Neural net and expert system diagnose transformer faults

Dissolved gas-in-oil analysis (DGA) is a common practice in transformer incipient fault diagnosis. The analysis techniques include the conventional key gas method, ratio methods, and artificial intelligence methods. Application of artificial intelligence (Al) techniques have shown very promising res...

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Bibliographic Details
Published in:IEEE computer applications in power 2000-01, Vol.13 (1), p.50-55
Main Authors: Zhenyuan Wang, Yilu Liu, Griffin, P.J.
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Diagnostic expert systems
Dissolved gas analysis
Evolutionary computation
Fault diagnosis
Fuzzy logic
Neural networks
Oil insulation
Petroleum
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Summary:Dissolved gas-in-oil analysis (DGA) is a common practice in transformer incipient fault diagnosis. The analysis techniques include the conventional key gas method, ratio methods, and artificial intelligence methods. Application of artificial intelligence (Al) techniques have shown very promising results. The methods include fuzzy logic, expert systems (EPS), evolutionary algorithms (EA), and artificial neural networks (ANN). A transformer incipient fault diagnosis system (ANNEPS) was developed over a period of 5 years at Virginia Tech, collaborating with Doble Engineering Company. The system can detect thermal faults (distinguishing overheating of oil from that of cellulose and between four overheating stages and overheating of oil), low-energy discharge (partial discharge), high-energy discharge (arcing), and cellulose degradation.
ISSN:0895-0156
1558-4151
DOI:10.1109/67.814667