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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines
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Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines

机译：基于惩罚方法的表面永磁同步机的启发式优化

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摘要

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.
机译：本文旨在提供智能设计，以提高表面永磁同步电动机的效率。涉及惩罚方法的有效设计策略被认为是提取所有可能的参数组合和在不可行区域中的解决方案。我们比较树启发式优化算法的性能和六种惩罚方法。启发式优化算法是：粒子群优化，差分搜索算法和树种子算法。罚款方法是：其中三种是静态惩罚方法，两个动态惩罚方法和Deb的规则。此外，通过有限元分析测试优化的电机设计。从实验中抽出了两个结论。首先，与标准和流行的启发式算法相比，使用惩罚方法的启发式算法显着更好。这强调了在SPMSM设计优化中使用具有惩罚方法的启发式算法的重要性。其次，对于表面永磁同步电动机设计，设计优化和数值分析结果的兼容性是可接受的，非常令人满意的。根据分析设计，通过所提出的方法实现了4％的效率提高。

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