多目标算法分层优化策略在开关磁阻电机中的应用

针对电机多参数多目标协同优化较为复杂的问题，提出了基于非支配排序遗传算法分层迭代优化的思想。首先，介绍定子分段混合励磁开关磁阻电机的设计流程和工作原理。其次，选择电机的待优化参数和优化目标，并引入Pearson相关系数分析电机参数与优化目标的相关性，根据相关性结果对待优化参数进行分层；建立各层优化参数与优化目标的非线性模型，将非线性目标模型引入多目标优化算法。最终，在Pareto前沿中选取最优个体，完成对电机结构参数和控制参数的分层迭代优化，确定电机的最优结构参数和控制参数，并通过有限元分析软件进行验证。相比较于初始模型，优化后电机的效率略有提高，平均转矩增加12.44%,转矩脉动减小64.96%。根据最优参数制造出实验样机，实验结果验证了优化设计的有效性和优越性。

Application of multi-objective algorithm layered optimization strategy in switched reluctance motor

Aiming at the complicated problem of multi-parameter and mult-objective cooperative optimization of motor, a layered iterative optimization method based on non dominated sorting genetic algorithm is proposed. Firstly, the design flow and working principle of stator segment mixed excitation switched reluctance motor are introduced. Secondly, the parameters to be optimized and the optimization target of the motor are selected. After Pearson correlation coefficient is introduced to analyze the correlation between the motor parameters and the optimization target, the optimization parameters are stratified according to the correlation results. The nonlinear model of each layer optimization parameter and optimization objective is established, and the nonlinear objective model is introduced into the multi-objective optimization algorithm. Finally, the optimal individual is selected in Pareto front, the hierarchical iterative optimization of motor structure parameters and control parameters is completed, the optimal structure parameters and control parameters of the motor are determined, and the finite element analysis software is used to verify. Compared with the initial model, the efficiency of the optimized motor is slightly improved, the average torque is increased by 12.44% and the torque ripple is reduced by 64.96%. The experimental prototype is manufactured according to the optimal parameters, and the experimental results verify the effectiveness and superiority of the optimal design.