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

刘朝华; 周少武; 刘侃; 章兢

doi:10.3724/SP.J.1004.2013.02121

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

doi: 10.3724/SP.J.1004.2013.02121

1.
湖南科技大学信息与电气工程学院湘潭 411201 中国;
2.
谢菲尔德大学电子与电气工程系谢菲尔德 S13JD 英国;
3.
湖南大学电气与信息工程学院长沙 410082 中国

基金项目:

国家科技支撑计划（2012BAH09B02），国家自然科学基金（61174140，61203309），教育部高校博士点基金（20110161110035），中国博士后科学基金项目（2013M540628），湖南省自然科学基金（13JJ8014，14JJ3107）资助

详细信息

作者简介:
周少武湖南科技大学信息与电气工程学院教授. 主要研究方向为机器人控制技术. E-mail：shaowuzhou@163.com

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出版历程
- 收稿日期: 2012-09-26
- 修回日期: 2013-04-15
- 刊出日期: 2013-12-20

Permanent Magnet Synchronous Motor Multiple Parameter Identification and Temperature Monitoring Based on Binary-modal Adaptive Wavelet Particle Swarm Optimization

1.
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China;
2.
Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S13JD, UK;
3.
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Funds:

Supported by Key Projects in the National Science and Technology Pillar Program (2012BAH09B02), National Natural Science Foundation of China (61174140, 61203309), Doctoral Fund of Ministry of Education of China (20110161110035), China Postdoctoral Science Foundation Funded Project (2013M540628), and National Natural Science Foundation of Hunan Province (13JJ8014, 14JJ3107)

摘要

摘要: 提出了一种双模态自适应小波粒子群（Binary-modal adaptive wavelet particle swarm optimization，BAWPSO）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自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.
- 粒子群 /
- 永磁同步电机 /
- 参数辨识 /
- 温度监测 /
- 自适应
Abstract: 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 track the varied parameter. Once the stator winding resistance is identified, the temperature can be calculated according to the principle that the metal resistance linearly depends on its temperature. The proposed method can realize on-line monitoring of the permanent magnet synchronous motor temperature effectively.
- Particle swarm optimization (PSO) /
- permanent magnet synchronous motor (PMSM) /
- parameter identification /
- temperature monitoring /
- adaptive

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