orginal ppt2003

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BY

B SAI HARSHA07F91A0255

REALTIME POWER SYSTEMSECURITY ASSESSMENT USINGARTIFICAL NEURAL NETWORKS


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ABSTRACTContingency analysis of a power system is a major activity

in power system planning and operation.

The traditional approach of security analysis involving thesimulation of all conceivable contingencies by full AC loadflows, becomes prohibitively costly in terms of time and

computing resourcesA new approach using Artificial Neural Network s has been

proposed in this paper for real-time network securityassessment.

The proposed paradigms are tested on IEEE 14 bus and

30 bus systems.


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INTRODUCTIONSecurity refers to the ability of the system to withstand the

impact of disturbance

The process of investigating whether the system secure orinsecure in a set of proposed contingencies is called SecurityAnalysis.

Security assessment has two functions the

first is violation detection in the actual system operatingstate.

The second, much more demanding, function of securityassessment is contingency analysis

In this paper, for the determination of voltage contingency

ranking, a method has been suggested, which eliminatesmisranking and masking effects and security assessment hasbeen determined using Radial Basis Function (RBF) neuralnetwork for the real time control of power system


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Artificial Neural NetworksArtificial neural networks (ANN) are massively parallel

inter connected networks of simple elements known as

artificial neurons and their connectivity is intended tointeract with the objects of real world, in a similarmanner as the biological nerves systems do.

The simple neuron model is shown in fig(a). unitmultiplies each input x by a weight w and sums the

weighted inputs. The output of the figure is

NET = x1w1 + x2w2 + .+xnwn :

OUT = f(NET)

input ouput

hidden layer

F


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Basic features of ANNs are:

High computational rates due to the massive parallelism.

Fault tolerance.

Training the network adopts itself, based on the informationreceived from the environment

Programmed rules are not necessary.

Primitive computational elements.

Radial basis function networks:

The Radial Basis Function is similar to the Gaussianfunction, which is defined by a center and a widthparameter.

The Gaussian function gives the highest output when theincoming variables are closest to the center appositionand decreases monotonically as the rate of decrease.


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Training of the RBF networka)Computation of RBF parameters:

Initialize the center of each cluster to a randomlyselected training pattern.

Assign each training pattern to the nearestcluster. This can be accomplished by calculating

the Euclidean distances between the trainingpatterns and the cluster centers.

When all the training patterns are assigned,calculate the average position for each cluster

center. Then they become new cluster centers.Repeat steps (2)and (3) until the cluster centers

do not change during the subsequent iterations.


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To achieve an accurate picture of systems vulnerability tooutage event several issues used to be considered. They are :

a) system model

b) Contingency definition

c) Contingency list

d) performance

e) Modeling details

When finding critical contingencies and giving ranking tothem, we should consider the following.

a)Magnitude of voltage violation

b)Number of violations occurringc)Relative importance to each voltage violationd)Nearness of voltages to the security limitse)Load level of the system when evaluated


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Real power performance index:

Real power based performance indices (PIp) to calculate PI valuesof each line under outage conditions, is defined as

On-line security analysis:

There are three basic elements of on-line security analysis andcontrol, namely, monitoring assessment and control. They aretied together in the following framework.

Step-1) Security monitoring :

Using real-time systems measurements. Identify

whether the system is in the normal state or not. If the system isin an emergency state, go to step-(3). If load has been lost, go

to step-(4).

=

=

L

kii i

ipip

P

PWPI

1

max2


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Step-2) Security Assessment:

If the system is in the normal state, determinewhether the system is secure or insecure with

respect to a set of next contingencies.

Step-3) Emergency control:

Execute proper corrective action to bringthe system back to the normal back to the normal

state following a contingency which causes thesystem to enter an emergency state. This issometimes called remedial action.

Step-4) Restorative Control:

Restore service to system loads


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Simulation results:

The training patterns were generated forbase case and different network outages using aNewton-Raphson load flow program by varyingthe loads at each bus randomly covering the

whole range of operating condition up to 110%of the base case loading value. For each power system total 100 patternswere generated. Out of these, 75 patterns were

used to train the RBF network and remaining 25patterns were used to test the accuracy androbustness of the trained RBF network.


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CONCLUSION

In this paper power system static security assessmenthas been investigated. The test results presented onIEEE 14-bus system and IEEE 30-bus system providesthe following observations.

A new method has been reported for calculating voltageperformance index for contingency ranking. Whicheliminates misranking and masking problems? Rankingof all contingencies is same irrespective of values ofweights supplied.

The RBF neural network model provides more accurateresults for both the security and insecurity cases.

Training is very fast as the RBF network has thecapability of handling large dateTesting time is less than 0.2 micro sec.


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