基于粒子群算法的内置式永磁同步发电机多目标优化

Multi-objective Optimization of Interior Permanent Magnet Synchronous Generator Based on Particle Swarm Optimization

为了提高内置式永磁同步发电机(IPMSG)的效率并降低齿槽转矩,使发电机性能更加优越,介绍了一种在响应面方法(RSM)的基础上,融合了多目标粒子群优化算法(MOPSO)的优化设计策略。建立发电机计算模型,确立优化双目标,通过灵敏度分析将极弧系数、定子槽口宽度和槽底宽度三个变量作为优化参数。运用Box-Behnken设计(BBD)获得17组样本数据。根据RSM获得效率和齿槽转矩的回归方程,并且根据方差分析检验优化模型的可靠性。将提高效率和降低齿槽转矩为目标,借助RSM和MOPSO相结合进行寻优,使参数组合最优化。仿真结果表明,优化后发电机性能得到明显提升,验证了该优化方法的可行性。

To improve the efficiency of interior permanent magnet synchronous generator(IPMSG)and reduce the cogging torque,an optimal design strategy based on the response surface method(RSM)and multi-objective particle swarm optimization(MOPSO)was introduced.The generator calculation model was established and the optimization double objective was established.Three variables,namely polar arc coefficient,stator notch width and the bottom of the stator groove were taken as optimization parameters through sensitivity analysis.Box-Behnken design(BBD)was used to design and obtain 17 groups of sample data.Regression equations of efficiency and cogging torque were obtained according to RSM,and reliability of the optimization model was tested according to variance analysis.Aiming at improving efficiency and reducing cogging torque,the combination of RSM and MOPSO was used to optimize the parameter combination.The simulation results showed that the performance of the generator was obviously improved,which verifies the feasibility of the optimization method.