Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines

Abstract

This paper aims to provide a smart design to improve the efficiency of surface permanent magnet synchronous motor. An efficient design strategy involving penalty approaches are considered for extracting all the possible parameter combinations and the solutions in the infeasible region. We compare the performance of tree heuristic optimization algorithms and six penalty methods. The heuristic optimization algorithms are: particle swarm optimization, differential search algorithm, and tree seed algorithm. The penalty methods are: three of which are static penalty approaches, two of dynamic penalty approaches, and Deb’s rule. Besides, the optimized motor design is tested with finite element analysis. Two conclusions were drawn from the experiments. First, heuristic algorithms using penalty approaches had significantly better performance compared to standard and popular heuristic algorithms. This emphasizes the importance of using heuristic algorithms with penalty approaches in SPMSM design optimization. Second, the compatibility of design optimization and numerical analysis results are acceptable and highly satisfactory for surface permanent magnet synchronous motor design. According to the analytical design, a 4% improvement in efficiency was achieved with the proposed approach.

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Correspondence to Mehmet Çunkaş.

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Mutluer, M., Şahman, M.A. & Çunkaş, M. Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines. Arab J Sci Eng (2020). https://doi.org/10.1007/s13369-020-04689-y

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Keywords

  • Heuristic algorithms
  • Penalty function
  • Finite element analysis
  • SPMSM
  • Constrained optimization