Radial basis function networks for fast contingency ranking

This paper presents an artificial neural network-based approach for static-security assessment. The proposed approach uses radial basis function (RBF) networks to predict the system severity level following a given list of contingencies. The RBF networks are trained off-line to capture the nonlinear relationship between the pre-contingency line flows and the post-contingency severity index. A method based on mutual information is proposed for selecting the input features of the networks. Mutual information has the advantage of measuring the 4 general relationship between the independent variables and the dependent variable as against the linear relationship measured by the correlation-based methods. The performance of the proposed approach is demonstrated through contingency ranking in IEEE 30-bus test system. (C) 2002 Elsevier Science Ltd. All rights reserved.