SWITCH GEAR AND PROTECTION

STUDY REPORT ON

ADAPTIVE RELAYING

Submitted in partial fulfillment of the requirements for the award of the degree of

Bachelor of Technology

In

Electrical Engineering.

Under the guidance of

Dr.Ashok S

By

NAME ROLL NO.

SURABHI VASUDEV (B110556EE)

Department of Electrical Engineering

NATIONAL INSTITUTE OF TECHNOLOGY CALICUT

DECEMBER 2014

ABSTRACT

A reliable, continuous supply of electrical energy is essential for the functioning

of today's modern complex and advanced society. Electricity is one of the prime

factors for the growth and determines the value of the society.

Conventional Power System analysis become difficult due to:

1. Complex versatile and large amounts of data that are used in calculation,

diagnosis and learning.

2. The increase in the computational time period and the accuracy due to

extensive system data handling.

The modern power system operates close to their limits due to the increasing

energy consumption and impediments of various kinds, and the extension of

existing electric transmission networks. This situation requires a significantly less

conservative power system operation and control regime which, in turn, is

possible only by monitoring the system states in much more detail than was

necessary previously.

Sophisticated computer tools have become predominant in solving the

difficultproblems that arise in the areas of Power System planning, operation,

diagnosis and design of the systems. Among these computer tools Artificial

Intelligence has grown extensively in recent years and has been applied in the

areas of the power systems. The most widely used and important ones of Artificial

Intelligent tools, applied in the field of Electrical Power Systems are the Artificial

Neural networks and the so-called Fuzzy systems. The details of the important

applications are discussed. Finally the major achievements of this soft computing

technique in power system areas are commented and the future scopes of these

methods in the modern power system are analyzed.

CONTENTS

CHAPTER TITLE PAGE NO.

ABSTRACT

1 INTRODUCTION 1

2 ARTIFICIAL INTELLIGENCE METHODS 2

3 ANALYSIS OF THE TECHNIQUES 4

4 APPLICATIONS 9

5 CONCLUSION 14

REFERENCES 15

1. INTRODUCTION

The microprocessor technology brings unquestionable improvements of the

protection relays- criteria signals are estimated in a shorter time; input signals are

filtered-out more precisely; it is easy to apply sophisticated corrections;the

hardware is standardized and may communicate with other protection and

control systems; relays are capable of self-monitoring. All this, however, did not

make a major breakthrough in power system protection as far as

security,dependability and speed of operation are considered. The key reason

behind this is that the principles used by digital relays blindly reproduce the

criteria known for decades.

The relaying task, however, may be approached as a pattern recognition problem

- by monitoring its inputs, the relay classifies on-going transients between internal

faults and all the other conditions. Or, the protective relaying may be considered

as a decision making problem - the relay should decide whether to trip or retrain

itself from tripping. This observation directly leads to AI application in power

system protection . Practically, it includes the artificial neural network approach

(pattern recognition), as well as the expert system and fuzzy logic methods

(decision making).

Thus three major families of AI techniques are considered to be applied in

modern power system protection :

• Expert System Techniques (XPSs),

• Artificial Neural Networks (ANNs),

• Fuzzy Logic systems (FL).

2.ARTIFICIAL INTELLIGENCE METHODS

AI is a subfield of computer science that investigates how the thought and action

of human beings can be mimicked by machines . Both the numeric, non-numeric

and symbolic computations are included in the area of AI. The mimicking of

intelligence includes not only the ability to make rational decisions, but also to

deal with missing data,adapt to existing situations and improve itself in the long

time horizon based on the accumulated experience.

A. Expert Systems

The first expert systems included a few heuristic rules based on the expert's

experience. In such systems, the knowledge takes the form of so called

production rules written using the If... then... syntax (knowledge base). The

system includes also the facts which generally describe the domain and the state

of the problem to be solved (data base).A generic inference engine uses the facts

and the rules to deduce new facts which allow the firing of other rules. The

knowledge base is a collection of domain-specific knowledge and the inference

system is the logic component for processing the knowledge base to solve the

problem. This process continues until the base of facts is saturated and a

conclusion has been reached .To guide the reasoning and to be more efficient,

these systems may incorporate some strategies known as meta knowledge. Rule

based systems represent still the majority of the existing expert systems. There

are few applications of XPS to power system protection reported, but all of them

solve the off-line tasks such as settings coordination, post-fault analysis and fault

diagnosis . As yet there is no application reported of the XPS technique employed

as a decision making tool in an on-line operating protective relay. The basic

reason for this is that there is no extensive rule base that describes the reasoning

process applicable to protective relaying. Instead, only a few rules or criteria are

collected .

