Artificial Neural Networkon Electrical

8/2/2019 Artificial Neural Networkon Electrical

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ARTIFICIAL NEURAL NETWORKBASED

PROTECTIVE SCHEMES


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Topics I will focus

Introduction

ANN, What does it mean ?

Mathematical modeling of ANN

Application in Power system protection

Conclusion


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Most important functional unit in human brain a class of cells called

NEURON

Dendrites Receive information

A bit of biology . . .

Cell Body Process information

Axon Carries processed information to other neurons

Synapse Junction between Axon end and Dendrites of other Neurons

Dendrites

Cell Body

Axon

Synapse
http://heart.cbl.utoronto.ca/~berj/neuron.gif


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ANN What does it mean?

The complex neural structure inside the human brain forms a massive parallel

information system ,the basic processing unit is the NEURON. This contrasts with

conventional computers in which a single processor executes a series of instructions

Neuro - Computing is something called the brain-like computations.

It is composed of a large number of highly interconnected processingelements (neurons) working in unison to solve specific problems.

From a mathematical point of view ANN, is a complex non-linear function

with many parameters that are adjusted (calibrated, or trained) in such a way

that the ANN output becomes similar to the desired output.


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The first step toward artificial neural networks came in 1943when Warre McCulloch, a neurophysiologist, and a youngmathematician, Walter Pitts, wrote a paper on how neuronsmight work. They modeled a simple neural network withelectrical circuits.

History


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Mathematical Model of An Artificial Neuron

Receives Inputs X1 X2 Xp from environment Inputs fed-in through connections with weights Total Input = Weighted sum of inputs from all sources Transfer function (Activation function) converts the input to output Output goes to other neurons or environment

f

X1

X2

Xp

I

I = w1X1 + w2X2+ w3X3 + + wpXp

V = f(I)

w1

w2...

wp

Dendrites Cell Body Axon

Direction of flow of Informationy1

y2

y3y4


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ANN Feed-forward Network

A collection of neurons form a Layer

Dire

ctionofinformationflow

X1 X2 X3 X4

y1 y2

Input Layer- Each neuron gets ONLY

one input, directly from outside

Output Layer- Output of each neuron

directly goes to outside

Hidden Layer- Connects Input and Output

layers


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Free parametersDecided by the structureof the problem

# Input Nrns = # of Xs

# Output Nrns = # of Ys

One particular Model an Example

Input: X1 X2 X3 Output: Y Model: Y = f( X1 , X2 , X3 )

X1X2 X3

Y

0.5

0.6 -0.1 0.1-0.2

0.7

0.1 -0.2

Parameters Example

# Input Neurons 3

# Hidden Layers 1

# Hidden Layer Size 2

# Output Neurons 1

Weights Specified


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How to train the Model ?

E= ( Yi V

i)2

Start with a random set of weights.

Feed forward the first observation through the netX1 Network V1 ; Error = (Y1 V1)

Adjust the weights so that this error is reduced( network fits the first observation well )

Feed forward the second observation.Adjust weights to fit the second observation well

Keep repeating till you reach the last observation

This finishes one CYCLE through the data

Perform many such training cycles till theoverall prediction errorE is small.

Fee

d

forward

BackPropagation

Training the Model


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Characteristics of the ANN

The NNs exhibits mapping capabilities, that is they can map input patterns

to their associated output patterns.

The NNs learns by example. Thus NN architectures can be trained with

known examples of the specific problem before they are tested for their

inference capability to unknown instance of the problem.

The NNs can process information in parallel, at high speed.

Learning of Ann

Supervised learning.

Unsupervised learning.


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PROTECTIVE RELAYING MECHANISM

Different parts of the fault clearance chain

RELAY

BUS

CT

CVT

TRIP COMMAND

LINE


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INPUT DATA FROM

CTs & CVTs

ANN TRAINING

(Supervised /

Unsupervised )

DATA FROM POWER SYSTEM

SIMULATION

SUBSTATION

HISTORICAL

DATABASE

NEURAL NETWORK

MODEL

NEURAL NETWORK BASED FAULT DIAGNOSIS

FAULT

DETECTED ?

NO

YES

TRIP COMMAND TO

BREAKER


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THE DESIGN PROCEDURE AND CONSIDERATIONS

Essential issues for formulating a neural network model canbe outlined as follows

Preparation of suitable training data Selection of a suitable ANN structure

Training of the ANN

Pattern recognition and classification

Evaluation of the trained network using test pattern


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High Impendence fault detection

A high impedance arcing fault in a distribution feeder can not be easily

detected using the conventional over current relay.

Ebron, Lubkeman & White proposed the use of ANN for detecting high

impedance fault.

The network was trained by a set of current patterns of arcing faults of a

12KV feeder, arc welder and varying loads .

The raw data obtained from the simulation are

Peak values of transient currents over 3 phases

Current before and after the largest transients

1st, 3rd, 5th harmonics of the transient currents

Magnitude of positive sequence current

One training pattern consists of 10 cycles of the above data mentioned.

The range of input data are brought between 0 to 1. Contd

h f h k h h d f l


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The target output of the network is 1 in case a high impedance fault ,

otherwise it is set to 0.

The input layer, hidden layer, output layer has 33,6 and 1 nodes respectively

The trained network was tested by the data that was never seen by the n/w. Fig.1 shows the current and output of the neural network for an arcing fault.

Fig.1(a) Input current Fig.1(b) ANN output for arcing fault

Fig.2(a) Input current Fig.2(b) ANN output for arc Welder

Fig.2 shows the current and neural network response for an arc Welder.


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Improvement of Distance Relaying Among the components of an electric power system, the

transmission line is the most susceptible element to experience

faults, especially if its physical dimension is considerable.

The principle of these techniques is the measurement ofimpedance at a fundamental frequency between the relay

location and the fault point; thus, determining if a fault isinternal or external to a protection zone.

power system protection techniques involve identification ofthe pattern of the associated voltage and current waveformsmeasured at the relay location.

As ANN provides excellent features for pattern classificationand recognition, it can be successfully applied here.

Contd

C i l di l i d ' d i f


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Conventional distance relaying doesn't produce satisfactoryresult, when fault occurs at very close distance to the protectedzone.

Pre-processed voltages and currents are used as inputs tothe ANN, which determines the fault location. Finally, a logicunit issues the trip order based on the output of the ANN.

A simulation is done for a transmission line under differentfaulted conditions assuming. The 100 km, 220kV transmissionline used to train and test the proposed ANN as shown in figure .

Contd


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It is assumed that the relay is to protect 80% of the line, i.e.,80 km. Fault data are generated at different distances for variousfault types, fault resistances.

Three phase voltages and currents taken and sampled at therate 1 kHz. These values are scaled to have values between1 and -1.

Training is done by providing the historical data and thesimulated data as obtained above.

For faults inside the protection zone, a trip signal will be sent

to the circuit breaker through the logic unit as ANN output is 1.


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Conclusion:-

The neural network approach is quite beneficial since it allows

For on-line learning, which is not available in conventional

Techniques.

This biologically inspired computation technique , if trainedproperly it will produce more accurate and reliable outcomes.


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http://www.weekipedia.com/


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Artificial Neural Networkon Electrical

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