• DocumentCode
    2271505
  • Title

    Online voltage stability contingency selection using improved RSI method based on ANN solution

  • Author

    Zhang, Y. ; Zhou, Z.

  • Author_Institution
    RTDS Technol. Inc., Winnipeg, Man., Canada
  • Volume
    2
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    888
  • Abstract
    This paper proposed an efficient methodology for voltage stability contingency selection, named improved RSI method based on ANN solution. The basic idea of the reactive support index (RSI) method is adopted, while the artificial neural network (ANN) solution is employed to handle the nonlinear relationship between the RSI and the voltage stability margin variation. The improved methodology combines the advantages of its clear physical meaning from the RSI method and its high accuracy from using the ANN. The method is tested on the IEEE 39 buses test system. Numerical studies illustrate that the new method has good performance on both the accuracy and speed.
  • Keywords
    neural nets; power system analysis computing; power system dynamic stability; ANN; IEEE 39 buses test system; improved RSI method; improved reactive support index method; online voltage stability contingency selection; voltage stability margin; Artificial neural networks; Drives; Neural networks; Power engineering computing; Power generation; Power system planning; Power system stability; Reactive power; System testing; Voltage control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society Winter Meeting, 2002. IEEE
  • Print_ISBN
    0-7803-7322-7
  • Type

    conf

  • DOI
    10.1109/PESW.2002.985134
  • Filename
    985134