Wavelet transform and neural networks for fault location of a teed-network

A new technique using wavelet transforms and neural networks for fault location in a tee-circuit is proposed in this paper. Fault simulation is carried out in EMTP96 using a frequency dependent transmission line model. Voltage and current signals are obtained for a single phase (phase-A) to ground fault at every 500 m distance on one of the branches, which is 64.09 km long. Simulation is carried out for 3 cycles (60 ms) with step size /spl Delta/t, of 2.5 /spl mu/s to abstract the high frequency 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. Four 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 cross-validated with unseen data.