Analysis and design of electrical machines with material uncertainties in iron and permanent magnet

• Min Li ^[1] ; Mohammad Hossain Mohammadi ^[1] ; Tanvir Rahman ^[1] ; David Lowther ^[1]
  1. [1] McGill University

     McGill University

     Canadá

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 36, Nº 5 (Special Issue: IGTE 2016), 2017, págs. 1326-1337
• Idioma: inglés
• Enlaces
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• Resumen

  • Purpose – Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.

    Design/methodology/approach – The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.

    Findings – The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.

    Originality/value – In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.