• DocumentCode
    3095367
  • Title

    Fast Online Identification of the Dominant Parameters of Composite Load Model Using Volterra Model and Pattern Classification

  • Author

    Li, Lili ; Xie, Xiaorong ; Yan, Jianfeng ; Han, Yingduo

  • Author_Institution
    Dept. of Electr. Eng., Tsinghua Univ., Beijing
  • fYear
    2007
  • fDate
    24-28 June 2007
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The accuracy of the load parameters has great effects on the validity of power system simulation. Based on PMU/WAMS, considering the requirements of online simulation, prediction and control, this paper systematically proposes an approach for fast online identification of the dominant parameters of composite load. The approach includes four parts, i.e., the dominance analysis of parameters, the transformation from state equation model to volterra model, mapping of the two types of models based on pattern classification and the fast online identification. Our research shows that the proposed approach can reduce the number of parameters to be identified, and can identify the dominant load parameters very quickly with only a few measurements. Simulation results on a provincial system have verified the effectiveness of the proposed approach.
  • Keywords
    Volterra equations; power system identification; power system simulation; power system stability; Volterra model; composite load model; dominant load parameters; fast online identification; pattern classification; power system simulation; transient stability; Control system synthesis; Load modeling; Pattern classification; Phasor measurement units; Power system modeling; Power system security; Power system simulation; Power system stability; Power system transients; Rotors; Identification; K-L transformation; load modeling; pattern classification; transient stability; volterra model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society General Meeting, 2007. IEEE
  • Conference_Location
    Tampa, FL
  • ISSN
    1932-5517
  • Print_ISBN
    1-4244-1296-X
  • Electronic_ISBN
    1932-5517
  • Type

    conf

  • DOI
    10.1109/PES.2007.385736
  • Filename
    4275502