Please use this identifier to cite or link to this item:
|Title:||Fault noise based approach to phase selection using wavelets based feature extraction||Authors:||Liao, Y.
|Issue Date:||Mar-1999||Citation:||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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 16, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.