Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/62192
Title: Fault noise based approach to phase selection using wavelets based feature extraction
Authors: Liao, Y.
Elangovan, S. 
Issue Date: Mar-1999
Source: Liao, Y.,Elangovan, S. (1999-03). Fault noise based approach to phase selection using wavelets based feature extraction. Electric Machines and Power Systems 27 (4) : 389-398. ScholarBank@NUS Repository.
Abstract: 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.
Source Title: Electric Machines and Power Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/62192
ISSN: 0731356X
Appears in Collections:Staff Publications

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