Contingency Evaluation Of Electrical Power System Using Artificial Neural Network
 

Shekhappa G. Ankaliki1, A. D. Kulkarni2, T. Ananthapadmanabha3

1 Department of Electrical & Electronics Engineering, Hirasugar Institute of Technology Nidasoshi-591 236, Belgaum (Dist), Karnataka.    (State) Email:sankaliki@rediffmail.com

2 Department of Electrical & Electronics Engineering, National Institute of Engineering, Mysore, Karnataka (State), India  

3 Department of Electrical & Electronics Engineering, National Institute of Engineering,  Mysore, Karnataka (State), India .


ABSTRACT

            This paper presents application of Artificial Neural Network (ANN) based contingency analysis of power system. The ANN has been chosen because of its high adaptation parallel information processing capability.  Another feature that makes the ANN more suitable for this type of problems is its ability to augment new training data without the need for retraining. In this Multilayer Feed Forward network is used for contingency analysis in planning studies where the goal is to evaluate the ability of a power system to support a projected range of peak demand under all foreseeable contingencies. This work involves selection of network design, preparation of input patterns, training & testing. In order to generate the training patterns three system topologies were considered. Training data are obtained by load flow studies (NR Method) for different system topologies over a range of load levels using software simulation package (Mipower) and the results are compiled to form the training set. For training the ANN back propagation algorithm is used. The proposed algorithm is applied to a sample six bus power system and the numerical results are presented to demonstrate the effectiveness of this proposed algorithm in terms of accuracy. It is concluded that the trained ANN can be utilized for both off-line simulation studies and on line estimation of line flows and voltages.

Key words: Contingency Evaluation, Load flow study, Artificial Neural Network.   

 

 

 

 


International eJournal of Mathematics and Engineering

Volume 1, Issue 4, Pages:  715 727