Fault noise based approach to phase selection using wavelets based feature extraction

Fault-generated high-frequency noise has been proven to be effective for faulted phase selection. A combined method using HF noise, fast Fourier transform (FFT), and neural networks (NN) for phase selection has been proposed previously; however, FFT and NN have some implicit disadvantages. This paper describes a HF noise based method for phase selection using wavelets based feature extraction. It is shown that the features extracted by wavelets transform (WT) have a more distinctive property than those extracted by FFT due to the good time and frequency localization characteristics of WT. As a result, the proposed method dispenses with the neural networks and hence is more reliable and simpler than the previous FFT-based method. Extensive simulation studies have been made to verify that the proposed approach is very powerful and apropos to phase selection.