Title :
Artificial intelligence in OLTC fault diagnosis using dissolved gas-in-oil information
Author :
Wang, Zhenyuan ; Liu, Yilu ; Griffin, Paul J.
Author_Institution :
Dept. of Electr. Eng., Virginia Tech., Blacksburg, VA, USA
Abstract :
On-load tap changers (OLTCs) are one of the most problematic components of power transformers. Detecting incipient faults in OLTCs is one of the key challenges facing the power equipment predictive maintenance community. This paper addresses the issue with an artificial intelligence approach, where logistic analysis is used to find the principal gases related to the fault conditions and neural networks are used to improve the performance of the diagnosis. The developed techniques could be integrated into a power transformer incipient fault diagnosis systems
Keywords :
artificial intelligence; computerised instrumentation; fault diagnosis; insulation testing; maintenance engineering; power transformer insulation; power transformer testing; OLTC fault diagnosis; artificial intelligence; dissolved gas-in-oil analysis; incipient fault detection; incipient fault diagnosis systems; logistic analysis; on-load tap changers; power transformers; predictive maintenance; Artificial intelligence; Artificial neural networks; Fault detection; Fault diagnosis; Gases; Logistics; On load tap changers; Performance analysis; Power transformers; Predictive maintenance;
Conference_Titel :
Power Engineering Society Summer Meeting, 2000. IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-6420-1
DOI :
10.1109/PESS.2000.867370
