• DocumentCode
    2945581
  • Title

    Artificial neural networks based steady state security analysis of power systems

  • Author

    Shukla, Meera ; Abdelrahman, Mohamed

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Tennessee Technol. Univ., Cookeville, TN, USA
  • fYear
    2004
  • fDate
    2004
  • Firstpage
    266
  • Lastpage
    269
  • Abstract
    The focus of this paper is to present an artificial neural network based methodology to assess the steady state security of a power system. The security of the system is assessed on the basis of the voltage profile at each bus with reference to changes in generation and load in the system. The input to the neural network is the voltage level at each bus. The ANN used is a feedforward multilayer network trained with a backpropagation algorithm. The output of the ANN classifies the security of the power system into normal, alert and emergency states. An IEEE 14-bus system is considered to demonstrate the results of the methodology.
  • Keywords
    backpropagation; electric power generation; feedforward neural nets; multilayer perceptrons; network topology; power system security; artificial neural networks; backpropagation algorithm; feedforward multilayer network; multilayered perceptrons; neural network topology; power generation; power systems; static security; steady state security analysis; voltage level; Artificial neural networks; Information security; Load flow; Power engineering and energy; Power engineering computing; Power system analysis computing; Power system reliability; Power system security; Steady-state; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, 2004. Proceedings of the Thirty-Sixth Southeastern Symposium on
  • ISSN
    0094-2898
  • Print_ISBN
    0-7803-8281-1
  • Type

    conf

  • DOI
    10.1109/SSST.2004.1295661
  • Filename
    1295661