Auto Configuring Artificial Neural Paper Presentation

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AUTOCONFIGURING ARTIFICIAL NEURAL
NETWORK APPLIED TO FAULT DIAGNOSIS
IN POWER SYSTEMS

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INTRODUCTION:

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The fault diagnosis of a power system provides an effective means to get
information about system restoration and maintenance of the power system.
Artificial intelligence has been successfully implemented on fault diagnosis

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and system monitoring. Expert systems are used by defining rules, for a fault
diagnosis. In the present work particularly a new method of “AI” namely
“Artificial Neural Network” is used as diagnosing to power system faults.

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A study has been made by taking a sample models of power systems. The all
possible faults of the system were diagnosed and predicted with the help of
“Auto-Configuring Artificial Neural Network” namely “Radial Basis Function
Network” and the comprehensive study reveals that the proposed method is
more efficient, faster and reliable than any other method used for fault
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diagnosis of power systems.
DEFINITION:
Artificial intelligence (AI) is simply the way of making the computer think
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intelligently. It there by provides a simple, structured approach to designing
complex decision-making programs. While designing an AI system, the goal
of the system must be kept in mind. There exists a more sophisticated system;
which guides the selection of a proper response to a specific situation. This
process is known as “Pruning”, as its name suggests eliminates path way of
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thoughts that are not relevant to the immediate objective of reaching a goal.
AI has made a significant impact on power system research. Power system
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engineers have applied successfully AI methods to power system research
problems like energy control, alarm processing, fault diagnosis, system
restoration, voltage/var control, etc. for the last couple of years a new AI
method namely Artificial Neural Network (ANN) has been used extensively in
power system research. In comparison to the AI method, which tries to mimic
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mental process that takes place in human reasoning, ANN on the other hand
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tries to stimulate the neural activity that takes place in the human brain. ANN
has been successfully applied to economic load dispatch, shot term load
forecasting, security analysis, alarm processing, capacitor installation and
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EMTP problems. An attempt has been made here to solve the fault diagnosis
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problem in power systems using ANN.
The principal functions of these diagnosis systems are:
1) Detection of fault occurrence
2) Identification of faulted sections
3) Classification of faults into types:
HIFs (high impedance faults) or LIFs(low impedance faults)



This has been achieved through a cascade, multilayered ANN structure. Using
these FDS accurately identifies HIFs, which are relatively difficult to identify
in the other methods.
FAULT ANALYSIS AND PROTECTIVE SYSTEM
A fault in electrical equipment is defined as a defect in its electrical circuit due

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to which the current is diverted from the intended path. Breaking of
conductors or failure generally causes fault. The other causes of fault include
mechanical failure, accidents, excessive internal and external stress the faults

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can be minimized by inputting the system, design, quality of equipment and
maintenance Voltage and current unbalanced, Over voltage, Under frequency,
Reversal of power, Power swings, Instability. However the faults can be
eliminated completely.

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For the purpose of analysis the faults can be classified as
1) Single line to ground fault
2) Line to line fault
3) Double line ground fault
4) Simultaneous fault
5) Three phase fault
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6) Open circuit fault etc
Some of the abnormal conditions are not serious enough to call for tripping of
the circuit breaker. In such cases the protection relaying is arranged for giving
an alarm where as in other cases it is harmful in such cases the fault should be
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disconnected immediately without any delay. This function is performed by
protective relaying and switch gear.
FAULT CALCULATION:
The knowledge of the fault current is necessary for selecting the circuit
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breakers of adequate rating, designing the sub –station equipment, determining
the relay setting, etc. The fault calculation provides the information about the
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fault currents and the voltages at various points of the power system under
different fault conditions. The per. Unit (p.u) system normally used for fault
calculations
The symmetrical faults such as three phase faults are analyzed on per phase
basis the unsymmetrical fault is calculated by the method of symmetrical
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components
Network analyzer and digital computers used for fault calculation for large
systems
ARTIFICIAL INTELLIGENCE APPLIED TO FAULT DIAGONISIS
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AND POWER SYSTEM RESTORATIONS:
AI is simply a way of making a computer think intelligently this
accomplished by studying how people think when they are trying to make
decisions and solve problems, breaking these thought processes down into
basic steps and designing a computer program that solves problems using
those some steps .AI thereby provides a simple , structured approach to design
complex decision making programs, human intelligence is of complex
function that scientists have only began to understand, but enough is known



for us to make certain assumptions about how we think and apply these
assumptions in designing AI problems.
SUPERFAST AUTOCONFIGURING ARTIFICIAL NEURAL
NETWORK:
The reasons for adapting ANNs are as follows:

