Multi-objective Cooperative Optimization Study of Permanent Magnet Synchronous Motors Based on the Fruit Fly Algorithm
To improve the performance of Permanent Magnet Synchronous Motors(PMSM),this paper used a 72-slot 60-pole PMSM as an example.It addressed the issue of extended computational time and low opti-mization efficiency during multi-objective optimization by proposing a multi-objective optimization method based on the Fruit Fly Optimization Algorithm(FOA).The optimization variables selected are the dimen-sions of the magnetic steel,the optimization objectives include the motor's average torque,torque fluctuation,and slot torque.These objectives were incorporated into a multi-objective optimization function with weigh-ting coefficients.The approach begined by obtaining the sample space of each variable through finite element simulations.Subsequently,a Generalized Regression Neural Network(GRNN)was employed to fit and train the simulation dataset,creating nonlinear models.Finally,the Fruit Fly Optimization Algorithm(FOA)was applied for optimization.The finite element simulation analysis shows that the FOA algorithm effectively re-duces torque fluctuation and increases the average torque.It also offers advantages such as minimal parame-ter configuration,fast convergence,and resistance to getting trapped in local optima.This method holds sig-nificant practical value.
万方数据
维普
