Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258868
