• DocumentCode
    1339200
  • Title

    Experimental testing of the artificial neural network based protection of power transformers

  • Author

    Zaman, M.R. ; Rahman, M.A.

  • Author_Institution
    Fac. of Eng. & Appl. Sci., Memorial Univ. of Newfoundland, St. John´´s, Nfld., Canada
  • Volume
    13
  • Issue
    2
  • fYear
    1998
  • fDate
    4/1/1998 12:00:00 AM
  • Firstpage
    510
  • Lastpage
    517
  • Abstract
    This work presents a novel technique to distinguish between magnetizing inrush and internal fault currents of a power transformer. The proposed differential algorithm is based on an artificial neural network (ANN) and unlike the existing relaying techniques, this method is independent of the harmonic contents of the differential current. A novel neural network is designed and trained using the back-propagation algorithm with experimental data. After training the network, simulation and on-line tests are carried out to evaluate the performance of the ANN based algorithm under different fault and energization conditions. Both simulation and experimental results are quite satisfactory
  • Keywords
    backpropagation; fault currents; neural nets; power engineering computing; power transformer protection; ANN; artificial neural network based protection; back-propagation algorithm; data acquisition; differential algorithm; differential current; energization conditions; fault conditions; harmonic contents; internal fault currents; magnetizing inrush currents; on-line tests; power transformer; power transformers; simulation; Artificial neural networks; Cost function; Current measurement; Neurons; Phase transformers; Power transformers; Surge protection; Testing; Transfer functions;
  • fLanguage
    English
  • Journal_Title
    Power Delivery, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8977
  • Type

    jour

  • DOI
    10.1109/61.660922
  • Filename
    660922