• DocumentCode
    3427429
  • Title

    Artificial neural network approach for locating faults in power transmission system

  • Author

    Teklic, Ljupko ; Filipovic-Grcic, Bozidar ; Pavicic, Ivan

  • Author_Institution
    HEP-Transm. Syst. Operator, Zagreb, Croatia
  • fYear
    2013
  • fDate
    1-4 July 2013
  • Firstpage
    1425
  • Lastpage
    1430
  • Abstract
    This paper presents fault location recognition in transmission power system using artificial neural network (ANN). Single phase short circuit on 110 kV transmission line fed from both ends was analysed with various fault impedances, since it is the most common fault in power system. Load flow and short circuit calculations were performed with EMTP-RV software. Calculation results including currents and voltages at both line ends were used for training ANN in Matlab in order to obtain correct fault location and fault impedance, even for those cases that ANN has never encountered before. The network was trained with back propagation algorithm. Test results show that this approach provides robust and accurate location of faults for a variety of power system operating conditions and gives an accurate fault impedance assessment.
  • Keywords
    EMTP; backpropagation; fault location; load flow; mathematics computing; neural nets; power transmission faults; power transmission lines; ANN; EMTP-RV software; Matlab; artificial neural network approach; backpropagation algorithm; fault impedance assessment; fault location recognition; load flow; power transmission line system; single phase short circuit; training; voltage 110 kV; Artificial neural networks; Circuit faults; Conductors; Fault location; Impedance; Load flow; Power transmission lines; Artificial Neural Network; Fault Location; Feed Forward Neural Network; Transmission Lines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    EUROCON, 2013 IEEE
  • Conference_Location
    Zagreb
  • Print_ISBN
    978-1-4673-2230-0
  • Type

    conf

  • DOI
    10.1109/EUROCON.2013.6625165
  • Filename
    6625165