考虑等效电磁损耗电阻偏移的永磁同步电机直流信号注入在线参数辨识方法

doi:10.19595/j.cnki.1000-6753.tces.230176

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摘要

在实际运行过程中,永磁同步电机的电气参数将受到温度、磁饱和等诸多物理因素的影响而发生偏移,进而影响到效率最优控制、电流解耦控制等优化算法的运行效果。因此,为实现高性能电机控制,永磁同步电机的多电气参数在线辨识显得尤为重要。然而,现有参数辨识方法并未考虑铁磁损耗的影响,更未关注铁磁损耗随电流的偏移情况,所以在辨识精度上仍存在提升空间。对此,本文综合考虑铁磁损耗、铜损,将其集总为电磁损耗,并基于串联电磁损耗电阻模型,提出一种考虑等效电磁损耗电阻偏移的永磁同步电机直流信号注入在线参数辨识方法。该方法首先提出等效电磁损耗电阻随电流偏移的电机模型;再由此设计同时考虑磁饱和、等效电磁损耗电阻偏移的直流信号注入在线参数辨识方法;由于待求解方程组较多,采用最小均方算法进行参数求解;最后,通过实验测试所提方法的准确性,测试结果表明：与正弦信号注入法、传统直流信号注入法相比,所提方法的参数精度有所提升。

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	马铱林
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关键词 ：永磁同步电机, 电磁损耗, 参数辨识, 直流信号注入

Abstract：

Permanent magnet synchronous machines (PMSMs) are widely used for its high operating efficiency. During the application, the parameters of PMSMs may vary a lot when considering the effect of temperature, magnetic saturation and so on. In such a case, the performance of some optimal control strategies, such as the maximum efficiency control and current decoupling control, may decay. So, in order to achieve high performance control, online parameters identification is important. However, there are few parameters identification methods considering the ferromagnetic loss, as well as its variation with currents, while the ferromagnetic loss occupies a great proportion in the PMSMs’ loss. To solve the problem, this paper integrates the ferromagnetic loss and copper loss as electromagnetic loss, and then, proposes an improved DC-signal-injection-based online parameters identification considering the variation of equivalent electromagnetic loss resistance.
Firstly, the magnetic saturation of PMSMs and the variation of electromagnetic loss with current are discussed. Due to the magnetic saturation characteristic of ferromagnetic materials, the magnetic permanence of PMSMs may be decreased as the current rises, and then the inductance and flux linkage may be affected. Besides, when considering the change of current vectors, the copper loss may be variable due to the proximate effect, while the ferromagnetic loss may also vary with the changeable magnetic distribution. To describe such characteristics, an improved series electromagnetic loss resistance model is proposed, which uses apparent flux linkage and incremental inductance to characterize the magnetic saturation, and uses change rates of the equivalent resistance to characterize the variation of electromagnetic loss.
Secondly, in order to realize the full identification of the proposed model’s parameters, 4 groups of d-/q-axis operating currents are chosen based on the criterion of alternating changes of d-/q-axis currents. According to the analysis, once the current increment of each change is not 0, then the steady-state voltage equations of these 4 groups are linearly independent, that is the corresponding coefficient matrix is full-rank and the full parameters identification can be achieved.
Thirdly, a least mean square (LMS) algorithm is adopted to solve the 4 groups of steady-state voltage equations, due to the complexity of the high-rank coefficient matrix’s analytical computation. According to the LMS algorithm, it calculates the gradient of voltage estimation errors’ mean square to each model parameters at first, based on the measured voltages, currents and speeds. Then, the model parameters are updated iteratively based on the corresponding gradients at each step. After a period of iteration, the model parameters which meet the preset voltage estimation accuracy can be obtained.
Finally, a prototype PMSM is used to validate the accuracy of the proposed method. Experimental results show that, when compare with the sinusoidal-signal-injection-based method and the traditional DC-signal-injection-based method, the proposed method is more precise in the identification of electromagnetic resistance and apparent flux linkage, with an average relative error of less than 2.3%. In addition, the proposed method also has high identification accuracy for torque and incremental inductance, with average relative errors less than 0.9% and 3.5%, respectively. However, the identification performance for the change rate of the equivalent resistance is not good enough, and needs further improvement.

