• DocumentCode
    976072
  • Title

    New method for generators´ angles and angular velocities prediction for transient stability assessment of multimachine power systems using recurrent artificial neural network

  • Author

    Bahbah, Amr G. ; Girgis, Adly A.

  • Author_Institution
    Clemson Univ., SC, USA
  • Volume
    19
  • Issue
    2
  • fYear
    2004
  • fDate
    5/1/2004 12:00:00 AM
  • Firstpage
    1015
  • Lastpage
    1022
  • Abstract
    Recurrent radial basis function (RBF) and multilayer perceptron (MLP) artificial neural network (ANN) schemes are proposed for dynamic system modeling, and generators´ angles and angular velocities prediction for transient stability assessment. The method is presented for multimachine power systems. In this scheme, transient stability is assessed based on monitoring generators´ angles and angular velocities with time, and checking whether they exceed the specified limits for system stability or not. Data generation schemes have been proposed. The proposed recurrent ANN scheme is not sensitive to fault locations. It is only dependent on the postfault system configuration.
  • Keywords
    angular velocity; multilayer perceptrons; power engineering computing; power system transient stability; radial basis function networks; angular velocity prediction; data generation schemes; generator angles; multilayer perceptrons; multimachine power system; recurrent artificial neural network; recurrent radial basis function; transient stability assessment; Angular velocity; Artificial neural networks; Monitoring; Multilayer perceptrons; Power generation; Power system dynamics; Power system faults; Power system modeling; Power system stability; Power system transients;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2004.826765
  • Filename
    1295012