基于改进布谷鸟算法的永磁同步电机参数辨识
Parameter identification of PMSM based on improved cuckoo algorithm
高雄 1郭凯凯1
作者信息
- 1. 安徽理工大学 电气与信息工程学院,安徽 淮南 232001
- 折叠
摘要
针对永磁同步电机中参数辨识精度不足以及速度较慢的问题,文章提出一种改进的布谷鸟算法实现对永磁同步电机的参数辨识.首先,采用Tent映射初始化种群;其次,采用动态发现概率调整布谷鸟蛋被发现的概率;最后,引入逐维反向学习策略,增强了算法的局部和全局寻优能力,同时加快了收敛速度.仿真分析表明,改进的布谷鸟算法相比于原算法,能更加有效地辨识永磁同步电机的电机参数.
Abstract
Aiming at the problems of insufficient accuracy and slow speed of parameter identification in permanent magnet synchronous motors(PMSM),an improved cuckoo algorithm is proposed to realize the parameter identification of PMSM.Firstly,the Tent mapping is used to initialize the population;secondly,the dynamic discovery probability is used to adjust the probability that the cuckoo's egg is discovered;finally,the dimension-by-dimension inverse learning strategy is introduced to enhance the local and global optimization ability of the algorithm,and at the same time,the convergence speed is accelerated.Simulation analysis shows that the improved cuckoo algorithm can identify the motor parameters of the PMSM more effectively compared with the original algorithm.
关键词
永磁同步电机/Tent映射/逐维反向学习/参数辨识/布谷鸟算法Key words
permanent magnet synchronous motor/Tent mapping/dimension-by-dimension inverse learning/parameter identification/cuckoo algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据