Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31528
Title: Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines
Authors: Fan, C
Li, KK
Chan, WL 
Yu, W
Zhang, Z
Keywords: Fault location
Fuzzy neural network
Wavelet transform
Issue Date: 2007
Publisher: Elsevier
Source: International journal of electrical power and energy systems, 2007, v. 29, no. 6, p. 497-503 How to cite?
Journal: International journal of electrical power and energy systems 
Abstract: This paper proposes a fault location method employing wavelet fuzzy neural network to use post-fault transient and steady-state measurements. When single line to ground fault (SLG) occurs in the distribution lines of an industrial system, the transient feature is distinct and the high frequency components in the transients can be employed to reveal fault characteristics. In this paper, wavelet transform is applied to extract fault characteristics from the fault signals. Fuzzy theory and neural network are employed to fuzzify the extracted information. Wavelet is then integrated with fuzzy neural network to form the wavelet fuzzy neural network (WFNN). The WFNN is most suitable for post-fault transient and steady-state signal analysis in industrial distribution power system. Analysis and simulation results illustrate that the theory and algorithm of the WFNN proposed in this paper are efficient in fault location. The WFNN can be widely applied in fault analysis of power system.
URI: http://hdl.handle.net/10397/31528
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2006.11.009
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