kdr report (repaired)

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ARTIFICIAL INTELLIGENCE IN POWER STATIONS

Chapter 1 INTRODUCTION

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Modern power systems are required to generate and supply high quality electric energy to customers.

To achieve this requirement, computers have been applied to power system planning, monitoring andcontrol. The increasing prominence of the computers has led to a new way of looking at the world. Power

system application programs for analyzing system behaviours are stored in computers. In the planning stage

of a power system, system analysis programs are executed repeatedly. Engineers adjust and modify the input

data to these programs according to their experience and knowledge about the system until satisfactory plans

are determined. Artificial intelligence emerged as a computer science discipline in the mid 1950s. Since

then, it has produced a number of powerful tools, many of which are of practical use in engineering to solve

difficult problems normally requiring human intelligence.Artificial Neural Networks and the Fuzzy logicsystems that are considered as the so called soft computing methods are now a days becoming predominant

tools in the area of Artificial Intelligence linked application oriented methods. Artificial intelligence has its

own well-developed programming languages. The most widely used languages are LISP and PROLOG.

The Neural network theory was first adopted in 1940 where the starting point was the learning law

proposed by ITEBB in 1949, which demonstrated how neurons could exhibit learning behaviour. The

application further waxed and waned away because of the lack of powerful technological advancement. The

resurgence occurred recently due to the new methods that are emerging as well as the computational power

suitable for simulation of interconnected neural networks. Further to the technological advancement in the

field of ANN, researchers were attracted on their important applications where logical and relational

thinking is required. Among the major applications viz., robotics, analysis, optimal control, database,

learning, signal processing, semiconductors, power system related applications became a useful tool for the

online researchers in this field.

Fuzzy Systems or logics were introduced by Lotfi Zadeh in 1965. It has basically been introduced to

solve inexact and vague concepts by relating those using multi-valuedness in a logical way. Earlier research

in this field was based on mathematical understanding of set theory and probability. Further as a part of

developing it as mathematics, the applications of these theories were considered in different areas. The

application of fuzzy systems other than in almost all the areas of the power systems are mainly in the field of

modal interface, speech recognition, functional reasoning, hybrid application along with Neural nets,

information, traction control, business.

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Chapter 2 WHAT IS AN ARTIFICIAL INTELLIGENCE?

_________________________________________________________________

Artificial intelligence (AI) is the intelligence of machines and robots and the branch of Computer

science that aims to create it. John McCarthy, who coined the term in 1956, defines it as "the science and

engineering of making intelligent machines". AI textbooks define the field as "the study and design of

intelligent agents"where an intelligent agent is a system that perceives its environment and takes actions that

maximize its chances of success. In a broader sense, AI is a branch of computer science that studies the

computational requirements for tasks such as perception, reasoning and learning and develop systems to

perform those tasks.

2.1Tools

In the course of 50 years of research, AI has developed a large number of tools to solve the most

difficult problems in various fields. Few of them are mentioned below-

1.Neural networks

A neural network, also known as a parallel distributed processing network, is a computing solution

which consists of interconnected processing elements called nodes or neurons that work together to producean output function. It is loosely modeled after cortical structures of the brain.

2.Fuzzy Logic

Logic is used for knowledge representation and problem solving, but it can be applied to other

problems as well. Fuzzy logic is a superset of conventional(Boolean) logic that has been extended to handle

the concept of partial truth values between "completely true" and "completely false". As its name suggests, it

is the logic underlying modes of reasoning which are approximate rather than exact. Fuzzy logic allows the

truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0).

3. Control theory

Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.

4.Languages

AI researchers have developed several specialized languages for research, including LISP and PROLOG.

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Chapter 3 NEURAL NETWORKS

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3.1 Definition of the Neural Network

Neural networks are based on neurophysical models of human brain cells and their interconnection.

Neural networks are systems that typically consist of a large number of simple processing units, called

Neurons. A neuron has generally a high-dimensional Input vector and one single output signal. This output

signal is usually a non-linear function of the input vector and a weight vector. The function to be performed

on the Input vector is hence defined by the non-linear function and the weight vector of the neuron.

3.2 Fundamentals of an Artificial Neural Network

Elementary processing unit of ANNs is neuron. Generally it contains several inputs but has only

one output. As basic element the neurons are not powerful but their interconnections allow encoding

relationship between variables of the problems to which it is applied and provide very powerful processing

capabilities.

Fig 3.1 Schematic Diagram of the Neuron

General model of the processing unit of ANN can be considered to have the following three elements.

I. Weighted Summing Unit

The weighted summing unit consists of external or internal inputs (Xi (x1, x2, x3 xn)) times the

corresponding weights Wij = (wi1, wi2,. win). The fixed weighted inputs may be either from the

previous layers of ANN or from the output of neurons. If these inputs are derived from neuron outputs, it

forms the feedback architecture it has feed forward architecture.

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Neuron

Incoming Weighted

Connections

Output = F ( Inputs )

Outgoing WeightedConnections


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II. Linear Dynamical Function

It is essentially a single input or single output function block. This block may exist for time varying

signals and introduces a function that is an integral, a proportional, a time delay or a combination of these.