B. Artificial Neural Networks

The ANNs are very different from expert systems since they do not need a

knowledge base to work. Instead, they have to be trained with numerous actual

cases. An ANN is a set of elementary neurons which are connected together in

different architectures organized in layers what is biologically inspired .An

elementary neuron can be seen like a processor which makes a simple non linear

operation of its inputs producing its single output. A weight (synapse) is attached

to each neuron and the training enables adjusting of different weights according

to the training set. The ANN techniques are attractive because they do not

require tedious knowledge acquisition, representation and writing stages and,

therefore, can be successfully applied for tasks not fully described in advance. The

ANN are not programmed or supported by a knowledge base as are Expert

Systems. Instead they learn a response based on given inputs and a required

output by adjusting the node weights and biases accordingly.The speed of

processing, allowing real time applications,is also an advantage.

C. Fuzzy Logic

The fuzzy logic approach to protective relaying assumes :

• The criteria signals are fuzzified in order to account for dynamic errors of the

measuring algorithms. Thus, instead of real numbers, the signals are represented

by fuzzy numbers. Since the fuzzification process provides a special kind of flexible

filtering, faster measuring algorithms that speed up the relays may be used.

• The thresholds for the criteria signals are also represented by fuzzy numbers to

account for the lack of precision in dividing the space of the criteria signals

between the tripping and blocking regions.

• The fuzzy signals are compared with the fuzzy settings.The comparison result is

a fuzzy logic variable between the Boolean absolute levels of truth and false.

• The tripping decision depends on multi-criteria evaluation of the status of a

protected element. Additional decision factors may include the amount of

available information,or the expected costs of relay maloperation.

3.ANALYSIS OF THE TECHNIQUES

3.1 Neural Network based Applications

The most of the applications related to neural network is based on multilayer

perceptron. Here the error back scheme is widely used. Fundamental aspects of

Multilayer Perceptron networks are random initial start up state and convergence

of connection weights to produce minimum error. However there are no set rules

for parameter selection associated with these algorithms. So in using ANN models

some trial and error is required.

3.1.1 Design of Network

As discussed in practical applications Multilayer Perceptron with at least one

hidden layer is used. It has been reported that using greater number of hidden

layer improve the overall performance. But some experimentation is required to

select the number of hidden layers and nodes. Generally at least twice of as many

nodes in the hidden layer has been taken as Inputs.

Some of the researchers gave an empirical formula as H = ni (ni-1) to calculate

hidden layer where 'H' is the number of the hidden layer and 'n i' the input. But

still some trial and error is needed to produce quick convergence and acceptable

results.

The introduction of the concept of structured ANNs (e.g.Perceptrons, Hopfield

Network, and SOM) designed for specific tasks simplify the design process. Also

research results are available for dynamically designs hidden layers. Cascaded

correlation's begins with minimal network, then automatically trains and adds

new hidden units one by one. Once the hidden layer is added it becomes a

permanent feature detector in ANN. This architecture learns quickly.

3.1.2 Training Set Generation

In many applications, there is no efficient way of generating a complete training

set to cover all possible operating states. This will be of greater concern in dealing

with a problem of large on line data handling. For example, In the cases of power

system security problem most of the literatures reports about offline simulation

to obtaining the training sets. It is possible to analyze if the samples chosen are

small in size. If the sample is large (500 buses, which are the case of the practical

system,) the analysis will be extremely difficult. Moreover its not easy to obtain

good performance on training data followed by much worse performance on test

data. There can be improvement if some knowledge can be incorporated about

the domain into the network architecture.

3.1.3 Hopfield Network

Hopfield Networks can be very useful in solving the optimization problems very

quickly and efficiently by minimizing energy function, defined in terms of its

weights and thresholds. However, this energy function has many local minima.

This is not acceptable especially in contingency screening. The reason is that we

should get the best rather than the feasible ranking of contingencies. Another

drawback is that the weights and thresholds are calculated based on the

optimization process, which has to be repeated if any of the input parameters

change.The enhancement in the recent development of the architecture reduces

thesedrawbacks. Also a mapping method is formulated from which the weights

and thresholds forthe particular optimization problem can be easily computed.

