改进型人工水母搜索算法的永磁同步电机在线参数辨识  

Online Parameter Identification Based on Improved Artificial Jellyfish Search Algorithm for Permanent Magnet Synchronous Motor

作　　者：文定都[1] 阳意平 罗朝旭 程谆 WEN Dingdu;YANG Yiping;LUO Zhaoxu;CHENG Zhun(College of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou 412007,China;Hunan Railway Professional Technology College,Zhuzhou 412001,China)

机构地区：[1]湖南工业大学电气与信息工程学院,湖南株洲412007 [2]湖南铁道职业技术学院,湖南株洲412001

出　　处：《湖南电力》2024年第6期38-45,共8页Hunan Electric Power

基　　金：国家自然科学基金(52207205)。

摘　　要：针对永磁同步电机人工水母搜索算法参数辨识精度低、同时辨识多参数速度慢、易陷入局部最优等缺点,提出一种改进型人工水母搜索算法。首先设计Tent混沌映射和反向学习策略,增强了水母群靠近最优位置的能力。其次,设计平衡算法非线性递减时间控制函数的洋流运动与水母群内部运动。最后,为了克服人工水母搜索算法易陷入局部最优导致精度下降的问题,设计高斯变异帮助水母搜索算法跳出局部最优。实验结果表明,改进型人工水母搜索算法对永磁同步电机参数辨识具有更高的精度、更快的收敛速度,辨识精度能达到99.15%。To address the issues of the artificial jellyfish search(JS)algorithm for permanent magnet synchronous motor(PMSM),such as low parameter identification accuracy,slow multi⁃parameter identification,and to falling into local optimum easily,an improved artificial Jellyfish search algorithm is proposed.Firstly,Tent map and opposition⁃based learning strategy are designed to enhance the ability of jellyfish groups to approach optimal positions.Secondly,The jellyfish swarm motion and the ocean current motion of nonlinear decreasing time control function and bance algorithm are designed.Finally,to overcome the problem that the JS algorithm is prone to fall into local optimum leading accuracy degradation,Gaussian mutation is designed to help JS algorithm jump out of local optimum.The experimental results show the proposed algorithm has higher accuracy and faster convergence speed for PMSM parameter identification,and the identification accuracy can reach 99.15%.

关 键 词：永磁同步电机 参数辨识 人工水母搜索算法 反向学习策略 高斯变异 

分 类 号：TM341[电气工程—电机]