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• Massive parallelism
• Distributive representation and computation
• Learning ability

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• Adaptivity
• Inherent contextual information processing
• Fault tolerance
• Low energy consumption

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BIOLOGICAL NEURON:
The concept of neuron in ANN structure is divided from biological neurons.
A neuron is special biological structure that process information. The output
area of the neuron is called axon through which an impulse triggered by the
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cell can be sent. The input area of the nerve cell is a branching fiber is called
dendrites. When a series of impulses is received at the dendrites area of the
neuron the result is usually an increase probability that the target nearer will
fire an impulse down its action.
ANN ARCHITECTURE:
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ANNs can be categorized into two groups:
• Feed forward networks
• Recurrent networks
Feed forward networks are static; they produce one set of output values rather
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a sequence of values from a given input. These networks are memory less in
the sense their response to an input is independent of the previous network
states. On the other hand recurrent network systems are dynamic systems
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when a new input pattern is presented the neuron outputs are computed,
because of the feedback paths. The inputs to each neuron are then modified,
which leads the network to enter a new state.
In a most common family of feed forward networks is called “multilayer
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perception”, neurons are organized into layers that have unidirectional
connections between them. The bottom layer of units is the input layer, the
only units in a network that receives external inputs. The layer above is the
hidden layer in which the PUs is interconnected to layers above and below.
The top layer is the output layer .the layers are fully interconnected to each PU
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is connected to every unit in the layer above and below it; units are not
connected to other units in the same layer.

A THREE LAYER NEURAL NETWORK



OUTPUT PATTERN

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OUTPUT LAYER
PATTERN

HIDDEN LAYER

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WEIGHT CONNECTED
BETWEEN NEURON

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INPUT LAYER

INPUT PATTERN tyo
LEARNING:
The ability to learn is a fundamental trait of intelligence. A learning process
in the ANN context can be viewed as the problem of updating network
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architecture and connection weights, so that a network can efficiently perform
a specific task. There are three main learning paradigms:
• Supervised
• Unsupervised
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• Hybrid
In supervised learning the network is provided without a correct answer for
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every input pattern weights are determined to allow the network to produce
answers as close as possible to the known correct answers.
In unsupervised learning doesn’t require correct answers associated with each
input pattern in the training dataset. It explores the underlined structure in a
data, or corrections between patterns in the data and organizes patterns into
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categories from these correlations.
Hybrid learning combines both the supervised and unsupervised learning’s.
TRAINING OF ANN:
There are several training methods used for training of ANN:
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• Back propagation network(BPN)
• Radial basis function network(RBF)
• Levenberg-Marquardt network(LMN)
• Hopfied network
SYSTEM UNDER STUDY:
Here a sample power system is selected to test the neural network model.
POWER SYSTEM MODEL-I:



The below power system-I consists of bus bars, transformers, transmission
lines, CBs and protective relays with their back-ups. The input pattern consists
of status (on or off) of the protective relays and the circuits breakers of the
power system. The output pattern for the training cases consists of the
corresponding faults of the system

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This power has 10 circuit breakers (CBs), 5 transmission lines (Ls), 2d buses
(Bs), 2 transformers (Ts) and 9 protective relays (Rs). It is assumed that each
protective relay for main and back-up protection and each line has two

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protective relays.

LINE1 LINE2

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BUS 1

C.B 3 C.B 5

T1
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C.B 5 C.B 6
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BUS 2

C.B 7 C.B 10
C.B 9
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LINE3 LINE4 LINE5
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POWER SYSTEM-I FOR FAULT DIAGNOSIS.

