The Community for Technology Leaders
36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the (2003)
Big Island, Hawaii
Jan. 6, 2003 to Jan. 9, 2003
ISBN: 0-7695-1874-5
pp: 63c
Dr. Rahmat Shoureshi , Colorado School of Mines
Tim Norick , Colorado School of Mines
David Linder , Colorado School of Mines
John Work , Western Area Power Administration
Paula Kaptain , Western Area Power Administration
ABSTRACT
An essential step toward the development of an intelligent substation is to provide self-diagnosing capability at the equipment level. Transformers, circuit breakers and other substation equipment should be enabled to detect their potential failures and make life expectancy prediction without human interference. This paper focuses on the development of an on-line equipment diagnostics using artificial intelligence and a nonlinear observer to prevent catastrophic failures in substation equipment, thus providing preventive maintenance. Key elements of the system are a nonlinear observer, system identifier, and fault detector that use a uniquely designed neuro-fuzzy inference engine. Experimental results from application of this system to a distribution transformer are presented.
INDEX TERMS
null
CITATION

P. Kaptain, J. Work, D. Linder, D. R. Shoureshi and T. Norick, "Sensor Fusion and Complex Data Analysis for Predictive Maintenance," 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the(HICSS), Big Island, Hawaii, 2003, pp. 63c.
doi:10.1109/HICSS.2003.1173904
93 ms
(Ver 3.3 (11022016))