• DocumentCode
    602171
  • Title

    Induction machine parameter identification: A comparison between GAs and PSO approaches

  • Author

    Yousfi, Laatra ; Bouchemha, Amel ; Bechouat, Mohcene ; Boukrouche, A.

  • Author_Institution
    Lab. Inverses Problems: Modeling, Inf. & Syst. (PI:MIS), Univ. of Tebessa, Tebessa, Algeria
  • fYear
    2013
  • fDate
    27-30 March 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This paper, deals with meta-heuristics methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) for parameters identification of induction machine. The considered method consists on minimizing quadratic criterion that represents the difference between measured rotor mechanical speed and those computed from the simulated model. The obtained results by simulation show that the method based on particle swarm optimization is more efficient than genetic algorithms in terms of convergence speed and gives optimal solution.
  • Keywords
    asynchronous machines; genetic algorithms; particle swarm optimisation; GA approach; PSO approach; genetic algorithms; induction machine parameter identification:; particle swarm optimization; quadratic criterion minimization; rotor mechanical speed; Genetic algorithms; Induction machines; Mathematical model; Parameter estimation; Particle swarm optimization; Rotors; Stators; Comparison; Experimental Validation; Genetic Algorithms; Identification; Induction Machine; Particle Swarm Optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Ecological Vehicles and Renewable Energies (EVER), 2013 8th International Conference and Exhibition on
  • Conference_Location
    Monte Carlo
  • Print_ISBN
    978-1-4673-5269-7
  • Type

    conf

  • DOI
    10.1109/EVER.2013.6521561
  • Filename
    6521561