Fast real power contingency ranking using counter propagation network: feature selection by neuro-fuzzy model

Abstract

In deregulated operating regime power system security is an issue that needs due consideration from researchers in view of unbundling of generation and transmission. Real power contingency ranking is an integral part of security assessment. The objective of contingency screening and ranking is to quickly and accurately shortlist critical contingencies from a large list of credible contingencies and rank them according to their severity for further rigorous analysis. In the present work, modified counter propagation network (CPN) with neuro-fuzzy (NF) feature selector is used for real power contingency ranking of the transmission system. The CPN is trained to estimate the severity of a series of contingencies for given pre-contingencies line-flows. But for larger size system it becomes rather difficult to cope with the increased size of input pattern and network as well. And it adversely affected the performance of the network and computational overhead. The proposed NF feature selector prunes the size of input pattern by exploring the individual power of features to characterize/discriminate different clusters. The reduced set of discriminating inputs not only ensures saving in training time but also improves estimation accuracy and execution time and these are the deciding parameters in evaluating the performance of particular contingency ranking technique. The effectiveness of proposed approach is demonstrated on IEEE 30-bus test system and practical 75-bus Indian system.