标准粒子群算法在永磁同步电机参数辨识中的应用研究  

Research and Application on Multi-Parameters Identification of PMSM Based on Standard Particle Swarm Optimization Algorithm

作　　者：沈蛟骁 余海涛[1] 王亚鲁[1] 徐鸣飞[1] 陈辉[1] 

机构地区：[1]东南大学,南京210096

出　　处：《微电机》2015年第12期32-35,共4页Micromotors

基　　金：中央高校基本科研业务费专项;江苏省普通高校研究生科研创新计划(SJLX_0062)

摘　　要：高性能的伺服驱动器参数整定需要参考永磁同步电机的相关参数。针对表贴式永磁同步电机的多参数辨识,设计了一种基于标准粒子群优化算法的辨识方法。在基本粒子群优化算法的基础上,引入了随时间线性递减权值的策略。仿真结果表明该辨识方法能够准确辨识出电机的多个参数,辨识速度快,稳定性好,精度较高,具有良好的实用性。The parameters of Permanent Magnet Synchromous Motor （PMSM） is needed in the parameter adjustment of servo drives with high performance. Based on the standard particle swarm optimization algorithm, a multi-parameter identification method was proposed. Based on the basic particle swarm optimization, a theory of weight decreased with time was introduced. The simulation illustrated that this method could accurately identify the multiple parameters of the machine and has good stability, validity and efficiency.

关 键 词：永磁同步电机 参数辨识 标准粒子群优化 线性权重 

分 类 号：TM351[电气工程—电机] TM341