A fast static security assessment method based on radial basis function neural networks using enhanced clustering

Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. This paper presents an enhanced radial basis function neural network (ERBFNN) and winner-take-all neural network (WTANN) to examine whether the power system is secure under steady-state operating conditions. Hidden layer units have been selected with the proposed algorithm which has the advantage of being able to automatically choose optimal unit centers and distances. The proposed approach to contingency analysis was found to be suitable for fast voltage and line-flow contingency screening. The generalization capability of the proposed method was able to identify unknown contingencies with large range of operating conditions and changes in network topology. A feature extraction technique based on class separability index and correlation coefficient has been employed to identify the inputs and dimensional reduction for the ERBFNN and WTANN networks. The advantages of this method are simplicity of algorithm and high accuracy in classification. Case studies with IEEE 14-bus, IEEE 30-bus and IEEE 118-bus power systems are used to illustrate the good performance of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.