Evaluation of hottest-spot temperature models using field measured transformer data

Oluwaseun Amoda, Daniel Tylavsky, Gary McCulla, Wesley Knuth

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The acceptability of two hottest-spot temperature models is assessed in this paper. The first model, contained in the IEEE loading guide, is shown not to accurately account for the effects of the top-oil temperature (TOT) variation on the hottest-spot temperature. A new model which accounts for TOT variation is derived. The original and modified models are linearized and fitted to field data using linear regression to obtain optimal parameter estimates. Comparison of the parameter estimates and prediction simulations show that even though the original model is not structurally accurate, it, as well as the modified model, are acceptable for prediction purposes. The method of nonlinear regression is also used in an attempt to find better parameter estimates for the nonlinear modified model. It is shown that parameter estimates for the nonlinear model are inferior to those obtained for the linear models.

Original languageEnglish (US)
Article number2
JournalInternational Journal of Emerging Electric Power Systems
Volume12
Issue number5
DOIs
StatePublished - 2011

Fingerprint

Temperature
Linear regression
Oils

Keywords

  • hottest spot temperature
  • temperature-models
  • top oil
  • transformers

ASJC Scopus subject areas

  • Energy Engineering and Power Technology

Cite this

Evaluation of hottest-spot temperature models using field measured transformer data. / Amoda, Oluwaseun; Tylavsky, Daniel; McCulla, Gary; Knuth, Wesley.

In: International Journal of Emerging Electric Power Systems, Vol. 12, No. 5, 2, 2011.

Research output: Contribution to journalArticle

@article{71e2c29dbddc4e9599376e292441ddfa,
title = "Evaluation of hottest-spot temperature models using field measured transformer data",
abstract = "The acceptability of two hottest-spot temperature models is assessed in this paper. The first model, contained in the IEEE loading guide, is shown not to accurately account for the effects of the top-oil temperature (TOT) variation on the hottest-spot temperature. A new model which accounts for TOT variation is derived. The original and modified models are linearized and fitted to field data using linear regression to obtain optimal parameter estimates. Comparison of the parameter estimates and prediction simulations show that even though the original model is not structurally accurate, it, as well as the modified model, are acceptable for prediction purposes. The method of nonlinear regression is also used in an attempt to find better parameter estimates for the nonlinear modified model. It is shown that parameter estimates for the nonlinear model are inferior to those obtained for the linear models.",
keywords = "hottest spot temperature, temperature-models, top oil, transformers",
author = "Oluwaseun Amoda and Daniel Tylavsky and Gary McCulla and Wesley Knuth",
year = "2011",
doi = "10.2202/1553-779X.2734",
language = "English (US)",
volume = "12",
journal = "International Journal of Emerging Electric Power Systems",
issn = "1553-779X",
publisher = "Berkeley Electronic Press",
number = "5",

}

TY - JOUR

T1 - Evaluation of hottest-spot temperature models using field measured transformer data

AU - Amoda, Oluwaseun

AU - Tylavsky, Daniel

AU - McCulla, Gary

AU - Knuth, Wesley

PY - 2011

Y1 - 2011

N2 - The acceptability of two hottest-spot temperature models is assessed in this paper. The first model, contained in the IEEE loading guide, is shown not to accurately account for the effects of the top-oil temperature (TOT) variation on the hottest-spot temperature. A new model which accounts for TOT variation is derived. The original and modified models are linearized and fitted to field data using linear regression to obtain optimal parameter estimates. Comparison of the parameter estimates and prediction simulations show that even though the original model is not structurally accurate, it, as well as the modified model, are acceptable for prediction purposes. The method of nonlinear regression is also used in an attempt to find better parameter estimates for the nonlinear modified model. It is shown that parameter estimates for the nonlinear model are inferior to those obtained for the linear models.

AB - The acceptability of two hottest-spot temperature models is assessed in this paper. The first model, contained in the IEEE loading guide, is shown not to accurately account for the effects of the top-oil temperature (TOT) variation on the hottest-spot temperature. A new model which accounts for TOT variation is derived. The original and modified models are linearized and fitted to field data using linear regression to obtain optimal parameter estimates. Comparison of the parameter estimates and prediction simulations show that even though the original model is not structurally accurate, it, as well as the modified model, are acceptable for prediction purposes. The method of nonlinear regression is also used in an attempt to find better parameter estimates for the nonlinear modified model. It is shown that parameter estimates for the nonlinear model are inferior to those obtained for the linear models.

KW - hottest spot temperature

KW - temperature-models

KW - top oil

KW - transformers

UR - http://www.scopus.com/inward/record.url?scp=80052569986&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80052569986&partnerID=8YFLogxK

U2 - 10.2202/1553-779X.2734

DO - 10.2202/1553-779X.2734

M3 - Article

AN - SCOPUS:80052569986

VL - 12

JO - International Journal of Emerging Electric Power Systems

JF - International Journal of Emerging Electric Power Systems

SN - 1553-779X

IS - 5

M1 - 2

ER -