On-line fault section estimation in power systems with radial basis function neural network

Abstract

Fault section estimation is of great importance to the restoration of power systems. Many techniques have been used to solve this problem. In this paper, the application of radial basis function neural network (RBF NN) to fault section estimation is addressed. The orthogonal least square (OLS) algorithm has been extended to optimize the number of neurons in hidden layer and the connection weights of RBF NN. A classical back-propagation neural network (BP NN) has been developed to solve the same problem for comparison. Computer test is conducted on a four-bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation.