Data driven modeling for power transformer lifespan evaluation

Charles V. Trappey, Amy J.C. Trappey, Lin Ma, Wan Ting Tsao

Research output: Contribution to journalArticle

7 Scopus citations

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.

Original languageEnglish
Pages (from-to)80-93
Number of pages14
JournalJournal of Systems Science and Systems Engineering
Volume23
Issue number1
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Condition based maintenance (CBM)
  • logistic regression
  • prognostics and health management (PHM)
  • remaining life prediction
  • sustainable engineering asset management

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