Prediction of top-oil temperature for transformers using neural networks

Qing He, Jennie Si, Daniel Tylavsky

Research output: Contribution to journalArticlepeer-review

99 Scopus citations


Artificial neural networks represent a growing new technology as indicated by a wide range of proposed applications. At a substation, when the transformer's windings get too hot, either load has to be reduced as a short-term solution, or another transformer by has to be installed as a long-term plan. To decide on whether to deploy either of these two strategies, one should be able to predict the transformer temperature accurately. This paper explores the possibility of using artificial neural networks for predicting top-oil temperature of transformers. Static neural networks, temporal processing networks and recurrent networks are explored for predicting the top-oil temperature of transformers. The results using different networks will be compared with the auto regression linear model.

Original languageEnglish (US)
Pages (from-to)1205-1211
Number of pages7
JournalIEEE Transactions on Power Delivery
Issue number4
StatePublished - Oct 2000

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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