Key words： Permanent magnet synchronous machine electromagnetic loss parameters identification DC signal injection

PACS:

TM341

基金资助:

国家自然科学基金（52177062）、中央高校基本科研业务费专项资金（226-2022-00011）资助项目

通讯作者: 杨欢男,1981年生,教授,博士生导师,研究方向为分布式发电与微电网、智能配用电、高效能电机系统等。E-mail：yanghuan@zju.edu.cn

作者简介: 马铱林男,1996年生,博士研究生,研究方向为高效能电机系统、数字化设计、铁磁材料性能测试等。E-mail：mayilin@zju.edu.cn

引用本文:

马铱林, 袁浩, 尹威, 杨欢. 考虑等效电磁损耗电阻偏移的永磁同步电机直流信号注入在线参数辨识方法[J]. 电工技术学报, 0, (): 239606-239606. Yilin Ma, Hao Yuan, Wei Yin, Huan Yang. DC-signal-injection-based Online Parameters Identification for Permanent Magnet Synchronous Machine Considering Variation of Equivalent Electromagnetic Loss Resistance. Transactions of China Electrotechnical Society, 0, (): 239606-239606.

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[1] 郭磊磊, 王朋帅, 李琰琰, 等. 不同代价函数下永磁同步电机模型预测控制参数失配可视化分析[J].电工技术学报, 2023, 38(04): 903-914.
Guo Leilei, Wang Pengshuai, Li Yanyan, et al.Visual Analysis of Parameters Mismatch in Model Predictive Control for Permanent Magnet Synchronous Motor Under Different Cost Functions[J]. Transactions of China Electrotechnical Society, 2023, 38(04): 903-914.
[2] Balasubramanian L., Bhuiyan N.A., Javied A., et al. Design and Optimization of Interior Permanent Magnet (IPM) Motor for Electric Vehicle Applications[J]. CES Transactions on Electrical Machines and Systems, 2023, 07(02): 202-209.
[3] 朱洒, 曾峰, 陆剑波, 等. 考虑PWM谐波损耗的车用扁线内嵌式永磁同步电机效率图简化工程计算[J].电工技术学报, 2022, 37(22): 5687-5703.
Zhu Sa, Zeng Feng, Lu Jianbo, et al.Simplified Engineering Calculation of Efficiency Map of Interior Permanent Magnet Synchronous Machines with Hairpin Windings Considering PWM-Induced Harmonic Losses[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5687-5703.
[4] 高剑, 李承栩, 黄守道, 等. 高磁路饱和永磁同步电机永磁体负载磁链动态估算[J]. 电工技术学报, 2022, 37(22): 5638-5648.
Gao Jian, Li Chengxu, Huang Shoudao, et al.Dynamic Estimation of Permanent Magnet Load Flux Linkage of Permanent Magnet Synchronous Motor with High Magnetic Circuit Saturation[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5638-5648.
[5] Hang Jun, Wu Han, Ding Shichuan, et al.Improved Loss Minimization Control for IPMSM Using Equivalent Conversion Method[J]. IEEE Transactions on Power Electronics, 2021, 36(02): 1931-1940.
[6] Reigosa D., Kang Y. g., Martínez M., et al. SPMSMs Sensorless Torque Estimation Using High-Frequency Signal Injection[J]. IEEE Transactions on Industry Applications, 2020, 56(03): 2700-2708.
[7] 吴荒原, 王双红, 辜承林, 等. 内嵌式永磁同步电机改进型解耦控制[J]. 电工技术学报, 2015, 30(01): 30-37.
Wu Huangyuan, Wang Shuanghong, Gu Chenglin, et al.An Improved Decoupling Control Strategy for the IPMSMS[J]. Transactions of China Electrotechnical Society, 2015, 30(01): 30-37.
[8] 李婕, 杨淑英, 谢震, 等. 基于有效信息迭代快速粒子群优化算法的永磁同步电机参数在线辨识[J].电工技术学报, 2022, 37(18): 4604-4613.
Li Jie, Yang Shuying, Xie Zhen, et al.Online Parameter Identification of Permanent Magnet Synchronous Motor Based on Fast Particle Swarm Optimization Algorithm with Effective Information Iterated[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4604-4613.
