Transformer thermal modeling: Improving reliability using data quality control

Daniel Tylavsky, Xiaolin Mao, Gary A. McCulla

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

Eventually, all large transformers will be dynamically loaded using models updated regularly from field-measured data. Models obtained from measured data give more accurate results than models based on transformer heat-run tests and can be easily generated using data already routinely monitored. The only significant challenge to use these models is to assess their reliability and improve their reliability as much as possible. In this work, we use data-quality control and data-set screening to show that model reliability can be increased by about 50% while decreasing model prediction error. These results are obtained for a linear model. We expect similar results for the nonlinear models currently being explored.

Original languageEnglish (US)
Pages (from-to)1357-1366
Number of pages10
JournalIEEE Transactions on Power Delivery
Volume21
Issue number3
DOIs
StatePublished - Jul 1 2006

    Fingerprint

Keywords

  • ANSI C57.91
  • Top-oil temperature
  • Transformer
  • Transformer thermal modeling

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this