@inproceedings{bffde17734e8407fa117296bc9c02ee8,
title = "Data screening to improve transformer thermal model reliability",
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 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.",
keywords = "ANSI C57.91, Top-oil temperature, Transformer, Transformer thermal modeling",
author = "Daniel Tylavsky and Mao Xiaolin and McCulla, {Gary A.}",
year = "2005",
month = dec,
day = "1",
doi = "10.1109/NAPS.2005.1560589",
language = "English (US)",
isbn = "0780392558",
series = "Proceedings of the 37th Annual North American Power Symposium, 2005",
pages = "560--568",
booktitle = "Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006",
note = "37th Annual North American Power Symposium, 2005 ; Conference date: 23-10-2005 Through 25-10-2005",
}