DWT and RBF neural networks algorithm for identifying the fault types in underground cable

A new technique for classifying fault type in underground distribution system has been proposed. Discrete wavelet transform (DWT) and Radial basis function (RBF) neural network are investigated. Simulations and the training process for the RBF neural network are performed using ATP/EMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. Positive sequence current signals are used in fault detection decision algorithm. The output pattern of RBF is divided into two case studies training for comparison between classifying of the fault types and identifying the phase with fault appearance. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The comparison of the coefficients DWT is also compared with the RBF neural network in this paper. The result is shown that an average accuracy values obtained from RBF gives satisfactory results.