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Optimization of Air Gap Length and Capacitive Auxiliary Winding in Three-Phase Induction Motors Based on a Genetic Algorithm

Author

Listed:
  • Mbika Muteba

    (Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa)

Abstract

There is a necessity to design a three-phase squirrel cage induction motor (SCIM) for high-speed applications with a larger air gap length in order to limit the distortion of air gap flux density, the thermal expansion of stator and rotor teeth, centrifugal forces, and the magnetic pull. To that effect, a larger air gap length lowers the power factor, efficiency, and torque density of a three-phase SCIM. This should inform motor design engineers to take special care during the design process of a three-phase SCIM by selecting an air gap length that will provide optimal performance. This paper presents an approach that would assist with the selection of an optimal air gap length (OAL) and optimal capacitive auxiliary stator winding (OCASW) configuration for a high torque per ampere (TPA) three-phase SCIM. A genetic algorithm (GA) assisted by finite element analysis (FEA) is used in the design process to determine the OAL and OCASW required to obtain a high torque per ampere without compromising the merit of achieving an excellent power factor and high efficiency for a three-phase SCIM. The performance of the optimized three-phase SCIM is compared to unoptimized machines. The results obtained from FEA are validated through experimental measurements. Owing to the penalty functions related to the value of objective and constraint functions introduced in the genetic algorithm model, both the FEA and experimental results provide evidence that an enhanced torque per ampere three-phase SCIM can be realized for a large OAL and OCASW with high efficiency and an excellent power factor in different working conditions.

Suggested Citation

  • Mbika Muteba, 2021. "Optimization of Air Gap Length and Capacitive Auxiliary Winding in Three-Phase Induction Motors Based on a Genetic Algorithm," Energies, MDPI, vol. 14(15), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4407-:d:598778
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    References listed on IDEAS

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    1. Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
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    Cited by:

    1. Tayfun Gundogdu & Zi-Qiang Zhu & Jean-Claude Mipo, 2021. "Design and Analysis of Advanced Nonoverlapping Winding Induction Machines for EV/HEV Applications," Energies, MDPI, vol. 14(20), pages 1-33, October.

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