[9] 苏有成, 陈志辉. 基于电感扰动的三相横向磁通永磁电机参数辨识与估算位置偏差修正[J]. 电工技术学报, 2023, 38(12): 3165-3175.
Su Youcheng, Chen Zhihui.Parameter Identification and Estimated Position Deviation Correction of a Three-Phase Transverse Flux Permanent Magnet Machine Based on Inductance Perturbation Injection[J]. Transactions of China Electrotechnical Society, 2023, 38(12): 3165-3175.
[10] Li Xinyue, Kennel R.General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM[J]. IEEE Transactions on Industrial Electronics, 2021, 68(04): 2856-2864.
[11] 连传强, 肖飞, 高山, 等. 基于实验标定及双时间尺度随机逼近理论的内置式永磁同步电机参数辨识[J]. 中国电机工程学报, 2019, 39(16): 4892-4898+4991.
Lian Chuanqiang, Xiao Fei, Gao Shan, et al. Parameter Identification for Interior Permanent Magnet Synchronous Motor Based on Experimental Calibration and Stochastic Approximation Theory with Two Time Scales[J]. Proceedings of the CSEE, 2019, 39(16): 4892-4898+4991.
[12] Dang D.Q., Rafaq M. S., Choi H. H., et al. Online Parameter Estimation Technique for Adaptive Control Applications of Interior PM Synchronous Motor Drives[J]. IEEE Transactions on Industrial Electronics, 2016, 63(03): 1438-1449.
[13] Zhang Jindong, Peng Fei, Huang Yunkai, et al.Online Inductance Identification Using PWM Current Ripple for Position Sensorless Drive of High-Speed Surface-Mounted Permanent Magnet Synchronous Machines[J]. IEEE Transactions on Industrial Electronics, 2022, 69(12): 12426-12436.
[14] Choi K., Kim Y., Kim K.-S., et al. Using the Stator Current Ripple Model for Real-Time Estimation of Full Parameters of a Permanent Magnet Synchronous Motor[J]. IEEE Access, 2019, 07: 33369-33379.
[15] 姚绪梁, 黄乘齐, 王景芳, 等. 具有参数辨识功能的永磁同步电机双矢量模型预测电流控制[J/OL]. 中国电机工程学报, 2023: 1-13.
Yao Xuliang, Huang Shengqi, Wang Jingfang, et al.A Two-Vector-Based Model Predictive Current Control with Online Parameter Identification for PMSM Drives[J/OL]. Proceedings of the CSEE, 2023: 1-13.
[16] Liu Zirui, Fan Xinggang, Kong Wubin, et al.Improved Small-Signal Injection-Based Online Multiparameter Identification Method for IPM Machines Considering Cross-Coupling Magnetic Saturation[J]. IEEE Transactions on Power Electronics, 2022, 37(12): 14362-14374.
[17] Wang Qiwei, Wang Gaolin, Zhao Nannan, et al.An Impedance Model-Based Multiparameter Identification Method of PMSM for Both Offline and Online Conditions[J]. IEEE Transactions on Power Electronics, 2021, 36(01): 727-738.
[18] 吴春, 赵宇纬, 孙明轩. 采用测量电压的永磁同步电机多参数在线辨识[J]. 中国电机工程学报, 2020, 40(13): 4329-4340.
Wu Chun, Zhao Yuwei, Sun Mingxuan.Multiparameter Online Identification for Permanent Magnet Synchronous Machines Using Voltage Measurements[J]. Proceedings of the CSEE, 2020, 40(13): 4329-4340.
[19] Feng Guodong, Lai Chunyan, Mukherjee K., et al.Current Injection-Based Online Parameter and VSI Nonlinearity Estimation for PMSM Drives Using Current and Voltage DC Components[J]. IEEE Transactions on Transportation Electrification, 2016, 02(02): 119-128.
[20] 谷鑫, 胡升, 史婷娜, 等. 基于神经网络的永磁同步电机多参数解耦在线辨识[J]. 电工技术学报, 2015, 30(06): 114-121.
