• DocumentCode
    1179676
  • Title

    Artificial Neural Networks in Power System Restoration

  • Author

    Bretas, Arturo S. ; Phadke, Arun G.

  • Author_Institution
    University of Sao Paulo, Sao Paulo, Brazil; Virginia Polytechnic Institute and State University, Blacksburg, Virginia
  • Volume
    22
  • Issue
    10
  • fYear
    2002
  • Firstpage
    61
  • Lastpage
    61
  • Abstract
    Power system restoration (PSR) has been a subject of study for many years. In recent years many techniques were proposed to solve the limitations of the predetermined restoration guidelines and procedures used by a majority of system operators to restore a system following the occurrence of a wide area disturbance. This paper discusses limitations encountered in some currently used PSR techniques and a proposed improvement based on artificial neural networks (ANN). The proposed scheme is tested on a 162 bus transmission system and compared with a breadth-search restoration scheme. The results indicate that the use of ANN in power system restoration is a feasible option that should be considered for real-time applications.
  • Keywords
    Artificial neural networks; Electrodes; Grounding; Impedance; Load modeling; Power system modeling; Power system relaying; Power system restoration; Protection; Protective relaying; Artificial neural networks; artificial intelligence; power system restoration; wide-area disturbances;
  • fLanguage
    English
  • Journal_Title
    Power Engineering Review, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1724
  • Type

    jour

  • DOI
    10.1109/MPER.2002.4311755
  • Filename
    4311755