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Abstract Recently, transverse flux permanent magnet machine (TFPMM) is widely used in electric vehicles and other direct-drive applications because of its high output torque. The sensorless control of TFPMM can omit mechanical sensors, reduce cost and improve system reliability. However, the stator resistance, inductance and flux linkage change due to the influence of temperature, magnetic saturation and load disturbance during the operation of the motor, and the nonideal factors such as the nonlinearity of the inverter lead to errors between the command voltage and the actual voltage, thus causing deviations between the estimated position and the actual position. In this paper, a parameter identification method based on inductance perturbations which does not rely on the accuracy of any parameters is proposed to correct the position deviation.
Firstly, the two main factors that cause position deviation under steady state, model parameter error and inverter nonlinearity, are analyzed. It can be intuitively obtained that the position deviation is only affected by the inductance parameter error in the sliding mode observer as soon as iδ=0. Secondly, according to the torque equation of TFPMM, the inductance perturbations of +Δd and -Δd are injected separately and average values of γ-axis current are extracted after the γ-axis current reaches steady state. Based on the assumption that the load torque changes slowly, the nonlinear equations of inductance error and the permanent magnet flux linkage are constructed. The Levenberg- Marquardt algorithm, which improves the non-convergence of the solution when the Jacobian matrix is ill-conditioned, is used to solve the nonlinear equations and realizes the identification of inductance and permanent magnet flux linkage. Finally, the identified inductance is fed back to the observer to correct position deviation.
To verify the effectiveness of the proposed method, specific operating conditions are simulated and investigated based on a prototype of TFPMM. The inductance and the permanent magnet flux linkage can be obtained by Levenberg-Marquardt algorithm with errors of 1.38% and 3.98% respectively. Further experiments are carried out to verify the feasibility and robustness of the method. By analyzing the results under different speeds and loads, it can be concluded that the mechanical angle deviation of the proposed method is controlled within 0.178° under different operating conditions. However, the identification error increases significantly under load conditions. To explain this phenomenon, position deviation equation considering saturation effect is deduced using the active flux concept. It shows that in the case of near-zero saliency, the identified inductance and flux linkage are Lq and ψaf, respectively. However, the permanent magnet flux linkage is not used in the observer, and the position deviation equation considering saturation effect leads to the conclusion that the position deviation can be corrected by modifying the observer parameters after Lq is identified.
The following conclusions can be drawn from the simulation and experimental results: (1) The proposed method does not require accurate parameters of the model and is not affected by the inverter nonlinearity. Only a priori range of permanent magnet flux linkage is required to ensure convergence of the sliding mode observer. (2) The matrix dimension is low, and the calculation is simple and easy to implement. (3) The identified parameters can be solved accurately and the steady-state position error can be suppressed effectively. The proposed method is also suitable for permanent magnet synchronous machine(PMSM) with near-zero saliency. Improvement of position estimation for salient PMSM will be the next focus of our research.
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[1] Chen Jiaxin, Chen Zhihui, Duan Jinjin, et al.Four-phase transverse flux permanent magnet motor control system with AC current control based on PR regulator[C]//2019 22nd International Conference on Electrical Machines and Systems (ICEMS), Harbin, China, 2019: 1-6.
[2] 王彤. 永磁电机无位置传感器控制及在线参数辨识研究[D]. 杭州: 浙江大学, 2019.
[3] 刘计龙, 肖飞, 沈洋, 等. 永磁同步电机无位置传感器控制技术研究综述[J]. 电工技术学报, 2017, 32(16): 76-88.
Liu Jilong, Xiao Fei, Shen Yang, et al.Position-sensorless control technology of permanent-magnet synchronous motor-a review[J]. Transactions of China Electrotechnical Society, 2017, 32(16): 76-88.
[4] 章春娟, 王慧贞, 刘伟峰, 等. 基于宽频带同步基频提取滤波器的永磁同步电机转子位置与转速估计[J]. 电工技术学报, 2022, 37(4): 882-891.
Zhang Chunjuan, Wang Huizhen, Liu Weifeng, et al.Rotor position and speed estimation of permanent magnet synchronous motor based on wideband synchronous FundamentalFrequency extraction filter[J]. Transactions of China Electrotechnical Society, 2022, 37(4): 882-891.
