• DocumentCode
    2382398
  • Title

    Electromechanical mode on-line estimation using regularized robust RLS methods

  • Author

    Zhou, Ning ; Trudnowski, Dan ; Pierre, John ; Mittelstadt, William

  • fYear
    2010
  • fDate
    25-29 July 2010
  • Firstpage
    1
  • Lastpage
    1
  • Abstract
    Summary form only given. This paper proposes a regularized robust recursive least squares (R3LS) method for on-line estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data. fn A robust objective function is utilized to reduce the negative influence from non-typical data, which include outliers and missing data. A dynamic regularization method is introduced to help include a priori knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte-Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and non-typical measurement data.
  • Keywords
    autoregressive moving average processes; electric variables measurement; least mean squares methods; power system measurement; power system state estimation; recursion method; 17-machine simulation model; ARMAX model; Monte-Carlo method; a priori knowledge; autoregressive moving average exogenous model; dynamic regularization method; electromechanical mode online estimation; low-level pseudo-random probing; power-system electromechanical modes; regularized robust RLS methods; regularized robust recursive least squares method; ringdown data; robust objective function; synchronized phasor measurement unit data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting, 2010 IEEE
  • Conference_Location
    Minneapolis, MN
  • ISSN
    1944-9925
  • Print_ISBN
    978-1-4244-6549-1
  • Electronic_ISBN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PES.2010.5589746
  • Filename
    5589746