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

  • Mehlika Şengül
  • Semra Öztürk
  • Hasan Basri Çetinkaya
  • Tarık Erfidan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

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.

Keywords

Artificial Neural Network Harmonic Component Power Transformer Internal Fault Fault Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mehlika Şengül
    • 1
  • Semra Öztürk
    • 1
  • Hasan Basri Çetinkaya
    • 1
  • Tarık Erfidan
    • 1
  1. 1.Engineering Faculty, Electrical Engineering DepartmentKocaeli UniversityKocaeliTurkey

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