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混合辅助同步磁阻电机转子拓扑分析与优化

Rotor Topology Analysis and Optimization of Hybrid Auxiliary Synchronous Reluctance Motor
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摘要 针对内置永磁同步电机稀土永磁体用量较多以及铁氧体辅助同步磁阻电机易发生不可逆退磁的问题,提出采用铁氧体和稀土混合的方式,将铁氧体退磁严重的区域换成稀土永磁体,建立混合磁体辅助同步磁阻电机。考虑到该电机转矩脉动较大,优化参数较多,采用基于遗传算法和田口法相结合的多目标优化分析方法,以降低转矩脉动和提高平均转矩为优化目标,对电机转子结构参数进行优化。相比传统遗传算法,该多目标优化方法不仅提升了平均转矩、降低了转矩脉动和稀土用量,而且减少了计算量。利用样机进行负载转矩实验,实验实测值与有限元仿真值误差在5%以内,证明了有限元仿真分析结果的正确性。 Aiming at the problem of irreversible demagnetization of ferrite-assisted synchronous reluctance motor due to the large amount of rare earth permanent magnet used in built-in permanent magnet synchronous motor,the hybrid magnet of ferrite and rare earth was proposed to replace the seriously demagnetized area of ferrite with rare earth permanent magnet and a mixed magnetic-assisted synchronous reluctance motor was built.Considering the large torque ripple of the motor and the large number of optimization parameters,a multi-objective optimization analysis method based on the combination of genetic algorithm and Taguchi method was adopted to reduce the torque ripple and increase the average torque as optimization objectives,taking the rotor structure parameters of the motor as optimization variables.Compared with the traditional genetic algorithm,the multi-objective optimization method not only improved the average torque,reduced the torque ripple and rare earth consumption.but also reduced the computation.The load torque experiment was carried out with the prototype,and the error between the experimental measured value and the finite element simulation value was less than 5%,which proved the correctness of the finite element simulation and the analysis results.
作者 王鑫颖 佟文明 WANG Xinying;TONG Wenming(National Rare Earth Permanent Magnet Motor Engineering Technology Research Center,Shenyang University of Technology,Shenyang 110870,China)
出处 《微特电机》 2024年第10期6-10,共5页 Small & Special Electrical Machines
关键词 永磁辅助同步磁阻电机 混合磁体 遗传算法 多目标优化 permanent magnet auxiliary synchronous reluctance motor(PMaSynRM) hybrid magnet genetic algorithm multi-objective optimization
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