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2014 47th Hawaii International Conference on System Sciences (2003)
Big Island, Hawaii
Jan. 6, 2003 to Jan. 9, 2003
ISBN: 0-7695-1874-5
pp: 63c
Paula Kaptain , Western Area Power Administration
John Work , Western Area Power Administration
David Linder , Colorado School of Mines
Dr. Rahmat Shoureshi , Colorado School of Mines
Tim Norick , Colorado School of Mines
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.
Paula Kaptain, John Work, David Linder, Dr. Rahmat Shoureshi, Tim Norick, "Sensor Fusion and Complex Data Analysis for Predictive Maintenance", 2014 47th Hawaii International Conference on System Sciences, vol. 02, no. , pp. 63c, 2003, doi:10.1109/HICSS.2003.1173904
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