On-line supervised learning for dynamic security classification using probabilistic neural networks

This paper addresses the problem of dynamic security classification of electric power systems using multiclass pattern recognition. In particular, on-line supervised learning using probabilistic neural networks is applied. The various patterns are recognized by calculating probabilities of belonging to each class. These probabilities are used in a subsequent decision-making stage to achieve classification. The learning of each class can be performed in parallel. Results regarding performance of the proposed pattern recognition tested on the dynamic security of an actual island power system are presented and discussed.