Example: Following two general functions can be used to relate input Pi with output Qi as(a1,a2) Qi(t) = Pi(t)

Qi(t) = Pi(t-T)

III. Non linear function

This decides the firing of neuron for given input values. It is a static nonlinear function which may be

pulse type or step type, differentiable (smooth) or non-identification (sharp) and having positive mean or

zero mean. Some of the examples of such functions are Threshold, Sigmoid, Tan hyperbolic or Gaussianfunctions.

3.3 Neural Network Design

A neural network element is a smallest processing unit of the whole network essentially forming a

weighted sum and transforming it by the activation function to obtain the output. In order to gain sufficient

computing power, several neurons are interconnected together. The manner in which actually the neurons

are connected together depends on the different classes of the neural networks. Basically neurons are

arranged in layers. ANNs have parallel distributed architecture with a large number of nodes and

connections.

3.3.1 ANN Architecture

Construction of neural network involves the following tasks.

(i) Determination of network topology

(ii) Determination of system (activation & synaptic) dynamics

(i)Determination of the Network Topology

The topology of the neural network refers to its framework as well as its interconnection scheme. The

number of layers and the number of nodes per layer often specify the framework. The types of layer include

Input Layer where the nodes are called input units, which do not process information but distribute

information to other units.

Hidden Layer(s) where the nodes are called hidden units, which are not directly observable. They provide

into the networks the capability to map or classify nonlinear problems.

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The Output Layer where the nodes are called output units, which encode possible concepts (or values) to

be assigned to the instance under consideration. For example each output unit a class of objects. Other main

important concept is the weightage for the connected unit. It can be real or integer numbers. They can be

confined to a range and are adjustable during network training. When training is completed, all of them

attain fixed values.

(ii)Determination of Systems (Activation & Synaptic) Dynamics

The dynamics of the network determines its operation. ANNs can be trainable non-linear dynamical

systems. Neural dynamics consists of two parts, one which corresponding to the dynamics of activation

states and the other corresponding to the dynamics of synaptic weights. The activation dynamics determines

the time evolution of the neural activations. Synaptic activation determines the change in the synaptic

weights. The synaptic weights form Long Term Memory (LTM) where as the activation state forms Short

Term Memory (STM) of the network. Synaptic weights change gradually, whereas the neuron's activation

fluctuate rapidly. Therefore, while computing the activation dynamics, the system weights are assumed to be

constant. The synaptic dynamics dictates the learning process.

3.4 When and why using Neural Network?

Neural set is basically a new way of solving the problems, which way can successfully be followed

for a number of problems. However for some problems, neural network is not useful. Main difference of

using the Neural Network and conventional method of solving problems are, Neural Network is trained to

perform satisfactory. In a training phase, training examples are presented to the networks and the weights of

the neural networks are adapted by a learning rate. The ways of implementing the solution to specific

problems can be divided as

Fig 3.2 Ways of Implementing a Solution to a Specific Problem

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3.5 Training of Artificial Neural Network

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 and another way is to 'train' the neural networkby feeding it teaching patterns and letting it change its weights according to some learning rule. We can

categorize the learning situations in distinct sorts

Supervised learning or Associative learning is the one 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).

Unsupervised learning or Self-organization is the one 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 itsparameters. Generally, parameter adjustment is continued until an equilibrium state occurs

3.6 Useful Functions of the Neural Network

Useful Functions to be performed by the Neural network are as follows:

Its useful to apply the neural networks on problems for which no direct algorithmic solutions exists but for

which problem examples of the desired responses are availed.

It is useful to apply Neural Networks for the problems that change over the time. The adaptability of theneural network will then be used to adapt the implemented solution whenever the problems changes.

Its useful to apply Neural Networks to problems for which only too complicated algorithms can be derived.

Too complicated means that implemented (conventional) algorithms are either too large, or consume too

much power. It's not useful to train neural network on problems for which the solution can easily be

implemented in an algorithm. Neural Network can also learn these simple algorithms but neural

implementation is generally larger and less accurate than the direct algorithmic implementation of the

solution. For number of problems the implementation of the solution in Neural Network is useful, while for

other problems the solution should not use neural networks.

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CHAPTER 4 FUZZY LOGIC AND FUZZY SYSTEMS

_________________________________________________________________

4.1 Importance of Fuzzy Systems

Fuzzy set theory derives from the fact that almost all-natural classes and concepts are fuzzy rather

than crisp in nature. Fuzziness describes event ambiguity. It matters the degree, to which an event occurs,

not whether it occurs or occurs in random to what degree it occurs is fuzzy. Whether an ambiguous event

occurs - as when we say, "there is 20 percent chance of light rain tomorrow" - involves compound

uncertainties, the possibility of fuzzy event emerges. Fuzzy systems store benefits of fuzzy associates or

common sense "rules". Fuzzy programming admits degrees.

