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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 distinctand the high frequency components in the transients can be employed to reveal fault characteristics. In this paper, wavelet transform isapplied to extract fault characteristics from the fault signals. Fuzzy theory and neural network are employed to fuzzify the extractedinformation. Wavelet is then integrated with fuzzy neural network to form the wavelet fuzzy neural network (WFNN). The WFNNis most suitable for post-fault transient and steady-state signal analysis in industrial distribution power system. Analysis and simulationresults illustrate that the theory and algorithm of the WFNN proposed in this paper are efficient in fault location. The WFNN can bewidely applied in fault analysis of power system.-Application of wavelet fuzzy neural network in locating single lineto ground fault (SLG) in distribution lines
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