摘要
将铁氧体应用于具有聚磁能力的辐向永磁电机中可获得与稀土永磁电机相媲美的转矩密度,并兼具低成本的优势.为克服传统辐向永磁电机高转矩脉动的弊端,提出一种新型辅助凸极式转子,增强了转子的可塑性并增加优化参数的数目.采用基于响应面分析与多目标骨干粒子群优化算法相结合的多目标优化方法来提升电机的转矩性能.进一步地,考虑到在电机批量生产过程中不可避免地会受到误差、公差等不确定性因素的影响,将六西格玛设计方法和蒙特卡罗分析方法相结合,对电机进行鲁棒性优化.通过对比分析优化前后的电磁性能可知,所提出的新型电机可以在提高输出转矩的同时有效降低转矩脉动,且极大降低电机批量生产中的失效率.最后,实验结果验证了所提新型电机结构及其鲁棒性优化设计的准确性和可行性.
Applying ferrite to spoke-type permanent magnet motors with flux-focusing ability is a low-cost process and can yield torque density comparable to that of rare-earth permanent magnet motors. To overcome the drawback of the high torque ripple associated with traditional spoke-type permanent magnet motors, a new rotor with auxiliary salient poles is proposed. These poles enhance the plasticity of the rotor and increase the number of optimized parameters. The multiobjective optimization method, which combines response surface analysis and multiobjective backbone particle swarm optimization algorithm, is used to improve the torque performance of the motor. The motor will inevitably be affected by uncertainties, such as errors and tolerances, during motor mass production. Therefore, the six sigma design method and Monte Carlo analysis method are combined for optimizing the robustness of the motor. A comparison of the pre-and post-optimization electromagnetic performance revealed that the proposed motor can effectively reduce torque ripple while increasing the output torque and significantly reducing the mass production failure rate of the motor. Finally, experimental results verify the accuracy and feasibility of the proposed new motor structure and its robust optimization design.
作者
陈前
廖继红
赵文祥
刘国海
徐高红
CHEN Qian;LIAO JiHong;ZHAO WenXiang;LIU GuoHai;XU GaoHong(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《中国科学:技术科学》
EI
CSCD
北大核心
2021年第6期659-672,共14页
Scientia Sinica(Technologica)
基金
国家自然科学基金(批准号:51707083,51907080)
江苏省自然科学基金(编号:BK20190848)
中国博士后科学基金(编号:2019M661746)资助项目。
关键词
永磁电机
辅助凸极
多目标优化
鲁棒性设计
permanent magnet motor
auxiliary salient-pole
multi-objective optimization
robust design