When asked a question or given an input, fuzzy systems fire each fuzzy rule in parallel, but to a

different degree, to infer a conclusion or output. Thus fuzzy systems reason with sets, fuzzy" or multi

valued sets. They estimate sampled functions from input to output. They may use linguistic or numeric

samples. For example, they may use heavy, longer or number (relative) for the degree of fuzziveness. Fuzzy

interpretations of data are a natural and intuitively plausible way to formulate and solve various problems in

pattern recognition. Fuzzy logic is a logical system for formalization of approximate reasoning, and in a

wider sense, used anonymously with Fuzzy set theory. It is an extension of multi valued logic. Fuzzy logic

systems provide an excellent framework to more completely and effectively model uncertainty and

imprecision in human reasoning with the use of linguistic variables with membership functions.Fuzzification offers superior expressive power, greater generality, and an improved capability to model

complex problems at a low solution cost.

4.2 Basic Concepts

Suppose your are approaching a red light and must advise a driving student when to apply brakes.

Would U say " begin braking 14 feet from the cross walk " or shall we say "apply brakes pretty soon". We

will say the latter and so the natural language is one example of ways vagueness arises, is used, and ispropagated in every days life. Imprecision in data and information gathered from and about our

environment is either statistical (e.g. a coin toss) the outcome is a matter of chance - or non-statistical - This

latter type of uncertainty is called fuzziness.

4.3 Fuzzy Sets and Rules

In fuzzy set theory ' normal 'sets are called crisp sets, in order to distinguish them from fuzzy sets.

Let C be a crisp set defined on the universe U, then for any element of u of U, either u (C) or U (C) occurs.In fuzzy set theory this property is generalized, therefore in a fuzzy set F, It is not necessary that either u

F or u (F) exist. In the fuzzy sets theory the generalization of the membership properties are as follows.7


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For any crisp set C it is possible to define a characteristic function C: U [0,1] instead from thetwo-element set {0,1}. The set that is defined on the basis of such an extended membership function is called

as fuzzy set. Fuzzy rules are elementary or composed proposals. They result from a conjunction between

elementary fuzzy proposals. A fuzzy rule is composed of a premise and a conclusion. The classical structure

of a rule is If < premise> then . When the premise is an elementary fuzzy proposal, the rule

is described as follows. If then < conclusion>. The x is a variable; generally real, defined on a

referential called the universe of discourse, given as a capital letter here X. A is a linguistic term, taken in a

set of terms noted as TX. Basic concept of fuzzy logic's is fuzzy " If then Rule " or Fuzzy Rule.

4.4 Classical Operations of Fuzzy Sets

Zadeh defined classical operations for fuzzy sets

Let f (X) = all fuzzy subsets of X (that is, m f (X) m: X | (0,1),

The fuzzy sets mA, mBF (x). The fuzzy rules are

1.Definition: Two fuzzy sets are equal (A = B) if and only if

X X: (=) Equality A = B mA(x) = mB(x)

(X where x: point wise, function __ theoretic operations)

2.Definition: A is a subset of B (A B) if and only if

X X: () Containment A B mA (x) mB (x)The other operations are

X X: (~) ComplimentmA (x) = 1-mA (x)

X X: () Intersection mA B (x) = min {mA (x), mB (x)}

X X: () UnionmAB (x) = min {mA (x), mB(x)}

4.5 Membership Function and Membership ValuesMembership function is the basis idea in fuzzy set theory. Its values measure degrees to which

objects satisfy imprecisely defined properties. Fuzziness represents similarities of objects to imprecisely

defined properties and probabilities which convey information about value frequencies. The member ship

function F of the fuzzy set F is a function

F: U [0,1]

So, every element u of U has a membership degree F (u) {0,1}. F is completely determined by the set of

tuplesF = {(u, F (u)) | u U}

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4.6 Fuzzy Relations

The fuzzy relation can be considered as a fuzzy set of tuples. That means each tuples has

membership degree between 0 and 1. Its definition is

Let U and V be uncountable (continuous) universe and R : U X V [ 0,1] , thenR = R(u,v) /(u,v)

UxV

This is a binary fuzzy relation on U x V. If U and V are controllable (discrete) universes, then

R = R(u,v) /(u,v)

UxV

The integral symbol denoted the set of all tuples on U x V denoted by

R(u,v) /(u,v)

4.7 Properties of Fuzzy Sets

Let A and B be the fuzzy sets, defined respectively on the universes X and Y, and let R be a fuzzy

relation defined on XxY. The support of fuzzy set A is the crisp that contains all element of A with non-zero

membership degree. This is denoted by S (A), formally defined as

S (A) = {uX | A (u) >0}

The width of the convex fuzzy set A with support set S (A) is defined by Width (A) which is

equal to Sup (S (A)) - Inf (S (A)) where Sup and Inf denote the mathematical operations supremum andinfimum.

4.8 Fuzzy Truth Value

A fuzzy truth-value is defined to be a fuzzy set on the closed interval V = [0,1] as follows. A is a

fuzzy truth-value if and only if A is a fuzzy set on [0,1] and L be the set of all fuzzy values, that is L = {a | a

is fuzzy set on [0,1]}The same can be graphically written as follows

(a) Numerical Truth Values (b) Interval Truth Values (c) Fuzzy Truth ValuesFigure 4.1 Truth Values in Fuzzy Logic

9

-1

0

0 1 0 a b 1 0 1


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4.9 Learning in Fuzzy Systems

Generally learning can be well or can be bad. But one cannot learn without changing, and we cannot

change without learning. Learning laws describe the synaptic dynamical system, how the system encodes

information. They determine how the synaptic web process unfolds in time as the system samples newinformation. This is one way neural network compute with dynamical systems.

