• DocumentCode
    601480
  • Title

    Reduced Model for Power System State Estimation Using Artificial Neural Networks

  • Author

    Onwuachumba, Amamihe ; Yunhui Wu ; Musavi, Mohamad

  • Author_Institution
    Electr. & Comput. Eng. Dept., Univ. of Maine, Orono, ME, USA
  • fYear
    2013
  • fDate
    4-5 April 2013
  • Firstpage
    407
  • Lastpage
    413
  • Abstract
    In this paper a new technique using artificial neural networks for power system state estimation is presented. This method does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on 6-bus and 18-bus power systems and the results are provided.
  • Keywords
    neural nets; observability; power system state estimation; 18 bus power systems; 6 bus power systems; artificial neural networks; measurement variables; network observability analysis; power system state estimation; Artificial neural networks; Power measurement; Reactive power; State estimation; Testing; Vectors; Artificial neural networks; network observability; power systems; state estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Green Technologies Conference, 2013 IEEE
  • Conference_Location
    Denver, CO
  • ISSN
    2166-546X
  • Print_ISBN
    978-1-4673-5191-1
  • Type

    conf

  • DOI
    10.1109/GreenTech.2013.69
  • Filename
    6520082
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=601480