Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line

This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) to identify the fault types on single circuit transmission lines. The ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results.