Fuzzy systems learn associative rules to estimate functions or control systems through unknown

probability (sub set hood) function p (x). The probability density function p (x) describes a distribution of

vector patterns or signals X, a few of which the neural or fuzzy systems sample. When a neural or fuzzy

system estimates a function f: X Y, it in effect estimates the joint probability density P (x, y). Then

solutions points (X, f (x)) should reside in high probability regions of the input/ output product space X x Y.

An unsupervised learning systems process each sample X but does not know" that X belongs to class Di

and not to class Dj. Supervised learning use class-membership information and unsupervised learning used

unlabelled samples.

4.10 Fuzzy Logic Controllers (FLC)

Fuzzy systems, utilizing neuristic knowledge, have been employed very effectively as controllers

popularly known as Intelligence Control. Design Problems of FLC are

1) Define Input and Output variables that are determined which status of the process shall be observed and

which control actions are to be considered.

2) Define the condition interface, that is, fix the way in which observations of the process are expressed as

fuzzy sets.

3) Design the rule base, which is, fixed the way in which observations of the process are expressed as fuzzy

sets.

4) Design the computational unit, that is, supply algorithm to perform fuzzy computations those will

generally lead to fuzzy outputs.

5) Determine rules according to which fuzzy control statements can be transformed into crisp control

actions. (Defuzzification).

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Chapter 5 SUBSTATION

_________________________________________________________________

A substation is a part of an electrical generation, transmission, and distributionsystem. Substations

transform voltage from high to low, or the reverse, or perform any of several other important functions.Electric power may flow through several substations between generatingplant and consumer, and its voltage

may change in several steps. A substation that has a step-up transformer increases the voltage while

decreasing thecurrent, while a step-down transformer decreases the voltage while increasing the current for

domestic and commercial distribution. The word substation comes from the days before the distribution

system became a grid.

Substations generally have switching, protection and control equipment, and transformers. In a large

substation,circuit breakers are used to interrupt any short circuits or overload currents that may occur on thenetwork. Substations themselves do not usually have generators, although a power plant may have a

substation nearby. Earth faults at a substation can cause a ground potential rise. Currents flowing in the

Earth's surface during a fault can cause metal objects to have a significantly different voltage than the ground

under a person's feet; this touch potential presents a hazard of electrocution.

5.1 Transmission Substation

A transmission substation connects two or more transmission lines. The simplest case is where alltransmission lines have the same voltage. In such cases, the substation contains high-voltage switches that

allow lines to be connected or isolated for fault clearance or maintenance. A transmission station may have

transformersto convert between two transmission voltages,voltage control/power factor correction devices

such as capacitors, reactors orstatic VAR compensators and equipment such asphase shifting transformers

to control power flow between two adjacent power systems. Modern substations may be implemented using

International Standards such as IEC61850.

5.2 Distribution Substation

A distribution substation transfers power from the transmission system to the distribution system of

an area. It is uneconomical to directly connect electricity consumers to the main transmission network,

unless they use large amounts of power, so the distribution station reduces voltage to a value suitable for

local distribution. The input for a distribution substation is typically at least two transmission or sub

transmission lines. Input voltage may be, for example, 115 kV, or whatever is common in the area. The

output is a number of feeders. Distribution voltages are typically medium voltage, between 2.4 and 33 kVdepending on the size of the area served and the practices of the local utility.

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Chapter 6 INTELLIGENT SUBSTATION

_________________________________________________________________

6.1 Concept Of Intelligent Substations

In conventional substations, apparatus such as switchgear and transformer, control, protection and

monitoring equipment is independent of every other device, and connection is based on the signals coming

through the cable. On the other hand, an intelligent substation shares all information on apparatus, control,

protection, measurement and apparatus monitoring equipment through one bus by applying both digital

technology and IT-related technology.

Moreover, high efficiency and miniaturization can be achieved because the local cubicle contains

unified control/protection and measurement equipment that is one integrated system (Fig 6.1). Since an

optical bus shares the information between the apparatus and equipment, the amount of cable is sharply

reduced.

6.2 Apparatus Monitoring System

All the data from each monitoring and measuring device is transmitted and used for a higher-level

monitoring system via an optical bus. The required data is accessed through the Intranet or the Internet at the

maintenance site of an electricity supply company or a manufacturer and the apparatus can be monitored

from a remote location. The construction, analysis and diagnosis of the database including trend

management and history management also become possible. As a result, signs of abnormalities can be

checked out well in advance, and prompt action can be taken in times of emergency.

Maintenance plans can also be drafted to ensure reliability, by inspecting revision description and

parts management, efficient maintenance planning and reliability maintenance are also realized

simultaneously.

Intelligent system techniques may be of great help in the implementation of area power systemcontrols. Most of these applications require large quantities of system information, which can be provided by

modern telecommunications and computing technology, but require new processing techniques able to

extract salient information from these large sets of raw data. Importantly, such large data sets are never error

free and often contain various types of uncertainty. Finally, control actions may be based on operating

strategies specified in qualitative form, which need to be translated into quantitative decisions.

