A hybrid neural network model for fast voltage contingency screening and ranking

In this paper, a hybrid neural network based approach is proposed for fast voltage contingency screening and ranking. The developed hybrid neural netw ork is a combination of a filter module and ranking modular neural network. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non -critical contingency class using a modified BP algorithm. The screened cri tical contingencies are passed to the ranking modular neural network for th eir further ranking. The ranking modular neural network reduces a K-class p roblem to a set of K two-class problems with a separately trained network f or each of the simpler problems. Total load demand, real and reactive pre-c ontingency line-flows and terminal voltages in the contingent element, alon g with a topology number corresponding to the contingent element, are selec ted as input features for the neural networks. The continuous values of vol tage performance index are classified into four classes (levels) according to their severity, and the modular neural network is trained for this multi -class classification problem. The effectiveness of the proposed method is demonstrated by applying it for contingency screening and ranking at differ ent loading conditions for IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the hybrid neural network gives fast and accurate sc reening and ranking for unknown patterns and is found to be suitable for on -line applications at Energy Management Systems. (C) 1999 Elsevier Science Ltd. All rights reserved.