Fault location of a teed-network with wavelet transform and neural networks

Abstract

A new technique using wavelet transform and neural network for fault location in a tee-circuit is proposed in this paper. Fault simulation is canied out in EMTP96 using a fiequency dependent transmission line model. Voltage and current signals are obtained for a single phase (phase-A) to ground fault at every 50Om distance on one of the branches, which is 64.09 km long.Simulation is carried out for 3 cycles (60ms) with step size At, of 2 . 5 p to abstract the high ftequency component of the signal and every 100 points have been selected as output. Two cycles of waveform, covering pre-fault and post-fault information are abstracted for further analysis. These waveforms are then used in wavelet analysis to generate the Training pattern. Two different mother wavelets have been used to decompose the signal, from which the statistical information is abstracted as the training pattern. RBF network was trained and crossvalidated with unseen data.