[1]林辉,吕帅帅.基于双STF-UKF算法的永磁同步电机参数联合估计[J].东南大学学报(自然科学版),2016,46(1):49-54.[doi:10.3969/j.issn.1001-0505.2016.01.009]
 Lin Hui,Lü Shuaishuai.PMSM parameters estimation based on dual STF-UKF algorithm[J].Journal of Southeast University (Natural Science Edition),2016,46(1):49-54.[doi:10.3969/j.issn.1001-0505.2016.01.009]
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基于双STF-UKF算法的永磁同步电机参数联合估计()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第1期
页码:
49-54
栏目:
电气工程
出版日期:
2016-01-20

文章信息/Info

Title:
PMSM parameters estimation based on dual STF-UKF algorithm
作者:
林辉吕帅帅
西北工业大学自动化学院, 西安 710129
Author(s):
Lin Hui Lü Shuaishuai
School of Automation, Northwest Polytechnical University, Xi’an 710129, China
关键词:
永磁同步电机 参数辨识 强跟踪滤波器 无迹卡尔曼滤波 稳定性
Keywords:
permanent magnet synchronous motor parameters estimation strong tracking filter unscented Kalman filter stability
分类号:
TM351
DOI:
10.3969/j.issn.1001-0505.2016.01.009
摘要:
针对永磁同步电机参数辨识问题,分析了永磁同步电机的可辨识模型. 将参数看成缓慢的变化状态,同时考虑系统噪声和测量噪声,提出了一种基于强跟踪滤波器的无迹卡尔曼滤波算法.该算法能够同时辨识定子电阻、直轴和交轴电感、永磁体磁链,讨论分析了该算法的稳定性.为了减少算法的计算量,将4个参数分成2部分,采用2个STF-UKF滤波器交替运行辨识全部参数.仿真结果表明,该算法在PMSM不同的工况下能够有效地辨识电机的全部参数.
Abstract:
To solve the parameter identification problem of the permanent magnet synchronous motor, the identification model of PMSM(permanent magnet synchronous motor)was analyzed. Regarding the parameters as slow varied states along with time and considering the presence of the system noise and measure noise, an improved unscented Kalman filter(UKF)algorithm was proposed based on the strong tracking filter(STF). This improved filter algorithm is able to estimate the parameters of PMSM including stator resistance, permanent magnet flux linkage and q-axis and d-axis inductance. The stability of the proposed algorithm was discussed. In order to reduce the calculation consumption of the algorithm, the four parameters were divided into two parts and estimated by dual STF-UKF, respectively. Simulation results show that the improved UKF algorithm can estimate the parameters accurately under different PMSM operations.

参考文献/References:

[1] Ha I J, Lee S H. An online identification method for both stator-and rotor resistances of induction motors without rotational transducers[J]. IEEE Transactions on Industrial Electronics, 2000, 47(4):842-853.
[2] Underwood S J, Husain I. Online parameter estimation and adaptive control of permanent-magnet synchronous machines[J]. IEEE Transactions on Industrial Electronics, 2010, 57(7):2435-2443.
[3] Inoue Y, Kawaguchi Y, Morimoto S, et al. Performance improvement of sensorless IPMSM drives in a low-speed region using online parameter identification[J]. IEEE Transactions on Industry Applications, 2011, 47(2): 798-804.
[4] Sim H W, Lee J S, Lee K B. On-line Parameter Estimation of Interior Permanent Magnet Synchronous Motor using an Extended Kalman Filter[J]. Journal of Electrical Engineering & Technology, 2014, 9(2):600-608.
[5] Liu K, Zhang Q, Chen J, et al. Online multiparameter estimation of nonsalient-pole PM synchronous machines with temperature variation tracking[J]. IEEE Transactions on Industrial Electronics, 2011, 58(5): 1776-1788.
[6] 陈振锋, 钟彦儒, 李洁. 感应电机参数辨识三种智能算法的比较[J]. 电机与控制学报, 2010, 14(11):7-12. DOI:10.3969/j.issn.1007-449X.2010.11.002.
  Chen Zhenfeng, Zhong Yanru, Li Jie. Comparison of three intelligent optimization algorithms for parameter identification of induction motors[J]. Electric Machines & Control, 2010, 14(11):7-12. DOI:10.3969/j.issn.1007-449X.2010.11.002.(in Chinese)
[7] 徐占国, 邵诚, 冯冬菊. 基于模型参考自适应的感应电机励磁互感在线辨识新方法[J]. 中国电机工程学报, 2010, 30(3):71-76.
  Xu Zhanguo, Shao Cheng, Feng Dongju. On-line identification of induction motor mutual inductance based on model reference adaptive system[J]. Proceedings of the CSEE, 2010, 30(3):71-76.(in Chinese)
[8] 东子昭. 异步电机参数辨识与自适应控制策略研究[D]. 北京:北方工业大学电气与控制工程学院, 2014.
[9] 陆可. 感应电机状态估计和参数辨识若干新方法研究[D]. 成都:西南交通大学电气工程学院, 2008.
[10] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422.
[11] Jafarzadeh S, Lascu C, Fadali M S. Square root unscented Kalman filters for state estimation of induction motor drives[J]. IEEE Transactions on Industry Applications, 2013, 49(1):92-99.
[12] 周东华, 席裕庚, 张钟俊. 非线性系统带次优渐消因子的扩展卡尔曼滤波[J]. 控制与决策, 1990, 5(5): 1-6.
  Zhou Donghua, Xi Yugeng, Zhang Zhongjun. Suboptimal fading extended Kalman filtering for nonlinear systems[J]. Control & Decision, 1990, 5(5): 1-6.(in Chinese)
[13] Li Z, Pan P, Gao D, et al. An improved unscented kalman filter based on STF for nonlinear systems[C]//2nd International Congress on Image and Signal Processing. Tianjin, China, 2009:1-5.
[14] 陆可, 肖建. 双UKF算法及其在感应电机矢量控制中的应用[J]. 电机与控制学报, 2007, 11(6):564-567,572. DOI:10.3969/j.issn.1007-449X.2007.06.002.
  Lu Ke, Xiao Jian. Dual unscented Kalman filter algorithm and its application to the vector control system of induction motor[J]. Electric Machines & Control, 2007, 11(6):564-567,572. DOI:10.3969/j.issn.1007-449X.2007.06.002.(in Chinese)
[15] Xiong K, Zhang H Y, Chan C W. Performance evaluation of UKF-based nonlinear filtering[J]. Automatica, 2006, 42(2):261-270.
[16] 周东华, 叶银忠. 现代故障诊断与容错控制[M].北京: 清华大学出版社, 2000:68-75.

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备注/Memo

备注/Memo:
收稿日期: 2015-07-02.
作者简介: 林辉(1957—),男,博士,教授,博士生导师,linhui@nwpu.edu.cn.
基金项目: 国家自然科学基金资助项目(51407143)、高等学校博士学科点专项科研基金资助项目(20136102120049)、中央高校基本科研业务费专项资助项目(3102014JCQ01066)、陕西省自然科学基础研究计划资助项目(2014JQ7264, 2015JM5227)、陕西省微特电机及驱动技术重点实验室开放基金资助项目(2013SSJ1002).
引用本文: 林辉,吕帅帅.基于双STF-UKF算法的永磁同步电机参数联合估计[J].东南大学学报(自然科学版),2016,46(1):49-54. DOI:10.3969/j.issn.1001-0505.2016.01.009.
更新日期/Last Update: 2016-01-20