An important aspect to be considered in the implementation of power systems controls is that, in the

restructured power system environment, several of these activities will fall under the category of ancillaryservices. Therefore, besides the technical issues, economic and financial infrastructure should be taken into

account in the design and implementation of control schemes.

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Fig.6.1 Intelligent Substation System Configuration (Image). The whole substation system is combined by

optical LAN, and apparatus composition is simplified.

6.3 Power System Controls

Power system controls can be broadly classified into two categories: local and area (regional/system-

wide). The boundary between these two categories is not precise as area controls are often implemented by

optimally adjusting local control parameters and set points. Area controls main characteristic is the need to

process information gathered at various points of the network and to model the behavior of large parts of the

power system. This type of control is usually not limited to the automatic feedback type but often includes

strategies based on empirical knowledge and human intervention. Local control, on the other hand, is

typically implemented using conventional automatic control rules, such as, PID control, which are believed

to offer adequate performance in most applications. Still, this is not to discount the usefulness of new

intelligent methodologies, such as, fuzzy logic controllers, for local controls.

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For convenience, power system higher level controls are classified here as:

Generation scheduling and automatic control: includes unit commitment, economic dispatch, and

automatic generation control; in the past, well established control methods were used but this situation

has been changing to deal with the new scenario created by the power industry restructuring;

Voltage control: is mostly of the local type but some systems have already gone to a higher coordinated

secondary control to allow a more effective use of reactive power sources and increase stability margins;

Preventive security control: has the objective to detect insecure operating points and to suggest

corrective actions; the grand challenges in this area are on-line Dynamic and Voltage Security

Assessment (DSA and VSA);

Emergency control: manages the problem of controlling the system after a large disturbance; it is an

event driven type of control and includes special protection schemes;

Restorative control: its main function is to re-energize the system after a major disturbance followed by

a partial or total blackout.

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CHAPTER 7 DEVICES CONTRIBUTING TO INTELLIGENT SYSTEM

_________________________________________________________________

7.1 Switchgear And Transformer

The burden can be drastically decreased because the sensor signal from the PCT is digitized at the

sensor output edge and the load on the PCT only reaches that of an A-D (analog-to-digital) converter.

Rogowski coils are used as the current sensors and capacitive potential dividers are used as the voltage

sensors. These sensors drastically reduce the size of the switchgear (Fig 7.1).

Fig 7.1 Gas Combined Switchgear Miniaturization by Digital Correspondence Sensor. 550-kV GCS (gas

combined switchgear)

GCB: gas circuit breaker

CT: current transformer

PT: potential transformer

Present studies on miniaturizing conventional equipment have so far been aimed at standardizing series.

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7.2 Protection And Control

Intelligent substations require protection and control equipment to be installed outdoors and this

needs to be compact so that the local cubicle is able to contain them. Outdoor installation requiresimprovements in insulation against heat and air tightness besides parts reliability. Compact protection and

control equipment will generate demand for unified fabrication of protection/control and high-density

components.

7.2.1 Trends In Protection And Control Systems

Due to the rapid progress in todays information field, applying digital technology and adding IT

function to the protection/control system are possible, to support stable power supply, and improvemaintenance. In Japanese protection/control systems, digitization has made advances since the last half of

the 1980s. Digital technology has unique advantages, namely minimizing maintenance and improving

reliability, and it has speeded up the conversion from individual analog-type to digital-type relays. Now,

however, digitization is not only required for independent single-function equipment, but for the systematic

operation and employment of the whole substation. Such systems have greatly improved efficiency in

employment and maintenance using IT. The key phrases to fulfill these needs are as follows:

(1) Slimming of total system as a protection control equipment

Unification of equipment

(2) High efficiency of employment/maintenance support using IT technology

Extended employment/maintenance by remote control

(3) System directly linked to the equipment for protection/control

Distributed installation near the apparatus

Thus, there has been a need for constructing a high efficiency system through system-wide miniaturization

and integration of IT.

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hhhhh.

17

Figure 1Highly flexible and intelligent energy system infrastructures are required to facilitate substantially

higher amounts of renewable energy than todays energy systems and thereby lead to the necessary COreductions as well as ensuring the future security of energy supply in all regions of the world.

Information And

Communication Technologies

Internet and Satellites

+Traditional PowerSystem structure

Links between the intelligent infrastructure and the traditionalpower system structure are the basis for the future flexible andintelligent energy system

Power plants/CHP HV transmission Transformer LV transmission End use

Intelligent, two way communication between suppliersand end-users together with distributed generationfurther enhances the flexibility Distributed generation and

efficient building systems

Internet

Smart end-usedevices

Distributedgeneration &storage

Controlinterface

Renewables

Data management Plug-in hybrids

Dynamicsystemscontrol

Utility

Communi-cations PV

Distributionoperations

Intelligent Energy

System

=


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Fig 7.2 Intelligent Energy System

CHAPTER 8 APPLICATION OF NEURAL NETWORKS

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Wireless Communication


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Neural Networks have been used in a board range of applications including: pattern classification,pattern recognition, optimization, prediction and automatic control. In spite of different structures andtraining paradigms, all NN applications are special cases of vector mapping. The following fields hasattracted the most attention in the past five years:1-Load Forecasting2-Fault Diagnosis/Fault Location3-Economic Dispatch4-Security Assessment5-Contingency Screening

Fig 8.1 Neural networks applications in power systems-April 2005

8.1 Load Forecasting

Commonly and popular problem that has an important role in economic, financial, development,expansion and planning is load forecasting of power systems. The availability of historical load data on theutility databases makes this area highly suitable for ANN implementation.

