• DocumentCode
    647886
  • Title

    Real time parameter identification of composite load model

  • Author

    Najafabadi, Amin M. ; Alouani, Ali T.

  • Author_Institution
    Center for Energy Syst. & Res., Tennessee Tech Univ., Cookeville, TN, USA
  • fYear
    2013
  • fDate
    21-25 July 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Real time accurate representation of electric load model is of great importance in power system simulation, control and stability studies. Composite load model made up of an induction motor in parallel with static load has been widely accepted as an appropriate structure for describing load behaviors, especially for the studies that deal with power system stability. Measurement-based composite load model is reviewed in this paper and a real time observer is proposed to estimate the load model parameters using the concept of multiple model estimation algorithm. Simulation results are provided for a composite load including IEEE type 6 motor to show the effectiveness of the proposed real time estimator. This paper is a step in the right direction toward developing accurate real time techniques for stability analysis.
  • Keywords
    Kalman filters; electric field measurement; induction motors; load (electric); parameter estimation; power system control; power system simulation; power system stability; composite load model; electric load model; induction motor; multiple model estimation algorithm; power system control; power system simulation; power system stability; real time parameter identification; stability analysis; static load; Estimation; Induction motors; Load modeling; Power system dynamics; Power system stability; Stability analysis; Vectors; Composite Load Model; Kalman filter; field measurements; parameter estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting (PES), 2013 IEEE
  • Conference_Location
    Vancouver, BC
  • ISSN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESMG.2013.6672435
  • Filename
    6672435