摘要
针对采用自然冷却的大功率永磁同步电机设计问题,提出了提高功率密度的设计方法,通过ANSYS Maxwell与Matlab联合仿真实现了针对永磁同步电机基于NSGA-Ⅲ算法的参数优化。为验证算法对于超多目标优化的有效性与先进性,同时采用了GA遗传算法与NSGA-II非支配遗传算法对电机进行了超多目标优化,在综合考虑电机的输出能力、转矩脉动与整机重量的情况下,特别针对电机的铁耗与铜耗进行优化,尽可能地降低电机运行时产生的热量,延长电机安全运行时长。对这3种方法优化后的电机进行了性能分析与比较,结果表明,使用NSGA-Ⅲ算法优化得到的电机工作效率相比于优化前的电机上升了0.64%,降低了工作时产生的发热量,延长了电机的工作时长,证明了该优化方法的可行性。
Aiming at the design problem of naturally cooled permanent magnet synchronous motors,a method to improve power density is proposed.By combining simulations between ANSYS Maxwell and Matlab,the parametric optimization for permanent magnet synchronous motor utilizing the NSGA-Ⅲalgorithm is achieved.To affirm the algorithm’s efficacy and superiority in handling ultra multi-objective optimization,both the GA genetic algorithm and the NSGA-II non dominated genetic algorithm were employed to conduct ultra multi-objective optimization of the motor.Optimizations particularly targeted the iron and copper losses of the motor with an aim to minimize generated heat during operation,thereby prolonging the motor′s safe operational duration.A comprehensive consideration was given to the motor′s output capability,torque ripple,and overall weight.Performance analysis and comparisons were made for motors optimized by these three methods.The results show that the motor optimized by NSGA-Ⅲalgorithm has higher efficiency,which validats the optimization approach′s viability.
作者
吴宇伦
熊新红
葛荣泰
冯伟
WU Yulun;XIONG Xinhong;GE Rongtai;FENG Wei(School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430070,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China)
关键词
NSGA-Ⅲ算法
自然冷却
永磁同步电机
高功率密度
超多目标优化
NSGA-ⅢAlgorithm
naturally cooling
permanent magnet synchronous motor
high power density
ultra multi-objective optimization
作者简介
吴宇伦(1997-),男,湖北武汉人,武汉理工大学交通与物流工程学院硕士研究生.
