基于改进混沌粒子群优化算法的永磁同步电机参数辨识  

Parameter identification of PMSM based on improved chaos PSO algorithm

作　　者：陈强 蔡琦盼 邓博仁 CHEN Qiang;CAI Qipan;DENG Boren(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《传感器与微系统》2023年第4期157-160,共4页Transducer and Microsystem Technologies

摘　　要：针对永磁同步电机(PMSM)在参数辨识过程中由于粒子容易早熟和陷入局部最优而导致辨识精度不高的问题,提出了一种改进混沌粒子群优化(ICPSO)算法,并将其应用在PMSM多参数辨识中。该算法通过对混沌算法和粒子群优化(PSO)算法结合并优化,且在算法中融入精英免疫原理,处于中间的粒子进行免疫升级,此举不仅扩大了粒子在种群中的搜索范围,而且在一定程度上克服了粒子早熟、不易跳出局部最优的问题。该算法对标准测试函数进行试验,且与PSO算法和混沌粒子群优化(CPSO)算法在参数辨识中的效果相比较,得出定子电阻、dq轴电感和转子磁链电磁参数,从而证明该算法的有效性。An improved chaotic particle swarm optimization(ICPSO)algorithm is proposed,aimming at the problem of low identification precision in parameter identification of permanent magnet synchronous motor(PMSM),and it is applied to multi-parameter identification of PMSM.The algorithm combines chaos algorithm with particle swarm optimization(PSO)algorithm and integrates the principle of elite immunity into the algorithm to upgrade the immune ability of the particle whose fitness is in the middle,this not only enlarges the searching range of the particle in the population,but also overcomes the problem of the particle being precocious and not easy to jump out of the local optimum.The standard test functions are tested,and the stator resistance,dq shaft inductance and rotor flux electromagnetic parameters are obtained by comparing with the results of the PSO and chaotic pso(CPSO)in parameter identification,the validity of the algorithm is proved.

关 键 词：永磁同步电机 参数辨识 混沌粒子群优化 免疫算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TM351[自动化与计算机技术—计算机科学与技术]