Static security assessment using radial basis function neural networks based on growing and pruning method

Abstract

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 a novel method based on growing and pruning training algorithm using radial basis function neural network (GPRBFNN) and winner-take-all neural network (WTA) to examine whether the power system is secure under steady-state operating conditions. Hidden layer neurons have been selected with the proposed algorithm which has the advantage of being able to automatically choose optimal centers and distances. A feature selection technique-based class separability index and correlation coefficient has been employed to identify the inputs for the GPRBF network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus and IEEE 30-bus systems.