• DocumentCode
    2905863
  • Title

    Optimal design of geometrical and physical parameters of permanent magnet machines in the purpose to reduce its vibratory behavior

  • Author

    Ferkha, N. ; Mekideche, M.R. ; Miraoui, Abdellatif ; Djerdir, A. ; N-Diaye, A.O. ; Peyraut, F.

  • Author_Institution
    Lab. LAMEL, Univ. de Jijel, Ouled Aissa, Algeria
  • fYear
    2013
  • fDate
    2-4 Oct. 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Permanent magnet synchronous machines (PMSM) have high efficiency and torque density, and have already been employed in hybrid electric vehicles. However, one of their disadvantages is the inherent cogging torque, which is a kind of torque ripple and it would be better to minimize. This torque, sometimes, can be an important source of noise and vibrations. In this paper, the effect of the geometric characteristics of the stator on the vibratory behavior of electrical machines is illustrated. The optimum geometry for obtaining a minimum vibration level has been reached. For this purpose, an approach by using the Artificial Intelligent (AI) and the Finite Element Method (FEM) is proposed to solve the magneto-mechanical problem of geometrical parameters identification in the optimization process. The obtained results by Genetic Algorithm (GA) method have been presented.
  • Keywords
    artificial intelligence; finite element analysis; genetic algorithms; hybrid electric vehicles; parameter estimation; permanent magnet machines; power engineering computing; synchronous machines; vibrations; FEM; PMSM; artificial intelligent; cogging torque; electrical machines; finite element method; genetic algorithm method; geometric characteristics; geometrical parameters; hybrid electric vehicles; magneto-mechanical problem; noise source; optimal design; optimization process; permanent magnet synchronous machines; physical parameters; torque density; torque ripple; vibration level; vibrations; vibratory behavior; Equations; Geometry; Optimization; Torque; finite element method; genetic algorithm; magneto-mechanical coupling; neural network; optimal design; permanent magnet synchronous machines; vibration reduction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electric Power and Energy Conversion Systems (EPECS), 2013 3rd International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4799-0687-1
  • Type

    conf

  • DOI
    10.1109/EPECS.2013.6713063
  • Filename
    6713063