• DocumentCode
    1515446
  • Title

    Preliminary results on using artificial neural networks for security assessment [of power systems]

  • Author

    Aggoune, M. ; El-Sharkawa, M.A. ; Park, D.C. ; Damborg, M.J. ; Marks, R.J., II

  • Author_Institution
    Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
  • Volume
    6
  • Issue
    2
  • fYear
    1991
  • fDate
    5/1/1991 12:00:00 AM
  • Firstpage
    890
  • Lastpage
    896
  • Abstract
    Artificial neural network techniques (ANNs) are explored as a tool to assess the dynamic security of power systems. The basic role of ANNs is to provide assessment of the system´s stability based on training examples from offline analysis. Such an assessment would be useful as an operations aid. In essence, ANNs interpolate among planning analysis data. The authors present the results of a study to assess the capability of ANNs to learn from offline stability analysis results and give stability assessments when queried with data representing the current system status. The important feature of the result is that correct stability assessments are provided by the ANN not only when it is queried with an element of the training set of data but also under other operating conditions
  • Keywords
    expert systems; neural nets; power system analysis computing; stability; artificial neural networks; dynamic security; offline analysis; planning; power system analysis computing; stability; Artificial neural networks; Data security; Monitoring; Power system analysis computing; Power system dynamics; Power system planning; Power system security; Power system stability; Stability analysis; Steady-state;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/59.76740
  • Filename
    76740