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New Phenemenon on Power Transformers and Fault Identification Using Artificial Neural Networks

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Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

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Abstract

In this paper voltage recovery after voltage dip that cause magnetizing inrush current which is a new phenomenon in power transformers are discussed and a new technique is proposed to distinquish internal fault conditions from no-fault conditions that is also containing these new phenomenons. The proposed differential algorithm is based on Artificial Neural Network (ANN). The training and testing data sets are obtained using SIMPOW-STRI power system simulation program and laboratory transformer. A novel neural network is designed and trained using back-propagation algorithm. It is seen that the proposed network is well trained and able to discriminate no-fault examples from fault examples with high accuracy.

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References

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Author information

Authors and Affiliations

  1. Engineering Faculty, Electrical Engineering Department, Kocaeli University, 41040, Kocaeli, Turkey

    Mehlika Şengül, Semra Öztürk, Hasan Basri Çetinkaya & Tarık Erfidan

Authors
  1. Mehlika Şengül
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  2. Semra Öztürk
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  3. Hasan Basri Çetinkaya
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  4. Tarık Erfidan
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Şengül, M., Öztürk, S., Çetinkaya, H.B., Erfidan, T. (2006). New Phenemenon on Power Transformers and Fault Identification Using Artificial Neural Networks. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_80

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  • DOI: https://doi.org/10.1007/11840930_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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