NDLI: A New Fuzzy Neural Network Based Insulator Contamination Detection

Content Provider	IEEE Xplore Digital Library
Author	Yu-Ping Lu Min Yu Lai, L.L. Xia Lin
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. Eng., Southeast Univ., Nanjing (Yu-Ping Lu; Min Yu)
Abstract	The contamination condition of insulators is usually estimated by detecting the root mean square (r.m.s) of surface leakage current via online monitoring system, ignoring the influence of environment factors, such as temperature, humidity, etc. For the detection factors have fuzzy characters, a new method based on fuzzy neural network is proposed in order to overcome the disadvantages of traditional insulation condition detection. It is through the build of the structure of fuzzy neural network and the establishment of net weights by training samples as well to estimate the contamination severity of insulators. The test samples simulation experiment result proves the validity of the method presented in this paper, which shows an instructive significance for the prevention of the insulator from flashover fault and the condition-based maintenance (CBM)
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	4099
Ending Page	4104
File Size	236215
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258868
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Insulation Leak detection Root mean square Surface contamination Leakage current Condition monitoring Temperature Humidity Insulator testing Contamination detection Insulators Fuzzy Neural network
Content Type	Text
Resource Type	Article

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