3.1.4 Training the Inputs

Many of the ANN models (like perceptron, SOM, ART Networks heavily rely on the

information retained to the input features. In any power system applications the

input patterns space consists of a large number of features. So feature selection is

necessary to reduce this pattern space to a reasonable size. These processes

make loss of information.

3.1.5 Knowledge Consistency and Interaction with the User

Knowledge Consistency is an important concern in the training set of ANN

research.The AI implementations are considered complete when they match with

human competence and thus further research is needed in this area.

In many cases AI technique is required to interact to demonstrate the validity of

the decision to the User. For example in the diagnosis of faults in the system, the

operator might want to ascertain the validity of the reasoning employed. Similarly

in preventive control an explanation might be necessary to validate and verify the

control strategy.

3.1.6 Practical Implementation

In the hardware part most of the present day ANN schemes are single-processor

simulations of the massively parallel ANN models. When using the multilayer

perceptron model, most of the implementations use a sequential algorithm on

conventional computer to train the ANN, in node by node manner. Ideally ANN

schemes should be implemented in parallel processing machines to fully reap the

benefits of their massively parallel structure. There is mainly two way of

implementation of ANN in the parallel computers.

1. Direct Implementation in which there is a physical-processing element for each

neuron in the neural network. This approach can potentially provide a very good

performance.However it can support only a specific ANN model since it is fixed in

the hardware.

2. Virtual implementations (with general-purpose neuro computer) in which a

processingelement takes charge of multiple neurons and simulates them in a

time-sharing fashion.

3.2 Fuzzy Logic

3.2.1Requirements of Fuzzy based Applications

The main characteristics and requirement for a problem suitable for fuzzy logic

applications are

1. The problem has to be solved by human experts for daily operation and

planning. Thusfunctional knowledge in terms of heuristic rules are available.

2. If the methodology cannot be expressed in terms of mathematical form.

3. If the modeling of mathematical problem requires various many assumptions to

be made,leading to an inaccurate models.

4. If the problem involves uncertainty, vague constraints and/or multiple

conflicting objectives.

3.2.2 Advantages of Fuzzy Logic Applications

The main advantages of the fuzzy systems are

1. Speed

2. Computationally less expensive and simpler tools.

3. Flexibility

4. Ease of computation

Creation of fuzzy logic

Creation of fuzzy logic is mostly through experts, which lacks in knowledge

engineering.That means it depends on expert opinion and cannot decide the rule

networks Genetic Algorithms and fuzzy clusters.

Common sense knowledge Representation

It’s difficult to represent and manipulate common sense knowledge and there are

no effective and sufficient methods to do so.

Fuzzy Logic Controller Stability

Stability of the FLC cannot be assessed and there are no established methods to

do that. This needs to be analyzed before they can be considered as alternative

for conventional controller.

Fuzzy inference allows to approximate nonlinear functions with finite fuzzy

rules.The main advantage of a rule-based system over the neural network is to

capture cause and effect in the inference process. Each subspace is described by a

fuzzy if-then rule based on the patterns of training set as shown in fig.3.2.1 in the

application of transformer fault diagnosis.

Fig.3.2.1.fuzzy subspaces with membership functions

4.APPLICATIONS

4.1.Transformer Differential Relaying

Conventional differential relays may fail in discriminating between internal faults

and other conditions (inrush current, over-excitation of core, CT saturation, CT

ratio mismatch, external faults,..).Detection of 2nd

and 5th

harmonics is not

sufficient (harmonics may be generated during internal faults)by ordinary relays.

Multi-Criteria Differential Relay based on Self-Organizing Fuzzy Logic is used.

� One differential relay per phase.

� 12 criteria are used and integrated by FL.

� Examples of criteria: (ID=differential current)

4.2.Distance Relaying

Changing the fault condition, particularly in the presence of DC offset in current

waveform, as well as network changes lead to problems of underreach or

overreach.Conventional schemes suffer from their slow response. Using ANN

schemes with samples of V&I measured locally, while training ANN with faults

inside and outside the protection zone.Same approach but after pre-processing to

get fundamental of V&I through half cycle DFT filter.Combining conventional with

AI: using ANN to estimate line impedance based on V&I samples so as to improve

the speed of differential equation based algorithm.

Pattern Recognition is used to establish the operating characteristics of zone-I.

The impedance plane is partitioned into 2 parts: normal and fault. Pre-classified

records are used for training.Application of adaptive distance relay using

ANN,where the tripping impedance is adapted under varying operating

conditions. Local measurements of V&I are used to estimate the power system

condition.

