Prepared as a part of the curriculum for the subject,'Switchgear and Protection' in NIT Calicut

1. 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

2. 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.

3. 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

4. 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).

5. 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 .

6. 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.

7. 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.

8. 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.

9. 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

10. 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.

11. 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

12. 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.

13. 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

14. Fig 4.3.2 ANN topology
Fig.4.3.3 .Relaying scheme

15. 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

16. 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.

17. 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.

18. 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