基于UPF的PMSM转子磁链辨识方法
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摘要
转子磁链的准确辨识是实现永磁同步电机(PMSM)永磁体工作状态监控,确保其驱动系统可靠运行并实现高性能控制的有效方法。综述了现有的转子磁链辨识方法及其技术特点,在基于冻结磁导率方法获取d,q轴实时电感及电机运行温度修正定子电阻的基础上,提出了基于无迹粒子滤波(UPF)的PMSM转子磁链辨识方法,并进行了仿真研究与实验验证,研究结果表明,在高斯与非高斯测量噪声及稳态与动态系统运行工况约束下,所提方法能够在考虑PMSM驱动系统d,q轴电感及定子电阻变化的基础上实现转子磁链的准确辨识,且辨识方差较小。
Accurate identification of rotor flux linkage is an effective method to monitor the permanent magnet working condition of permanent magnet synchronous motor(PMSM),which guarantee the reliable running and realize high performance control of its drive system. The existing principal methods and their technical features for rotor flux linkage identification were first summarized,then,the real-time d,q axis inductance and correctional stator resistance were obtained based on frozen permeability method and PMSM operating temperature respectively,the rotor flux linage identification method of PMSM based on unscented particle filter(UPF)was proposed,and the simulation research and experiment validation of the proposed method were also implemented. The research results show that under some constraints of Gaussian and non-Gaussian noise environment,steady state and dynamic state operation condition of PMSM drive system,the proposed method always can realize accurate identification of rotor flux linkage with the consideration of d,q axis inductance and stator resistance variation of PMSM drive system,and the identification variance is relatively smaller.
Accurate identification of rotor flux linkage is an effective method to monitor the permanent magnet working condition of permanent magnet synchronous motor(PMSM),which guarantee the reliable running and realize high performance control of its drive system. The existing principal methods and their technical features for rotor flux linkage identification were first summarized,then,the real-time d,q axis inductance and correctional stator resistance were obtained based on frozen permeability method and PMSM operating temperature respectively,the rotor flux linage identification method of PMSM based on unscented particle filter(UPF)was proposed,and the simulation research and experiment validation of the proposed method were also implemented. The research results show that under some constraints of Gaussian and non-Gaussian noise environment,steady state and dynamic state operation condition of PMSM drive system,the proposed method always can realize accurate identification of rotor flux linkage with the consideration of d,q axis inductance and stator resistance variation of PMSM drive system,and the identification variance is relatively smaller.
引文
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[2]周荔丹,陆凯捷,姚钢,等.多相永磁同步电机控制及风电应用综述[J].电气传动,2016,46(1):7-15.
[3]Henwood N,Malaize J,Praly L.A Robust Nonlinear Luenberger Observer for the Sensorless Control of SM-PMSM:Rotor Position and Magnets Flux Estimation[C]//38thAnnual Conference on IEEE Industrial Electronics Society.Canada,Montreal,2012:1625-1630.
[4]Moon S,Lee J,Jeong H-Y,et al.Demagnetization Fault Diagnosis of a PMSM Based on Structure Analysis of Motor Inductance[J].IEEE Transactions on Industrial Electronics,2016,63(6):3795-3803.
[5]Lee D M.On-line Parameter Identification of SPM Motors Based on MRAS Technique[J].International Journal of Electronics,2017(4):593-607.
[6]肖曦,许青松,王雅婷.基于遗传算法的内埋式永磁同步电机参数辨识方法[J].电工技术学报,2014,29(3):21-26.
[7]Lin G H,Zhang J,Liu Z H.Parameter Identification of PMSMUsingImmuneClonalSelectionDifferentialEvolutionAlgorithm[J].Mathematical Problems in Engineering,2014(2):1-10.
[8]Wu L H,Liu Z H,Wei H L.Parameter Identification of Permanent Magnet Synchronous Machine Based on Adaptive Mutation Dynamic Differential Evolution[J].Journal of Dynamic Systems Measurement and Control Transaction for the ASME,2017(6):1-10.
[9]Liu Z H,Wei H L,Zhong Q C.Parameter Estimation for VSI-fed PMSM Based on Dynamic PSO with Learning Strategies[J].IEEE Transactions on Power Electronics,2017,32(4):3154-3165.
[10]肖曦,张猛,李永东.永磁同步电机永磁体状况在线监测[J].中国电机工程学报,2007,27(24):43-47.
[11]陈涛.电动汽车PMSM退磁故障诊断及容错控制[D].合肥:合肥工业大学,2017:47-50.
[12]文传博,齐亮.永磁同步电机磁链信息在线监测的新方法[J].电力系统及其自动化学报,2010,22(2):22-26.
[13]占荣辉,张军.非线性滤波理论与目标跟踪应用[M].北京:国防工业出版社,2013:39-40.
[14]Crisan D,Doucet A.A Survey of Convergence Results on Pariticle Filtering Methods for Practitioners[J].IEEE Transactions on Computing,2000,10(3):197-208.
[15]Van Der Merwe R,Doucet A,De Feritas N.The Unscented Particle Filter[R].Cambridge:Department of Engineering,Cambridge University,2000.
[16]Julier S J,Uhlmann J K,Durrant-Whyte H F.New Approach for Filtering Nonlinear Systems[C]//Proceedings of the American Control Conference.Washington,1995:1628-1632.