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首页> 外文期刊>Journal of Electrical Engineering >Optimization of surface-mounted permanent magnet brushless AC motor using analytical model and differential evolution algorithm
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Optimization of surface-mounted permanent magnet brushless AC motor using analytical model and differential evolution algorithm

机译：基于解析模型和差分进化算法的表面贴装式永磁无刷交流电动机优化

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This paper discusses the optimization of surface-mounted permanent magnet brushless AC (PMBLAC) motor using Analytical Sub-domain model with Differential Evolution Algorithm (ASDEA). Only two regions were considered in this analytical sub-domain model, ie magnet and airgap regions, with assistance of Complex Relative Permeance Function (CRPF) to account for the stator slotting effect. Five machine parameters were chosen to be optimized, namely the magnet arc-pole-pitch ratio, slot opening width, magnet thickness, airgap length and stator inner radius. The optimization process has four objectives, ie minimum torque ripple, low cogging torque, high efficiency, and high output torque. The results from the optimized ASDEA were compared with the Analytical Sub-domain Genetic Algorithm (ASGA) and further validated against 2-D finite element model (FEM). Results show a good agreement between analytically optimized models and finite element model. The ASDEA has faster computational time compared to ASGA, and this provides benefit in terms of reducing the machine design parameterization time and less redundancy work required to achieve motor design specifications.
机译：本文讨论了使用差分演化算法（ASDEA）的解析子域模型对表面贴装式永磁无刷交流（PMBLAC）电机进行的优化。在此分析子域模型中，仅考虑两个区域，即磁体和气隙区域，并借助复数相对磁导函数（CRPF）来考虑定子的开槽效应。选择了五个要优化的机器参数，即磁铁的弧-极-螺距比，槽的开口宽度，磁铁的厚度，气隙长度和定子的内半径。优化过程具有四个目标，即最小转矩波动，低齿槽转矩，高效率和高输出转矩。将优化的ASDEA的结果与分析子域遗传算法（ASGA）进行比较，并针对二维有限元模型（FEM）进行了进一步验证。结果表明，分析优化模型与有限元模型之间具有很好的一致性。与ASGA相比，ASDEA的计算时间更快，这在减少机器设计参数化时间和减少达到电机设计规格所需的冗余工作方面具有优势。

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