Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Torque ripple reduction for switched reluctance motors using global optimization algorithm

  • Ben, Tong (College of Electrical Engineering and New Energy, China Three Gorges University) ;
  • Nie, Heng (College of Electrical Engineering and New Energy, China Three Gorges University) ;
  • Chen, Long (College of Electrical Engineering and New Energy, China Three Gorges University) ;
  • Jing, Libing (College of Electrical Engineering and New Energy, China Three Gorges University) ;
  • Yan, Rongge (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology)
  • Received : 2022.04.05
  • Accepted : 2022.07.14
  • Published : 2022.11.20
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Abstract

Global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-of angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance-current-position and torque-current-position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-of angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant No. 52007102, and State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology (No. EERIKF2021015).

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