Introduction to Artificial Neural Networks and Its Application

8/8/2019 Introduction to Artificial Neural Networks and Its Application

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Introduction to Artificial Neural

Networks and Its Application InPower System

sanjay negi

07237

EEE


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Contents:

Biological Inspiration

Artificial Neurons and Neural Network

Activation Function

Application Of ANN


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Inspiration From Neurobiology

Many input single output unit

If the sum of the input signals

surpasses a certain threshold, then

neuron sends electrical signal along

the axon.


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Artificial Neurons (Mathematical Representation )

The synapses of the neuronare modeled as weights.

The strength of theconnection between aninput and a neuron is noted

by the value of the weight. An adder sums up all the

inputs modified by theirrespective weights.

Finally, an activation

function controls theamplitude of the output ofthe neuron.

The McCulloch-Pitts model


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Activation Functions

This function act as squashing function .

Threshold Function: this function can takes on a value of

0 if the summed input is less than a certain threshold

value (v), and the value 1 if the summed input is greater

than or equal to the threshold value.

Piecewise-Linear function: This function again can take

on the values of 0 or 1, but can also take on values

between that.

sigmoid function: This function can range between 0 and1 but sometime it is useful to take -1 to 1 range.


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Artificial Neural Network

An artificial neural network is composed of many

artificial neurons that are linked together according to a

specific network architecture. The objective of the neural

network is to transform the inputs into meaningful

outputs.

input Output


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Weight settings determine the behaviour of a network

How can we find the right weights?


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One way is to set the weights explicitly, using a priori

knowledge.

Another way is to train the neural network by feeding it

teaching patterns and letting it change its weights according

to some learning rule.


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The learning situations in neural networks may be classified

into three distinct sorts. These are

Supervised learning

Unsupervised learning

Reinforcement learning


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Application of ANN In Power System

Load forecasting

Economic dispatch

Fault diagnosis/fault location

Transient stability problems

Security assessment


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Load Forecasting

This can only be done if, among other vital factors, there is a

good and accurate system in place for forecasting the load

that would be in demand by electricity customers.

Als

oSuch f

orecasts wi

ll be high

ly usefu

lin pr

oper syste

m

planning and operations.


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Load Forecasting Using Neural Networks

The back-propagation algorithm is a supervised learning

algorithm used to change or adjust the weights of the neural

network.

Inb

ack-pro

pagatio

n, the gradient vecto

ro

f the erro

r surface iscalculated. This vector points along the direction of steepest

descent from the current point, so that a movement over a

short distance along it decreases the error. A sequence of

such moves will eventually find a minimum error point.


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In this fig. basically two load

patterns wereob

servabl

e:o

nefor weekends (Saturday and

Sunday) and another for week

days (Monday through Friday).

After the neural network is

trained on the input data set, anew data set is presented at its

input, and the network

provides a forecast of the load

fo

r the nexto

ne ho

ur.


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References

Ajith Abraham .Artificial Neural Networks .Oklahoma State

University, Stillwater, OK, USA

Carlos Gershenson. [email protected] . Artificial

NeuralNetw

orks f

or Beginners

Bakirtzis, A.G., Petridis, V., Kiartzis, S.J., Alexiadis, M.C., and

Maissis, A.H. 1996. A Neural Network Short Term Load

Forecasting Model . IEEE Transactions on Power Systems. 11:

858-863.

M. Tarafdar Haque, and A.M. Kashtiban. Application of

Neural Networks in Power Systems; A Review


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Introduction to Artificial Neural Networks and Its Application