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Soft Computing: State of the Art Theory and Novel Applications pp 255–268Cite as

System Modeling and Forecasting with Evolving Fuzzy Algorithms

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 291))

Abstract

Many of the activities associated with the systems planning and operation require forecasts of future events. For instance, thermal models of distribution transformers with core immersed in oil are of utmost importance for power systems operation and safety. Its hot spot temperature determines the degradation speed of the insulation material and parts. High temperatures cause loss of mechanical stiffness, generating failures. Insulation degradation determines the lifetime limits of power transformers. Thermal models are needed to generate reliable data for lifetime forecasting methodologies. One of the greatest difficulties in thermal modeling is the non stationary nature of the transformers due to aging, parts replacement, and operational overloads. In this paper we use an evolving fuzzy model to build adaptive thermal models of distribution transformers. The model is an evolving fuzzy linear regression tree. The tree grows adaptively by replacing leaves with subtrees whenever they improve the model quality. The performance of the evolving regression is evaluated using actual data from an experimental transformer. The results suggest that the evolving fuzzy tree approach outperforms current state of the art models.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Federal University of Minas Gerais, Av. Antônio Carlos, 6627, 31270-901, Belo Horizonte, MG, Brazil

    Andre Lemos & Walmir Caminhas

  2. Institute of Economics, University of Campinas, 13083-857, Campinas, SP, Brazil

    Rosangela Ballini

  3. School of Electrical and Computer Engineering, University of Campinas, 13083-852, Campinas, SP, Brazil

    Fernando Gomide

Authors
  1. Andre Lemos
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  2. Rosangela Ballini
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  3. Walmir Caminhas
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  4. Fernando Gomide
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Corresponding author

Correspondence to Andre Lemos .

Editor information

Editors and Affiliations

  1. , Iona College, Machine Intelligence Institute, New Rochelle, 10801, New York, USA

    Ronald R. Yager

  2. Information Technologies of the, Republic of Azerbaijan, Ministry of Communications and, Zarifa Aliyeva str. 33, Baku, AZ1000, Azerbaijan

    Ali M. Abbasov

  3. , Department of Electrical, University of Alberta, ECERF Building W5-023, Edmonton, T6G 2V4, Alberta, Canada

    Marek Z. Reformat

  4. , Department of Information Technology, Azerbaijan Technical University, 25 H.Cavid Ave., Baku, AZ1073, Azerbaijan

    Shahnaz N. Shahbazova

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Lemos, A., Ballini, R., Caminhas, W., Gomide, F. (2013). System Modeling and Forecasting with Evolving Fuzzy Algorithms. In: Yager, R., Abbasov, A., Reformat, M., Shahbazova, S. (eds) Soft Computing: State of the Art Theory and Novel Applications. Studies in Fuzziness and Soft Computing, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34922-5_18

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  • DOI: https://doi.org/10.1007/978-3-642-34922-5_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34921-8

  • Online ISBN: 978-3-642-34922-5

  • eBook Packages: EngineeringEngineering (R0)

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