Data driven modeling for power transformer lifespan evaluation

Trappey, Charles, Trappey, Amy, Ma, Lin, & Tsao, Wan-Ting (2014) Data driven modeling for power transformer lifespan evaluation. Journal of Systems Science and Systems Engineering, 23(1), pp. 80-93.

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Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Impact and interest:

1 citations in Scopus
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1 citations in Web of Science®

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ID Code: 88669
Item Type: Journal Article
Refereed: Yes
Keywords: Condition based maintenance (CBM), prognostics and health management (PHM), ogistic regression, remaining life predictio, sustainable engineering asset management
DOI: 10.1007/s11518-014-5227-z
ISSN: 1004-3756
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2014
Deposited On: 02 Nov 2015 23:02
Last Modified: 02 Nov 2015 23:02

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