SPECIAL ISSUE: Learning Under Uncertain Environment and Its Application to Pattern Recognition; Edited by X. Wang, Y. Tang and D. YeunNo Access

A NEW FUZZY NEURAL NETWORK STRATEGY OF INSULATORS CONTAMINATION DETECTION FOR POWER SYSTEM TRANSMISSION LINE

YUPING LU

School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China

Author for correspondence.

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MIN YU

School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China

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L. L. LAI

Energy Systems Group, City University, London EC1V0HB, UK

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, and

XIA LIN

School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China

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https://doi.org/10.1142/S0218001408006181Cited by:1 (Source: Crossref)

Abstract

The detection of insulators contamination is difficult in power systems because many factors can influence the pollution. 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. It ignores the influence of environmental factors, such as temperature, humidity, etc. As these factors are fuzzy-characterized, a new method based on Fuzzy Neural Network (FNN) is proposed to improve traditional insulation contamination detection. The renewed structure of FNN is put forward. The evaluation of contamination severity of insulators is achieved through FNN, which are trained by the field samples. The results prove the validity of the method proposed in the paper and can be used to eliminate the insulator from flashover fault and improve the condition-based maintenance (CBM).

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