A New Fuzzy Neural Network Based Insulator Contamination Detection

Author

Lu, Yu-Ping ; Yu, Min ; Lai, L.L. ; Lin, Xia

Author_Institution

Dept. of Electr. Eng., Southeast Univ., Nanjing

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

4099

Lastpage

4104

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)

Keywords

computerised monitoring; flashover; fuzzy neural nets; insulator contamination; insulator testing; leakage currents; learning (artificial intelligence); mean square error methods; power engineering computing; condition-based maintenance; flashover fault; fuzzy characters; fuzzy neural network; insulator contamination detection; online monitoring system; root mean square; surface leakage current; Condition monitoring; Fuzzy neural networks; Humidity; Insulation; Insulator testing; Leak detection; Leakage current; Root mean square; Surface contamination; Temperature; Contamination detection; Fuzzy; Insulators; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258868

Filename

4028789