Fault noise based approach to phase selection using wavelets based featureextraction

Fault-generated high-frequency noise has been proven to be effective for fa ulted phase selection. A combined method using HF noise, fast Fourier trans form (FFT), and neural networks (NN) for phase selection has been proposed previously; however, FFT and NN have some implicit disadvantages. This pape r describes a HF noise based method for phase selection using wavelets base d 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 v ery powerful and apropos to phase selection.