[5] 赵文祥, 刘桓, 陶涛, 等. 基于虚拟信号和高频脉振信号注入的无位置传感器内置式永磁同步电机MTPA控制[J]. 电工技术学报, 2021, 36(24): 5092-5100.
Zhao Wengxiang, Liu Huan, Tao Tao, et al.MTPA control of sensorless IPMSM based on virtual signal and high-frequency pulsating signal injection[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5092-5100.
[6] 麦志勤, 刘计龙, 肖飞, 等. 基于估计位置反馈电流解调算法的改进型高频旋转电压注入无位置传感器控制策略[J]. 电工技术学报, 2022, 37(4): 870-881, 891.
Mai Zhiqin, Liu Jilong, Xiao Fei, et al.Sensorless control strategy of improved HF rotating voltage injection based on estimated position feedback current demodulation algorithm[J]. Transactions of China Electrotechnical Society, 2022, 37(4): 870-881, 891.
[7] 阙鸿杰, 全力, 张丽, 等. 基于自适应滤波器在线解耦的磁场增强型永磁电机无位置传感器控制[J]. 电工技术学报, 2022, 37(2): 344-354.
Que Hongjie, Quan Li, Zhang Li, et al.Sensorless control of flux-intensifying permanent magnet synchronous motor based on adaptive Notch filter online decoupling[J]. Transactions of China Electrotechnical Society, 2022, 37(2): 344-354.
[8] Liu Gang, Zhang Haifeng, Song Xinda.Position-estimation deviation-suppression technology of PMSM combining phase self-compensation SMO and feed-forward PLL[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9(1): 335-344.
[9] Liang Donglai, Li Jian, Qu Ronghai, et al.Adaptive second-order sliding-mode observer for PMSM sensorless control considering VSI nonlinearity[J]. IEEE Transactions on Power Electronics, 2018, 33(10): 8994-9004.
[10] 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(6): 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(6): 1198-1207.
[11] 史婷娜, 刘华, 陈炜, 等. 考虑逆变器非线性因素的表贴式永磁同步电机参数辨识[J]. 电工技术学报, 2017, 32(7): 77-83.
Shi Tingna, Liu Hua, Chen Wei, et al.Parameter identification of surface permanent magnet synchronous machines considering voltage-source inverter nonlinearity[J]. Transactions of China Electrotechnical Society, 2017, 32(7): 77-83.
[12] Bolognani S, Ortombina L, Tinazzi F, et al.Model sensitivity of fundamental-frequency-based position estimators for sensorless pm and reluctance synchronous motor drives[J]. IEEE Transactions on Industrial Electronics, 2018, 65(1): 77-85.
[13] Gu Cong, Wang Xiaolin, Zhang Feilong, et al.Correction of rotor position estimation error for high-speed permanent magnet synchronous motor sensorless drive system based on minimum-current-tracking method[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10): 8271-8280.
[14] Wang Yangrui, Xu Yongxiang, Zou Jibin.Online multiparameter identification method for sensorless control of SPMSM[J]. IEEE Transactions on Power Electronics, 2020, 35(10): 10601-10613.
[15] Tang Qipeng, Chen Duxin, He Xiangning.Integration of improved flux linkage observer and I-f starting method for wide-speed-range sensorless SPMSM drives[J]. IEEE Transactions on Power Electronics, 2020, 35(8): 8374-8383.
[16] Kim H, Son J, Lee J.A high-speed sliding-mode observer for the sensorless speed control of a PMSM[J]. IEEE Transactions on Industrial Electronics, 2011, 58(9): 4069-4077.
[17] Wang Tong, Huang Jin, Ye Ming, et al.An EMF observer for PMSM sensorless drives adaptive to stator resistance and rotor flux linkage[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2019, 7(3): 1899-1913.
[18] Kim H W, Youn M J, Cho K Y, et al.Nonlinearity estimation and compensation of PWM VSI for PMSM under resistance and flux linkage uncertainty[J]. IEEE Transactions on Control Systems Technology, 2006, 14(4): 589-601.
[19] Burden R L, Faires J D, Burden A M.Numerical analysis[M]. Boston: Cengage learning, 2015.
[20] Boldea I, Paicu M C, Andreescu G D.Active flux concept for motion-sensorless unified AC drives[J]. IEEE Transactions on Power Electronics, 2008, 23(5): 2612-2618. |
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