摘要
针对PSO算法在永磁同步电机(PMSM)电气参数辨识过程中易陷入局部最优解,辨识结果精度不高,提出了一种改进的模糊粒子群优化算法(IFPSO)。将每个粒子的速度由只受最优粒子影响改为受周围粒子影响,采用Logistic映射搜索提高种群初始化质量,并引入收敛因子,保证IFPSO算法的收敛。建立了适应度函数和模糊权重规则,对比了PSO、FPSO和CPSO算法PMSM参数辨识效果。仿真结果表明,IFPSO算法辨识精度最高,可以为工程应用提供一定的借鉴。
Aiming at the problem of PSO algorithm which is prone to getting stuck in local optima and resulting in low accuracy in electrical parameter identification of Permanent Magnet Synchronous Motor(PMSM),an improved Fuzzy Particle Swarm Optimization(IFPSO)algorithm is proposed.The speed of each particle is changed from being only affected by the optimal particle to being affected by surrounding particles,and the logistic mapping search is used to improve the initialization quality of the population,and a convergence factor is introduced to ensure the convergence of the IFPSO algorithm.The fitness functions and fuzzy weight rules are established,and the PMSM parameter identification performance of PSO,FPSO,and CPSO algorithms are compared.The simulation results show that the IFPSO algorithm has the highest identification accuracy and can provide some reference for engineering applications.
作者
刘洪
李致远
LIU Hong;LI Zhiyuan(Liuzhou Railway Vocational Technical College,Guangxi Liuzhou 545616,China)
出处
《广西电力》
2023年第5期24-28,共5页
Guangxi Electric Power
基金
2022年度广西高校中青年教师科研基础能力提升项目(2022KY1403)。
关键词
永磁同步电机
电气参数辨识
LOGISTIC映射
收敛因子
改进模糊粒子群算法
permanent magnet synchronous motor
electrical parameter identification
Logistic mapping
convergence factor
Improved Fuzzy Particle Swarm Optimization(IFPSO)
