• DocumentCode
    1532137
  • Title

    Machine learning approaches to power-system security assessment

  • Author

    Wehenkel, Louis

  • Author_Institution
    Dept. of Electr. Eng., Liege Univ., Belgium
  • Volume
    12
  • Issue
    5
  • fYear
    1997
  • Firstpage
    60
  • Lastpage
    72
  • Abstract
    The paper discusses a framework that uses machine learning and other automatic-learning methods to assess power-system security. The framework exploits simulation models in parallel to screen diverse simulation scenarios of a system, yielding a large database. Using data mining techniques, the framework extracts synthetic information about the simulated system´s main features from this database
  • Keywords
    deductive databases; digital simulation; expert systems; knowledge acquisition; learning (artificial intelligence); power system analysis computing; power system security; very large databases; automatic-learning methods; data mining; expert systems; large database; machine learning; power-system security assessment; simulation models; Data mining; Data security; Decision making; Information security; Machine learning; Numerical simulation; Power system analysis computing; Power system planning; Power system security; Power system simulation;
  • fLanguage
    English
  • Journal_Title
    IEEE Expert
  • Publisher
    ieee
  • ISSN
    0885-9000
  • Type

    jour

  • DOI
    10.1109/64.621229
  • Filename
    621229
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1532137