基于模拟退火粒子群算法的永磁同步电机参数辨识方法  

Parameter Identification Method of Permanent Magnet Synchronous Motor Based on Simulated Annealing Particle Swarm Algorithm

作　　者：张聪[1] 马国梁[1] ZHANG Cong;MA Guoliang(School of Energy and Power Engineering,Nanjing University of Science and Technology,Nanjing 210018,China)

机构地区：[1]南京理工大学能源与动力工程学院,江苏南京210018

出　　处：《微电机》2024年第11期18-23,共6页Micromotors

摘　　要：针对常规粒子群算法在参数辨识中精度和效率较低的问题,提出一种自适应模拟退火粒子群优化算法,用于辨识永磁同步电机的参数。在常规粒子群优化算法中引入模拟退火算法中的Metropolis准则,构造了每个粒子的位置矢量退火算法适应度函数,以扩大搜索范围,避免粒子陷入局部最优。仿真结果表明,相比于常规粒子群算法,模拟退火粒子群算法具有更高的辨识效率和辨识精度。To address the problem of low accuracy and efficiency of conventional particle swarm algorithms in parameter identification,an adaptive simulated annealing particle swarm optimization algorithm was proposed to identify the parameters of permanent magnet synchronous motors.By introducing the Metropolis criterion from the simulated annealing algorithm into the conventional particle swarm optimization algorithm,a fitness function for each particle’s position vector annealing algorithm was constructed to expand the search range and avoid particles from falling into local optima.Simulation results demonstrate that compared with the conventional particle swarm optimization algorithm,the simulated annealing particle swarm optimization algorithm has higher identification efficiency and accuracy.

关 键 词：模拟退火 粒子群优化 METROPOLIS准则 参数辨识 永磁同步电机 

分 类 号：TM351[电气工程—电机] TM341[自动化与计算机技术—控制理论与控制工程] TP18[自动化与计算机技术—控制科学与工程]