• DocumentCode
    2728307
  • Title

    A novel approach for ringdown detection using extended Kalman filter

  • Author

    Yazdanian, Masoud ; Mehrizi-Sani, Ali ; Mojiri, Mohsen

  • Author_Institution
    Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
  • fYear
    2013
  • fDate
    10-13 Nov. 2013
  • Firstpage
    3270
  • Lastpage
    3274
  • Abstract
    Estimation of electromechanical modes has attracted attention during past few decades because the estimation of these modes provides vital information about the stability of the power system. In this paper, a new state-space model is developed for online detection of a ringdown signal using extended Kalman filter (EKF). The proposed model not only can estimate constant parameters, but it can also track time-varying parameters. Simulation results demonstrate the desirable performance of the proposed method for ringdown parameter estimation.
  • Keywords
    Kalman filters; power system stability; power system state estimation; EKF; electromechanical oscillations; extended Kalman filter; ringdown parameter estimation; ringdown signal online detection; time-varying parameters; Damping; Estimation; Frequency estimation; Kalman filters; Mathematical model; Noise; State-space methods; Extended Kalman filter; power system modes identification; ringdown detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE
  • Conference_Location
    Vienna
  • ISSN
    1553-572X
  • Type

    conf

  • DOI
    10.1109/IECON.2013.6699652
  • Filename
    6699652