Short-term load forecasting over an interval ranging from an hour to a week is important for variousapplications such as unit commitment, economic dispatch, energy transfer scheduling and real timecontrol.

Mid-term load forecasting that range from one month to five years, used to purchase enough fuel forpower plants after electricity tariffs are calculated

.

Long-term load forecasting (LTLF), covering from 5 to 20 years or more, used by planningengineers and economists to determine the type and the size of generating plants that minimize bothfixed and variable costs.

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Fig 8.2 Types of load forecasting that done with NN

8.2 Fault Diagnosis/Fault Location

Fig 8.3 Fault Diagnosis process

Progress in the areas of communication and digital technology has increased the amount of

information available at supervisory control and data acquisition (SCADA) systems. Although information is

very useful, during events that cause outages, the operator may be overwhelmed by the excessive number of

simultaneously operating alarms, which increases the time required for identifying the main outage causeand to start the restoration process. Besides, factors such as stress and inexperience can affect the operators

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1PREPROCESSING

2DISTURBANCEDETECTION ANDCLASSIFICATION

FAULT DIAGNOSIS

3HYPOTHESISGENERATION

4HYPOTHESISJUSTIFICATION


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performance; thus, the availability of a tool to support the real-time decision-making process is welcome.

The protection devices are responsible for detecting the occurrence of a fault, and when necessary, they send

trip signals to circuit breakers (CBs) in order to isolate the defective part of the system.

However, when relays or CBs do not work properly, larger parts of the system may be disconnected.

After such events, in order to avoid damages to energy distribution utilities and consumers, it is essential to

restore the system as soon as possible. Nevertheless, before starting the restoration, it is necessary to identify

the event that caused the sequence of alarms such as protection system failure, defects in communication

channels, corrupted data acquisition.

The heuristic nature of the reasoning involved in the operators analysis and the absence of an

analytical formulation, leads to the use of artificial intelligence techniques. Expert systems, neural networks,

fuzzy logic, genetic algorithms (GAs), and Petri nets constitute the principal techniques applied to the fault

diagnosis problem.

8.3 Economic dispatch

Main goal of economic dispatch (ED) consists of minimizing the operating costs depending on

demand and subject to certain constraints, i.e. how to allocate the required load demand between the

available generation units. In practice, the whole of the unit operating range is not always available for load

allocation due to physical operation limitations. Several methods have been used in past for solving

economic dispatch problems including Lagrangian relaxation method, linear programming(LP) techniques

specially dynamic programming(DP), Beales quadratic programming, Newton-Raphsons economic

method, Lagrangian augmented function, and recently Genetic algorithms and NNs. Because of, economic

dispatch problem becomes a non convex optimization problem, the Lagrangian multiplier method, which is

commonly used in ED problems, can not to be directly applied any longer.

Dynamic programming approach is one of the widely employed methods but for a practical-sized

system, the fine step size and the large units number often cause the curse of dimensionality'. Main

drawbacks of genetic algorithm and tabu search for ED are difficult to define the fitness function, find the

several sub-optimum solutions without guaranty that this solution isn't locally and longer search time.

Neural networks have a well-demonstrated capability of solving combinational optimization

problem. Because of this networks capability to consider all constrained limitation such as transmission line

loss and transmission capability limitations, penalty factor when we have special units, control the units

pollutions and etc., caused increasing the paper proposed recently. Recently attractive tools for ED are

neural network based on genetic algorithm and fuzzy systems.

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8.4 Security Assessment

Security of a power system is the ability to sustain, without any abnormalities, the worst impending

contingency. Security assessment has been at the forefront of ANN applications from the beginning. The

goal of security assessment is to supply the operating state so that suitable preventive actions can beundertaken. In one of the early approaches, Sobajic and Pao synthesized one of the crucial parameters of the

system, the critical clearing time (CCT). The principle task of an electric power system is to deliver the

power requested by the customers, without exceeding acceptable voltage and frequency limits. This task has

to be solved in real time and in safe, reliable and economical manner.

Fig 8.4 Data flow in power System Operation

Generally there are two types of security assessments:

static security assessment and dynamic security assessment. In both types different operational states are

defined as follows: Normal or secure state: In the normal state, all customer demands are met and operating limit is within

presented limits.

Alert or critical state: In this state the system variables are still within limits and constrain are satisfied,

but little disturbance can lead to variable toward instability.

Emergency or unsecure state: the power system enters the emergency mode of operation upon violation of

security related inequality constraints.

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8.5 Contingency Screening

To assess system security, a huge number of possible contingencies are to beevaluated and ranked.

Conventional ranking methods suffer from masking & longcomputing time. Since a systems operational

history is available in most utility databases, it should be possible to group contingencies into varioussubclasses. However it is impossible to generate enough training sets to cover the entire range of power

system operation. The optimization method used linear programming techniques to maximize the

probability of correct classification of contingencies. This implementation classifies contingencies

according to the number and type of limit violations. The method has interesting applications in combining

security monitoring and preventive control.

