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Fault Noise Based Approach to Phase Selection Using Wavelets Based Feature Extraction.
- Source :
-
Electric Machines & Power Systems . Apr99, Vol. 27 Issue 4, p389-398. 10p. - Publication Year :
- 1999
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ELECTRIC machinery
*FOURIER transforms
Subjects
Details
- Language :
- English
- ISSN :
- 0731356X
- Volume :
- 27
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Electric Machines & Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 3973120
- Full Text :
- https://doi.org/10.1080/073135699269226