Improving reliability assessment of transformer thermal top-oil model parameters estimated from measured data

Lida Jauregui-Rivera, Xiaolin Mao, Daniel Tylavsky

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

This paper presents a methodology for assessing the reliability of thermal-model parameters for transformers estimated from measured data. The methodology uses statistical bootstrapping to calculate confidence levels (CL) and confidence intervals (CI). Bootstrapping allows us to make a small dataset look statistically larger, which allows a precise estimate of the transformer thermal model's reliability. The proposed methodology is tested on a 167-MVA oil-forced air-forced transformer. The CIs are evaluated with and without bootstrapping and the reliability indices are compared. The results show that the CI and CL values with bootstrapping are more consistently reproducible than the ones derived without bootstrapping.

Original languageEnglish (US)
Pages (from-to)169-176
Number of pages8
JournalIEEE Transactions on Power Delivery
Volume24
Issue number1
DOIs
StatePublished - Jan 1 2009

Keywords

  • Bootstrapping
  • Confi-dence levels (CLs)
  • Confidence intervals (CIs)
  • Least squares regression
  • Parameter estimation
  • Top-oil temperature
  • Transformer thermal modeling

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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