Recurrent neural approaches for power transformers thermal modeling

  • Authors:

  • Michel Hell;Luiz Secco;Pyramo Costa, Jr.;Fernando Gomide

  • Affiliations:

  • State University of Campinas – UNICAMP, Campinas, SP, Brazil;Pontifical Catholic University of Minas Gerais – PUC-MG, Belo Horizonte, MG, Brazil;Pontifical Catholic University of Minas Gerais – PUC-MG, Belo Horizonte, MG, Brazil;State University of Campinas – UNICAMP, Campinas, SP, Brazil

  • Venue:
  • PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
  • Year:
  • 2005

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Abstract

This paper introduces approaches for power transformer thermal modeling based on two conceptually different recurrent neural networks. The first is the Elman recurrent neural network model whereas the second is a recurrent neural fuzzy network constructed with fuzzy neurons based on triangular norms. These two models are used to model the thermal behavior of power transformers using data reported in literature. The paper details the neural modeling approaches and discusses their main capabilities and properties. Comparisons with the classic deterministic model and static neural modeling approaches are also reported. Computational experiments suggest that the recurrent neural fuzzy-based modeling approach outperforms the remaining models from both, computational processing speed and robustness point of view.