The training process involves the following steps:

1. Selection of input/output parameters for training.

2. Generation of training data.

3. Normalization of training data

4. Testing of the network with unknown set of data

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CHAPTER 9 APPLICATION OF FUZZY LOGIC

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In power systems operation, economy and security, maximum load supply and minimum generating

cost are conflicting objectives. The combination of these objectives by weighing coefficients is the

traditional approach to solve this problem. Fuzzy theory offers better compromise and obtain solutions

which cannot be found by weighing methods. The benefits of fuzzy set theory over traditional methods are

as follows:

(1) It provides alternatives for the many attributes of objective selected.

(2) It resolves conflicting objectives by designing weights appropriate to a selected objective.

(3) It provides capability for handling ambiguity expressed in diagnostic process which involves symptoms

and causes.(4) It develops process control as fuzzy relation between information about the condition of the process to be

controlled.

(5) It develops intelligent robots that employ sensors for path or position determination.

(6) It improves human reliability models in cases where many people perform multiple tasks. The areas

where fuzzy logic can be used in power systems cover all the aspects of the power system:

9.1 Reactive Power and Voltage ControlReactive sources which are spread throughout the system should be controlled accurately based on

the loading conditions (light load or peak load) to optimize and ensure the security of electric power

transmission system. These controls are known as voltage/reactive power or voltage/VAR control. The aim

of these controls is to reduce voltage deviations or minimum losses or enhancing voltage. Main types of

voltage/ VAR problems are

1. Planning of system reactive demands and control facilities as well as installation of reactive power control

resources.2. The operation of existing voltage/VAR resources and control device.

Fuzzy set theory has been applied off late for reactive power control with the purpose of improving

the voltage profile of power system. Here the voltage deviation and controlling variables are translated into

fuzzy set notations to formulate the relations between voltage deviation and controlling ability of controlling

device. Main control variables are VAR compensators, transformer taps and generator excitation. A fuzzy

rule system is formed to select these controllers, their movement and step size. The controllers are selected

based on

1. Local controllability towards a bus having unacceptable voltage.

2. Overall controllability towards the buses having poor voltage profile.

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9.2 Fuzzy logic in Control areas

Fuzzy logic has been used in control area by using fuzzy logic stabilizer, converter/drives and in

other types of control. In order to design a robust controller for the auxiliary control loop of static VAR

system, both fuzzy logic and variable structure system concepts are used. The design of a simple fuzzycontroller using the least number of rules for stabilization of a synchronous generator connected to a large

power system gives a superior results compared to conventional control in better damping during transient

disturbances. In order to enhance voltage security of an electric power system, fuzzy set theory for voltage

reactive control of power system is use by translating voltage bus voltage and s\controlling variables into

fuzzy set notations to formulate the relation between voltage violation level and controlling ability of

controlling devices. Max- Min method is employed on the fuzzy sets in accordance with requirement of real-

time control. By Fuzzification the bus voltage violation level and controlling ability of controlling devices to

essentially reflect the operators intuition in operation , the aim of enhancing the control effects is achieved.

This is to simulate the usual action of the operator if he is not satisfied with the grading of the fuzzy model,

he can adjust the parameters used in the definition of the membership functions, so that his desire will be

closely matched. Conventional Optimal Power Flow solutions utilize standard techniques. These techniques

limit the practical value and scope of optimal power flow applications. Different considerations have to

make a trade-off between minimum objective function, satisfying constraints and desirable moving control

variables. In real-life system, it has been found that a slight violation of the normal operation limits may

result in significant cost saving. Fuzzy logic can reach the trade-off in a better way using eg. Min-Max

techniques.

Demand side management programs are strategies designed to alter the shape of the load curve. In order

to successfully implement such a strategy, customer acceptance of the program is vital. Thus it is desirable to

design a model for direct load control which may accommodate customer preferences. Fuzzy logic may be

used to optimize both customer satisfaction and utility unit commitment savings based on a fuzzy load

model for the direct load control of appliances.

9.3 Dissolved Gas Analysis

A Periodic maintenance of large power transformers fault diagnosis system has been proposed and

implemented of fuzzy diagnosis to improve the conventional DGA methods. DGA technology is approved as

applicable to discover internal latent transformer failure and its development trend. Therefore, whether in the

domestic or international arena, DGA technology has secured a significant position in the rank of preventive

testing of electrical equipments. The fuzzy logic analysis involves three successive processes, namely:

Fuzzification, Fuzzy Inference and Defuzzification. In the process of designing a transformer fault

diagnostic system, the uncertainly shows the following two characteristics-

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(i) A great number of test data (including the preventive test and other tests), some expert experience

and some criteria which can be directly numerated should be translated into fuzzy numbers. This process is

called numerical uncertainty translation.

(ii) Some expert experience and some criteria which are expressed in linguistic language cannot be

numerated directly. They also need to be changed into fuzzy numbers. The process is called linguistic

uncertainty translation.

