Application of radial basis function neural network for differential relaying of a power transformer

Abstract

Function approximation has been found in many applications. The radial basis function network is one of the approaches which has shown a great promise in this sort of problems because of its faster learning capacity. The application of RBF neural network for differential relaying of power transformer is presented in this paper. Performance of this model is compared with feed-forward neural network (FFNN). The proposed method of power transformer protection is evaluated using simulation performed with EMTP package. The proposed model requires less training time and is more accurate in prediction as compared to FFNN. © 2002 Elsevier Science Ltd. All rights reserved.