Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines

Fan Chunju, K. K. Li, Wai Lok Chan, Yu Weiyong, Zhang Zhaoning

Research output: Journal article publicationJournal articleAcademic researchpeer-review

79 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)497-503
Number of pages7
JournalInternational Journal of Electrical Power and Energy Systems
Issue number6
Publication statusPublished - 1 Jul 2007


  • Fault location
  • Fuzzy neural network
  • Wavelet transform

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines'. Together they form a unique fingerprint.

Cite this