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Figure 6(o) PI Controller Output with respect to reference

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6.4.2 Neural Network Output with Respect to Reference

Figure 6(p) Neural Network Output with Respect to Reference

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CONCLUSION:

Suitability of Artificial Neural Networks for fault detection and diagnosis of power systems is

described in this project. Various network topologies (number of hidden nodes and layers)

have been tested and compared. The results show that accurate recall and generalization

behaviors are obtained during the diagnosis of single faults. Performance during recall

improves at first with an increase in the number of hidden nodes (units) and with the amount

of training, and finally attains convergence. In general, performance during generalization

improves with the duration of the training period. The neural network diagnostic system is

also able to diagnose correctly even in the presence of faulty operation of the relays of the

power system and under disturbances.

The neural network diagnostic system trained for single faults was found to be able to

accurately diagnose abnormal behavior resulting from simultaneous multiple faults. Graceful

degradation of the diagnostic system was observed in situations where faults where not

accurately diagnosed or under damage to a few nodes. Research is under progress in the

implementation of advanced training algorithms, like Counter Propagation and Simulated

Annealing using Boltzmann Machines, for efficient diagnostic problem Solving.

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The Role of Simulation in Design

Electrical power systems are combinations of electrical circuits and electromechanical devices

like motors and generators. Engineers working in this discipline are constantly improving the

performance of the systems. Requirements for drastically increased efficiency have forced

power system Designers to use power electronic devices and sophisticated control system

concepts that tax traditional analysis tools and techniques.

Further Complicating the analyst’s role is the fact that the system is often so nonlinear that the

only way to understand it is through simulation.

Overview

SimPowerSystems software is a modern design tool that allows scientists and engineers to

rapidly and easily build models that simulate power systems. It uses the Simulink

environment, allowing you to build a model using simple click and drag procedures. Not only

can you draw the circuit topology rapidly, but your analysis of the circuit can include its

interactions with mechanical, thermal, control, and other disciplines. This is possible because

all the electrical parts of the simulation interact with the extensive Simulink modeling library.

Since Simulink uses the MATLAB® computational engine, designers can also use MATLAB

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toolboxes and Simulink blocksets. SimPowerSystems software belongs to the Physical

Modeling product family and uses similar block and connection line interface.

How SimPowerSystems Software Works

Every time you start the simulation, a special initialization mechanism is called. This

initialization process computes the state-space model of your electric circuit and builds the

equivalent system that can be simulated by Simulink software. This process performs the

following steps:

Sorts all SimPowerSystems blocks, gets the block parameters and evaluates the

network topology. The blocks are separated into linear and nonlinear blocks, and each

electrical node is automatically given a node number.

Once the network topology has been obtained, the state-space model (A, B, C, D

matrices) of the linear part of the circuit is computed. All steady-state calculations and

initializations are performed at this stage. If you have chosen to discretize your circuit,

the discrete state-space model is computed from the continuous state-space model,

using the Tustin method.

Solution methods available through the Powergui block are

• Continuous solution method using Simulink variable-step solvers

• Discretization for solution at fixed time steps

Continuous Versus Discrete Solution

One important feature of SimPowerSystems software is its ability to simulate electrical

systems either with continuous variable-step integration algorithms or with a fixed-step using

a discretized system. For small size systems, the continuous method is usually more accurate.

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Variable-step algorithms are also faster because the number of steps is fewer than with a

fixed-step method giving comparable accuracy. When using line-commutated power

electronics, the variable-step, event-sensitive algorithms detect the zero crossings of currents

in diodes and thyristors with a high accuracy so that you do not observe any current chopping.

However, for large systems (containing either a large number of states or nonlinear blocks),

the drawback of the continuous method is that its extreme accuracy slows down the

simulation. In such cases, it is advantageous to discretize your system.

89

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