4.3.Transmission Line Fault Classification

Conventional schemes: cannot adapt to changing operating conditions, affected

by noise& depend on DSP methods (at least 1-cycle).Single-pole

tripping/autorecloser SPAR requires the knowledge of faulted phase (on detecting

SLG Single-pole tripping is initiated, on detecting arcing fault recloser is initiated).

The adaptiveness is ,hence,incorporated as in fig.4.3.1.The ANN topology and the

relaying scheme are shown in fig.4.3.2 and fig.4.3.3 respectively.

Fig.4.3.1.AI based transmission line

Fig 4.3.2 ANN topology

Fig.4.3.3 .Relaying scheme

4.4.Machine Winding Protection

If the generator is grounded by high impedance, detection of ground faults is not

easy (fault current < relay setting).Conventional algorithms suffer from poor

reliability and low speed (1-cycle). So,adaptive relay is made as per the algorithm

in fig.4.4.1.

Fig.4.4.1

4.5.Fault Diagnosis

ANN’s has recently invaded fault diagnosis, which has been a traditional area for

ES(expert system) implementation. However, at present the ES implementations

outnumber the ANN implementations. The explanatory abilities of ESs and their

more powerful user interface make them a more attractive alternative. However,

still there are certain areas, which require a quick response, and are still open to

ANN implementation. Many applications for the various fault diagnosis problems

have been reported in the literature. Kanoh et al [HMK88] proposed a cascade

structure of three three-layer perceptron networks for the identification of a

faulted transmission section. The ANNs were trained using backpropagation.The

first and the second ANN in the cascade structure identify the candidate’s one

and two for fault selection, using current amplitude and phase angle distribution

patterns.

The third ANN obtains the final fault location using the above candidates one and

two, and acurrent amplitude distribution pattern. Results of this approach

indicates that this method canachieve 98.4 percentage accuracy even when the

measured values differed by thirtypercentage from the EMTP .

C.Rodriguez at el [RRMLMP 96] presented a modular and neural network-based

solution to power systems alarm handling and fault diagnosis described it

overcomes the limitations of ‘toy’ alternatives constrained to small and fixed-

topology electrical networks. In contrast with the monolithically diagnosis

systems, the neural network-based approach presented here fulfills the scalability

and dynamic adaptability requirements of the application.

Mapping the power grid onto a set of interconnected modules that model the

functional behavior of electrical equipment provides the flexibility and speed

demanded by the problem. The way in which the neural system is conceived

allows full scalability to realsize power systems.

5.CONCLUSION

The importance of the use of the AI tools has been felt in all the areas of the

Power System Relaying and the need is emphasized. The easiness in evaluating

the vague or non-crisp concepts and the ability of these techniques to learn due

to the technological improvement elevated the effect of these soft computing

techniques.

The study presents concepts, survey and the important analysis of typical

applications of AI techniques (ANN and FUZZY LOGIC) in the field of Power

systems. The fundamentals of the Artificial Neural Network and the Fuzzy Systems

are also described. The analysis of these techniques is indicated in a broader

sense and the practical difficulties are narrated. Also the future concentration on

the modification of the techniques is analyzed to obtain better result and making

these techniques competitive to the human brains.

As in the case of Fuzzy Logic applications it can be seen that these techniques can

be blended with the conventional systems as well as with the other techniques

like Neural Networks and Genetic Algorithms. The hybrid systems thus formed

can be the most powerful systems for design, planning and control & Operation of

practical problems.

Hybrid Systems combining the individual strengths of the ESs and ANNs along

with the Fuzzy systems seems to be the most promising area in future and

promising for the most of the Power system Applications.Moreover there are

sufficient scope in the improvement of the various soft-computing techniques to

increase their strengths and capability. The tools for the simulation of these

conditions also need to be enhanced for their limitations. The application fields

combining the conventional and these techniques can remarkably reduce the

difficulties faced in the Power Systems design, operation and control.

REFERENCES

� Artificial Intelligence Techniques in Power Systems by K. Warwick, Arthur

Ekwue, Raj Aggarwal, Institution of Electrical Engineers.

� http://web.stanford.edu/class/cs227/Lectures/lec01.pdf

� Computational Intelligence Systems and Applications: Neuro-Fuzzy and

Fuzzy logic By Marian B. Gorzalczany