Data screening to improve transformer thermal model reliability

Daniel Tylavsky, Mao Xiaolin, Gary A. McCulla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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 using these models is to assess their reliability and to improve it 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)
Title of host publicationProceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006
Number of pages9
StatePublished - Dec 1 2005
Event37th Annual North American Power Symposium, 2005 - Ames, IA, United States
Duration: Oct 23 2005Oct 25 2005

Publication series

NameProceedings of the 37th Annual North American Power Symposium, 2005


Other37th Annual North American Power Symposium, 2005
Country/TerritoryUnited States
CityAmes, IA


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

ASJC Scopus subject areas

  • Engineering(all)


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