In fuzzy diagnosis system we take first, the associated membership functions of fuzzy subsets were

determined empirically or basically in a trial-and-error manner, while the conventional DGA diagnosis

criteria were to be implicitly obeyed. And second step, a great number of previous diagnosis records of

dissolved gas were mainly employed as a test purpose rather than as a development basis of the fuzzy

diagnosis system.

The information inherently contained in the numerical data was not fully utilized in establishing the

diagnosis system. And the last step, due to the wide variety of conditions which affect the results of DGA,

the diagnosis system should be continuously maintained or modified according to the cases newly obtained.

However, manual knowledge acquisition and knowledge base revision out of the large numerical data are

quite tedious, often resulting in a lengthy process of generating the fuzzy if-then rules and the membership

functions of the corresponding fuzzy subsets.

The approach is based on the interpretation of DGA data using Fuzzy Logic (FL). The proposed

diagnostic method adopts indicators related to the ratios C2H4/C2H6, C2H2/C2H4 CH4/H2 and to the

concentration of specific gases such as hydrogen, carbon monoxide, methane, ethane, ethylene, acetylene.

Different combinations of these four codes represent different fault patterns, overheating, arcing, and

corona. Fuzzy Analysis an integrated analysis, it can diagnose the fault of transformer effectively and

manage the data of DGA in oil of transformer.

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Fig 9.1 Structure of the Fuzzy Diagnosis System

9.4 Fuzzy Logic in Power Electronics

The perspective of extensive use of AI tools, such as expert system, fuzzy logic , neural networks and

genetic algorithms, are expected to usher a new era in power electronics and motion control in the coming

decades. In spite of AI progress, their applications in power electronics is just at it beginning. Logic

controllers have witnessed quite a number of applications of fuzzy logic. A rule based fuzzy logic controller

to control output power of a pulse width modulated (PWM) inverter used in standalone wind energy

conversion scheme has been used.

The self excited induction generator used has the inherited problem of fluctuations in the magnitude

and frequency of its terminal voltage with changes in wind velocity and load. To overcome this drawback

the variable magnitude, variable frequency voltage at the generator terminals is rectified and the power is

27

ICE/IEEE Transformer

DGA Criteria

Set of membership functionand knowledge based rule

Data base of Dissolvedgas in oil

Fuzzification Rule Base FuzzyInterface System

Diagnosis Result

Defuzzification


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transferred to the load through a PWM inverter. In order to extract maximum power from the wind energy

system and transfer it to the load, a fuzzy logic controller has to be provided to regulate the modulation

index of the PWM inverter based on the input signals. By fuzzifying these signals and the use of rules based

on these fuzzified signals, the fuzzy control is performed giving the fuzzy output required after

Defuzzification. This will provide an optimum utilization of the wind energy.

9.5 Generator Operation and Control

The major application lies in the control of excitation system of the Synchronous Generator.

Synchronous Generator excitation control is one of the most important measures to enhance power system

stability and to guarantee the quality of the electrical power it provides. A number of new control theories

have been introduced to design high performance excitation controllers. Among them the linear optimal

control theory, the adaptive control theory the fuzzy logic control theory and the nonlinear control theory are

the most commonly used ones. Fuzzy logic Controllers are advantageous in many respects. They are simple

in structure and relatively easy to realize.

Mathematical models of the control systems are not required. Variations of the parameters and

operation conditions of the controlled systems do not significantly affect the performance of the controller.

All of these advantages have enabled this technique to attract more and more attention in recent years. Jinyu

Wena, O.P. Malik et al suggested a method to design the FLC based on Genetic Algorithm. In this controller

the generator terminal voltage and the rotor speed deviation are used as its inputs. As a result, both the

voltage profile and the dynamic stability of the generating unit are enhanced. Also FLC design has been

carried out by G.A. Chown, R.C. Hartman et al for Automatic Logic Controller (AGC). The main problem

solved by this method is the secondary frequency controller and AGC. The fuzzy controller was

implemented in the control ACE calculation, which determines the shortfall or surplus generation unit that

has to be corrected. Short term generation scheduling with take-or-pay fuel contract was developed by Kit

Po Wong and Suzannah Yin Wa Wong et al in which a fuzzy set approach is developed to assist the solution

process to find schedules which meet as closely as possible the take-or-pay fuel consumption. This

formulation is then extended to the entire economic dispatch problem when the fuel consumption is higher

than the agreed amount in the take-or pay contract.

The extended formulation is combined with the genetic algorithms and simulated- annealing

optimization methods for the establishment of new algorithms for the problem. Stabilizer control and the

exciter and governor loops using fuzzy set theory and the Neural nets was developed by M.B. Djukanovic

and M. S. Calvoic. Here a design technique for the new hydro power plant controller using fuzzy set theory

and ANN was developed. The controller is suitable for real time operation, with the aim of improving thegenerating unit transients by acting through the exciter input, the guide vane and the runner blade positions.

The developed fuzzy logic controller, whose control signals are adjusted using the on-line measurements,28


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can offer better damping effects for generator oscillations over a wider range of operating conditions than

conventional regulators. The FLC, based on a set of fuzzy logic operations that are performed on controller

inputs, provides a means of converting linguistic control requirements based on expert knowledge into an

efficient control strategy. Using unsupervised learning of ANN generates a fuzzy associative matrix.

kdr report (repaired)

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