Title :
Power system security evaluation using ANN: feature selection using divergence
Author :
Niazi, K.R. ; Arora, C.M. ; Surana, S.L.
Author_Institution :
Reader in Electr. Eng., Malaviya Nat. Inst. of Tech., Jaipur, India
Abstract :
This paper presents an Artificial Neural Network (ANN) based method for on-line security evaluation of power systems. One of the important considerations in applying ANN is feature selection. A new divergence based feature selection algorithm has been proposed and investigated. The method has been applied on an IEEE test system and the results demonstrate the suitability of the proposed method for online security evaluation of power systems even under changing topological conditions.
Keywords :
feature extraction; learning (artificial intelligence); network topology; neural nets; power system analysis computing; power system security; real-time systems; IEEE test system; artificial neural network; backward sequential algorithm; divergence based feature selection algorithm; feature selection; learning; network topology; online security evaluation; power system security evaluation; topological conditions; Artificial neural networks; National security; Neural networks; Power system dynamics; Power system modeling; Power system security; Power system stability; Power system transients; Real time systems; System testing;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223731