APPLICATION OF RADIAL BASIS FUNCTION NETWORK TO THE
PROBLEM:
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As mentioned earlier the radial basis function network model is adopted for
solving the system under study problems. The network can be represented by a
number of inputs, hidden layer and outputs are calculated and subsequently,
radial basis algorithm is applied to determine the weight element changes. The
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more efficient batching operation is applying Q input vector simultaneously
and get the network response to each of them. The inputs and outputs can be
represented by matrices called P and T, which can be written in the following
form:
The network also produces the output in matrix form.



P= T=

P(1,1) P(1,2)……P(1,q) T(1,1) T(1,2)…………T(1,t)
P(2,1) P(2,2)……P(2,q) T(2,1) T(2,2)………….T(2,t)

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………………………. …………………………..
………………………. …………………………..
P(x,1) P(x,2)……P(x,q) T(s,1) T(s,2)……….....T(s,q)

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PERCEPTRON:
The perceptron is the simplest form of the neural network used for

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classification. It consists of single layer with adjustable synaptic weights and a
threshold. A single layer perceptron is limited to performing pattern
classification with only two separate classes.
• The model of each neuron in the network includes a non linear element
at the output end. tyo
• The network contains one or more layers of hidden neurons that are not
of a part of the input or output of the network. The hidden neurons
enable the network to learn complex tasks by extracting progressively
more meaningful features from the input patterns..
The simulation of perceptron consists of two phases
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• Initialization
• Training
INITIALIZATION: The MATLAB function for the initialization is rad. This
function is used to initialize the weights and bias elements to small positive
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and negative values.
TRAINING:
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The major steps in the training phase can be summarized as follows:
i. The presentation phase: presented the inputs and calculate the network
outputs.
ii. Checking phase: check to see if each output vector is equal to the
target vector associated with the given input.
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iii. Training algorithm: training is done by orthogonal least square
algorithm for radial basis function network.
iv. Learning phase: adjust weight and bias accordingly using perceptron
learning rule.
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FOR POWER SYSTEM 1:
The input layer of the neural network contains informations about the above
mentioned 10 circuit breakers and 9 protective relays.
The input layers are (from the left):
CB1, CB2, CB3, CB4, CB5, CB6, CB7, CB8, CB9,
CB10,LIM,LIB,L2M,L2B,L3M,L3B,L4M,L4B,L5M,L5B,T1M,T2M,X1B,
X2B



Where:
CB*=circuit breaker
L*M=main relay associated with line
L*B=back up relay associated with line
T*M=main relay associated with transformer

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X*=main relay associated with bus
The possible faults associated with the given power system are transmission
line faults, transformer faults and bus bar faults

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Therefore the variables of the output layer of the neural network1(from the
left)
B1, B2, L1, L2, L3, L4, L5, T1, T2
Where:

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B*=fault of bus bar*
L*=fault of line
T*=fault of transformer
The on/off status of the circuit breakers and the relays are represented by 1s
and 2s as defined in the below table.

DEFINITION OF THE STATUS OF THE NEURON
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NEURON STATUS
1 2
Relay Not operated Operated
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Circuit breaker Not tripped Tripped
Fault components No fault fault
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The typical input patterns and the corresponding output pattern that can be
used to train the neural network are given below:
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TRAINING PATTERNS:
PATTERN-1:
INPUT PATTERN:
112211111111111111111121
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OUTPUT PATTERN:
211111111

PATTERN-2:
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Failure of line L1, due to over current
Relay operated: L1M
Circuit breaker operated: CB1
INPUT PATTERN:
211111111121111111111111
OUTPUT PATTERN:
112111111
TEST PATTERN:



Failure of main line L1 relay
Relay operated : L1B
Circuit breaker operated: CB1
In this way the patters were computed assuming that only one single fault
occurs at any time. The total number of pattern chosen for the training sets

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were equal to 9,in addition to this 5 patters were selected for the testing of
neural network. This test pattern consists of one piece of equipment
malfunction for the single fault(failure of main relay). Refer the below given

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tables:
PATTERN GENERATION FOR POWER SYSTEM-1:
TRAINING PATTERN FOR SIMULATION
INPUT (9 patterns,24 inputs)