Gu Xin, Hu Sheng, Shi Tingna, et al.Muti-Parameter Decoupling Online Identification of Permanent Magnet Synchronous Motor Based on Neural Network[J]. Transactions of China Electrotechnical Society, 2015, 30(06): 114-121.
[21] 刘细平, 胡卫平, 丁卫中, 等. 永磁同步电机多参数辨识方法研究[J]. 电工技术学报, 2020, 35(06): 1198-1207.
Liu Xiping, Hu Weiping, Ding Weizhong, et al.Research on Multi-Parameter Identification Method of Permanent Magnet Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2020, 35(06): 1198-1207.
[22] Li Chen Kudra B., Balaraj V., et al.Absolute Inductance Estimation of PMSM Considering High-Frequency Resistance[J]. IEEE Transactions on Energy Conversion, 2021, 36(01): 81-94.
[23] Balamurali A., Kundu A., Li Ze,et al.Improved Harmonic Iron Loss and Stator Current Vector Determination for Maximum Efficiency Control of PMSM in EV Applications[J]. IEEE Transactions on Industry Applications, 2021, 57(01): 363-373.
[24] Kumar P., Bhaskar D.V., Muduli U. R., et al. Iron-Loss Modeling with Sensorless Predictive Control of PMBLDC Motor Drive for Electric Vehicle Application[J]. IEEE Transactions on Transportation Electrification, 2021, 07(03): 1506-1515.
[25] 曹阳, 刘旭. 计及损耗的混合励磁电机建模与硬件在环实时仿真系统[J]. 电工技术学报, 2020, 35(22): 4657-4665.
Cao Yang, Liu Xu.Modeling Method for Hybrid-Excited Machine and Hardware-in-Loop Real-Time Simulation System with Accounting for Loss Calculation[J]. Transactions of China Electrotechnical Society, 2020, 35(22): 4657-4665.
[26] Urasaki N., Senjyu T., Uezato K.Relationship of parallel model and series model for permanent magnet synchronous motors taking iron loss into account[J]. IEEE Transactions on Energy Conversion, 2004, 19(02): 265-270.
[27] Senjyu T., Shimabukuro T., Uezato K.Vector control of synchronous permanent magnet motors including stator iron loss[J]. International Journal of Electronics, 1996, 80(02): 181-190.
[28] Kazerooni M., Hamidifar S., Kar N.C. Analytical modelling and parametric sensitivity analysis for the PMSM steady-state performance prediction[J]. IET Electric Power Applications, 2013, 07(07): 586-596.
[29] Feng Guodong, Lai Chunyan, Kar N.C. A Novel Current Injection-Based Online Parameter Estimation Method for PMSMs Considering Magnetic Saturation[J]. IEEE Transactions on Magnetics, 2016, 52(07): 1-4.
[30] Zhu Ziqiang, Liang Dawei, Liu Kan.Online Parameter Estimation for Permanent Magnet Synchronous Machines: An Overview[J]. IEEE Access, 2021, 09: 59059-59084.
[31] Kemmetmüller W., Faustner D., Kugi A.Modeling of a Permanent Magnet Synchronous Machine With Internal Magnets Using Magnetic Equivalent Circuits[J]. IEEE Transactions on Magnetics, 2014, 50(06): 1-14.
[32] Kim J., Park Y.Approximate Closed-Form Formula for Calculating Ohmic Resistance in Coils of Parallel Round Wires with Unequal Pitches[J]. IEEE Transactions on Industrial Electronics, 2015, 62(06): 3482-3489.
[33] Yu Yelong, Huang Xiaoyan, Li Zhaokai, et al.Full Parameter Estimation for Permanent Magnet Synchronous Motors[J]. IEEE Transactions on Industrial Electronics, 2022, 69(05): 4376-4386.