DocumentCode :
2169943
Title :
Transformer oil diagnosis using fuzzy logic and neural networks
Author :
Dukarm, James J.
Author_Institution :
Delta-X Res., Victoria, BC, Canada
fYear :
1993
fDate :
14-17 Sep 1993
Firstpage :
329
Abstract :
Dissolved-gas analysis (DGA) is widely used for detection and diagnosis of incipient faults in large oil-filled transformers. Many factors contribute to extreme “noisiness” in the data and make early fault detection and diagnosis difficult. This paper shows how fuzzy logic and neural networks are being used to automate standard DGA methods and improve their usefulness for power transformer fault diagnosis. The use of neural networks for DGA-with or without fuzzy logic-is discussed, and some related work is described briefly
Keywords :
electric breakdown of liquids; fault location; fuzzy logic; insulating oils; insulation testing; neural nets; power engineering computing; power transformers; transformer insulation; transformer testing; dissolved-gas analysis; fuzzy logic; insulating oil testing; neural networks; noisiness; power transformer fault diagnosis; standard DGA methods; Dissolved gas analysis; Fault detection; Fault diagnosis; Fuzzy logic; Hydrogen; Neural networks; Oil insulation; Petroleum; Power transformer insulation; Power transformers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 1993. Canadian Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2416-1
Type :
conf
DOI :
10.1109/CCECE.1993.332323
Filename :
332323
Link To Document :
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