A novel radial basis function neural network for fault section estimation in transmission network

In this paper, the application of a radial basis function neural network (RBF NN) to fault section estimation in power systems is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN. In order to assess the effectiveness of RBF NN, a classical back-propagation neural network (BP NN) has been developed to solve the same problem for comparison. A computer test is conducted on a 4-bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation.