• DocumentCode
    2191991
  • Title

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

  • Author

    Bahbah, A. ; Girgis, A.

  • Author_Institution
    Clemson Univ., SC, USA
  • fYear
    2004
  • fDate
    6-10 June 2004
  • Abstract
    Summary form only given. Recurrent radial basis function (RBF), and multi-layer 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 multi-machine 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 post-fault system configuration.
  • Keywords
    angular velocity; electric generators; fault location; multilayer perceptrons; power engineering computing; power system faults; power system transient stability; radial basis function networks; angles prediction; angular velocities prediction; data generation scheme; fault location; multilayer perceptron; 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
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society General Meeting, 2004. IEEE
  • Conference_Location
    Denver, CO
  • Print_ISBN
    0-7803-8465-2
  • Type

    conf

  • DOI
    10.1109/PES.2004.1372769
  • Filename
    1372769