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.

View at publisher

Abstract

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
Search Google Scholar™
1 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page