• DocumentCode
    2038898
  • Title

    Application of artificial intelligence methods for definition of electric power losses

  • Author

    Mogilenko, A.V.

  • Author_Institution
    Open Joint Stock Co., Novosibirsk
  • fYear
    2005
  • fDate
    Sept. 2005
  • Firstpage
    595
  • Lastpage
    599
  • Abstract
    This paper presents the comparative study for fuzzy regression model using linear programming, fuzzy regression model using genetic algorithms and standard regression model. The fuzzy and standard models were developed for estimation of electric power losses in electrical networks. Simulation was carried out with a tool developed in MATLAB
  • Keywords
    artificial intelligence; fuzzy set theory; genetic algorithms; linear programming; losses; power system analysis computing; regression analysis; MATLAB; artificial intelligence methods; electric power losses; electrical networks; fuzzy regression model; genetic algorithms; linear programming; losses estimation; standard regression model; Artificial intelligence; Electric variables measurement; Fuzzy sets; Information analysis; Loss measurement; Mathematical model; Power measurement; Power system measurements; Power system modeling; Regression analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Telecommunications Conference, 2005. INTELEC '05. Twenty-Seventh International
  • Conference_Location
    Berlin
  • Print_ISBN
    978-3-8007-2905-0
  • Type

    conf

  • DOI
    10.1109/INTLEC.2005.335165
  • Filename
    4134404