An improved Hopfield model for power system contingency classification

A method for designing neural networks (NNs) for classifying contingencies in terms of the number and type of limit violations is presented. Specifically, an optimization method (in contrast to a learning method) for finding the weights and thresholds of an associated Little-Hopfield NN is developed. This optimization method, which uses the linear programming technique, maximizes the probability of classifying the contingency correctly. The contingency classification problem is formulated into a pattern recognition problem. A NN to detect a prescribed set of patterns is then designed.<
>