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INPUT/
1 2 3 4 5 6 7 8 9
PATTERN
1 CB1 1 1 2 1 1 1 1 1 2
2 B2 1 1 1 2 1 1 1 1 2
3
4
5
CB3
CB4
CB5
2
2
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
2
1
tyo OUTPUT (9 pattern,9 output)

OUTPUT/
1 2 3 4 5 6 7 8 9
PATTERN
6 CB6 1 1 1 1 1 1 1 1 2 1 B1 2 1 1 1 1 1 1 1 1
7 CB7 1 2 1 1 2 1 1 1 1 2 B2 1 2 1 1 1 1 1 1 1
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8 CB8 1 2 1 1 1 1 1 1 1 3 L1 1 1 2 1 1 1 1 1 1
9 CB9 1 1 1 1 1 2 1 1 1 4 L2 1 1 1 2 1 1 1 1 1
10 CB10 1 1 1 1 1 1 2 1 1 5 L3 1 1 1 1 2 1 1 1 1
11 L1M 1 1 2 1 1 1 1 1 1 6 L4 1 1 1 1 1 2 1 1 1
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12 L1B 1 1 1 1 1 1 1 1 1 7 L5 1 1 1 1 1 1 2 1 1
13 L2M 1 1 1 2 1 1 1 1 1 8 T1 1 1 1 1 1 1 1 2 1
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14 L2B 1 1 1 1 1 1 1 1 1 9 T2 1 1 1 1 1 1 1 1 2
15 L3M 1 1 1 1 2 1 1 1 1
16 L3B 1 1 1 1 1 1 1 1 1
17 L4M 1 1 1 1 1 2 1 1 1
18 L4B 1 1 1 1 1 1 1 1 1
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19 L5M 1 1 1 1 1 1 2 1 1
20 L5B 1 1 1 1 1 1 1 1 1
21 T1M 1 1 1 1 1 1 1 2 1
22 T2M 1 1 1 1 1 1 1 1 2
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23 X1M 2 1 1 1 1 1 1 1 1
24 X2B 1 2 1 1 1 1 1 1 1



INPUT/PATTERN 1 2 3 4 5
1 CB1 2 1 1 1 1
2 CB2 1 2 1 1 1
3 CB3 1 1 1 1 1
4 CB4 1 1 1 1 1 OUTPUT(TESTING PATTERN)

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5 CB5 1 1 1 1 1 (5 patterns 9 outputs)
6 CB6 1 1 1 1 1
7 CB7 1 1 1 1 1 OUTPUT/
1 2 3 4 5

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8 CB8 1 1 1 1 1 PATTERN
1 B1 1 1 1 1 1
9 CB9 1 1 1 2 1
2 B2 1 1 1 1 1
10 CB10 1 1 1 1 2
3 L1 2 1 1 1 1
11 L1M 1 1 1 1 1
4 L2 1 2 1 1 1

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12 L1B 2 1 1 1 1
13 L2M 1 1 1 1 1 5 L3 1 1 2 1 1

14 L2B 1 2 1 1 1 6 L4 1 1 1 2 1

15 L3M 1 1 1 1 1 7 L5 1 1 1 1 2

16 L3B 1 1 2 1 1 8 T1 1 1 1 1 1

17
18
19
L4M
L4B
L5M
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
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20 L5B 1 1 1 1 2
21 T1M 1 1 1 1 1
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22 T2M 1 1 1 1 1

23 X1M 1 1 1 1 1
24 X2B 1 1 1 1 1
INPUT( TESTING PATTERN)5 pattern, 24
inputs)
CONCLUSION:
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The salient features of the RBF networks are:
a) They are extremely fast due to the hybrid two stage training scheme
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employed.
b) They have only a single hidden layer with growing number of neurons
during learning to achieve an optimal configuration.
c) Only a single network parameter called spread factor (SF) is varied.
LIMITATIONS:
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• Requires more training data for more accurate results.
• Unworthiness to detect multiple faults due to more piece of
equipment malfunctioning.
REFRENCES:
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1) Sharestani.S.A, Silartis.J.Y.P “Application of pattern recognition to
identification of power faults, electric power system research”.
2) Fausett.L “ Fundamentals of neural networks”, PHI, 1994 and several other
technical periodicals.


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