• DocumentCode
    2294527
  • Title

    Application of artificial intelligence technologies for monitoring large power interconnections

  • Author

    Kurbatsky, V.G. ; Tomin, N.V.

  • Author_Institution
    Dept. of Electr. Power Syst., Energy Syst. Inst., Irkutsk, Russia
  • Volume
    3
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    1360
  • Lastpage
    1365
  • Abstract
    Sophisticated operation conditions for large interconnected power systems (IPSs) need a powerful instrument to study dynamic characteristics of electric power systems (EPSs) in real time for different system states. It is a system of operating condition monitoring that enhances control efficiency of normal and emergency conditions in the current market environment. Effective organization of the system of IPS operation monitoring is possible only by a extensive involvement of new tools for the analysis and calculations of operating conditions, and first of all technologies of artificial intelligence. The paper presents an approach to the super short-term forecasting of state variables on the basis of neural network technologies and algorithms of nonlinear optimization that is realized in the ANAPRO software.
  • Keywords
    artificial intelligence; neural nets; optimisation; power engineering computing; power system interconnection; power system management; ANAPRO software; IPS operation monitoring; artificial intelligence technology; control efficiency enhancement; electric power systems; large interconnected power systems; large power interconnection monitoring; neural network technologies; nonlinear optimization; operating condition monitoring; state variables forecasting; Artificial neural networks; Forecasting; Monitoring; Power system dynamics; Predictive models; artificial intelligence methods; forecasting; monitoring; state variables;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5583579
  • Filename
    5583579