NDLI: Artificial intelligence in power equipment fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Zhenyuan Wang Yilu Liu Nien-Chung Wang Tzong-Yih Guo Huang, F.T.C. Griffin, P.J.
Copyright Year	2000
Description	Author affiliation: Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA (Zhenyuan Wang)
Abstract	An artificial neural network and expert system based diagnostic system for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA) has been developed. This system takes advantage of the inherent positive features of each method and offers a better diagnostic accuracy. The knowledge base of its expert system (EPS) is derived from IEEE and IEC DGA standards and expert experiences to include as many known diagnosis rules as possible. The topology and training data set of its artificial neural network (ANN) are carefully selected to extract known as well as unknown diagnostic rules implicitly. The combination of the ANN and EPS outputs has an optimization mechanism to ensure high diagnostic accuracy. This work has been reported in the past. In this paper, the new development in fault location identification using logistic regression analysis and neural network is introduced. Test results show that it is possible not only to diagnose and predict fault types, but also to predict the location of the fault.
Starting Page	128
Ending Page	133
File Size	645723
Page Count	6
File Format	PDF
ISBN	0780359356
DOI	10.1109/PESW.2000.849942
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-01-23
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial intelligence Fault diagnosis Artificial neural networks Dissolved gas analysis Diagnostic expert systems IEC standards Network topology Training data Data mining Fault location
Content Type	Text
Resource Type	Article

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