• DocumentCode
    3484121
  • Title

    Power flow model based on artificial neural networks

  • Author

    Müller, Heloisa H. ; Rider, Marcos J. ; Castro, Carlos A. ; Paucar, V. Leonardo

  • Author_Institution
    Univ. of Campinas, Campinas
  • fYear
    2005
  • fDate
    27-30 June 2005
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this paper a model and a methodology for using artificial neural networks to solve the load flow problem are proposed. An evaluation of the input data required by the ANN as well as its architecture is also presented. The ANN model used in this paper is the multilayer perceptron, and the training process is based on the second order Levenberg-Marquardt method. The proposed methodology was evaluated using the Ward-Hale 6 bus, the IEEE 14 bus and the IEEE 30 bus systems, considering normal operating conditions (base case) and different contingency scenarios, including different load/generation patterns. The simulation results show the excellent performance of the ANN, proving its ability to solve the load flow problem.
  • Keywords
    learning (artificial intelligence); load flow; multilayer perceptrons; power system analysis computing; IEEE 14 bus; IEEE 30 bus; Levenberg-Marquardt method; Ward-Hale 6 bus; artificial neural networks; multilayer perceptron; power flow model; Artificial intelligence; Artificial neural networks; Control systems; Equations; Load flow; Power engineering and energy; Power system analysis computing; Power system control; Power system planning; Power systems; Load flow; artificial neural networks; electric power systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Tech, 2005 IEEE Russia
  • Conference_Location
    St. Petersburg
  • Print_ISBN
    978-5-93208-034-4
  • Electronic_ISBN
    978-5-93208-034-4
  • Type

    conf

  • DOI
    10.1109/PTC.2005.4524546
  • Filename
    4524546