基于双模态自适应小波粒子群的永磁同步电机多参数识别与温度监测方法

提出了一种双模态自适应小波粒子群（Binary-modal adaptive wavelet particle swarm optimization，BAWPS01的永磁同步电机（Permanent magnet synchronous motor，PMSM）多参数识别与温度监测方法．为了提高算法动态寻优性能，群体被划分为正向学习和反向学习两种模态；对处于不同模态的粒子分别采用正向学习策略与反向学习策略协同求解，扩大了解的搜索空间；同时对粒子个体极值采用白适应小波算子增强学习以提高收敛精度．永磁同步电机参数辨识结果表明所提方法能够有效地辨识电机电阻，dq轴电感与转子磁链等参数，且能有效追踪系统...

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Bibliographic Details
Published in自动化学报 Vol. 39; no. 12; pp. 2121 - 2130
Main Author 刘朝华 周少武 刘侃 章兢
Format Journal Article
LanguageChinese
Published 2013
Subjects
参数辨识
永磁同步电机
温度监测
粒子群
自适应
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ISSN0254-4156
1874-1029

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Summary:提出了一种双模态自适应小波粒子群（Binary-modal adaptive wavelet particle swarm optimization，BAWPS01的永磁同步电机（Permanent magnet synchronous motor，PMSM）多参数识别与温度监测方法．为了提高算法动态寻优性能，群体被划分为正向学习和反向学习两种模态；对处于不同模态的粒子分别采用正向学习策略与反向学习策略协同求解，扩大了解的搜索空间；同时对粒子个体极值采用白适应小波算子增强学习以提高收敛精度．永磁同步电机参数辨识结果表明所提方法能够有效地辨识电机电阻，dq轴电感与转子磁链等参数，且能有效追踪系统参数变化值．在辨识出电机定子绕阻值后，根据金属阻值与温度之间的线性原理间接计算定转子温度，从而实现永磁同步电机系统温度在线监测．
Bibliography:11-2109/TP
Particle swarm optimization （PSO）, permanent magnet synchronous motor （PMSM）, parameter identifica-tion, temperature monitoring, adaptive
LIU Zhao-Hua ZHOU Shao-Wu LIU Kan ZHANG Jing
A novel parameter identification and temperature monitoring approach to permanent magnet synchronous motor （PMSM） based on binary-modal adaptive wavelet particle swarm optimization （BAWPSO） is proposed. In order to enhance the dynamic optimal performance of the swarm, the population is split into two states involving positive learning state and opposition learning state during the search process. The positive learning strategy and the opposition learning strategy are applied to different state swarms respectively to exhibit a wide range exploration. An adaptive wavelet learning mechanism is employed for accelerating the convergence accuracy of pbest. The experimental results show that the proposed method can estimate the machine dq-axis inductances, stator winding resistance and rotor flux linkage effectively, as well as
ISSN:0254-4156
1874-1029