基于新型解析权重因子配置的感应电机模型预测转矩控制

doi:10.19595/j.cnki.1000-6753.tces.221465

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3031 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Finite control set-model predictive control (FCS-MPC) has received extensive attention in the field of electric drive systems in recent years, because of its intuitive concept, fast dynamic response, easy to deal with nonlinear and multivariable constraints. Especially, finite control set-model predictive torque control (FCS-MPTC), which uses electromagnetic torque and stator flux as control variables, has the advantages of fast transient response of electromagnetic torque, easy to handle field weakening control. However, the weighting factor allocation of the FCS-MPTC cost function has always been a research difficulty in the academic community. Since the essence of FCS-MPTC is the exhaustive method of discrete voltage vectors, the setting of weight factor also lacks theoretical basis. Rating method and cut-and-trial method are two classical weighting factor design strategies. The rating method sets the weighting factor as the ratio of rated electromagnetic torque to rated stator flux amplitude. This method can realize the coordinated control of electromagnetic torque and stator flux under rated condition of IM, but the control performance is poor under other conditions. The cut-and-trial method obtains the weighting factor configuration through repeated online debugging, which is not only cumbersome, but also difficult to obtain optimal control.
Firstly, the influence mechanism of different weighting factors on stator current total harmonic distortion (THD) of stator current, root mean square (RMS) of electromagnetic torque and RMS of stator flux linkage is studied. The research results show that the stator flux RMS decreases with the increase of the weighting factor, while the electromagnetic torque RMS is just the opposite, especially the stator current THD decreases first and then increases. Secondly, Stator current THD, RMS of electromagnetic torque and RMS of stator flux linkage of the motor under different operating conditions with a fixed weighting factor are studied. The results show that when the induction motor (IM) FCS-MPTC operates at low speed, the performances of stator flux RMS and electromagnetic torque RMS become worse. For the induction motor FCS-MPTC under low speed and light load, the stator current THD presents large harmonics. Then, the mathematical expression of weighting factor is derived analytically according to the internal relations of electromagnetic relations of IM, which can achieve multi-objective optimal control of induction motor under different working conditions. Because the analytical expression of the weighting factor depends on the motor parameters, this paper integrates the online parameter identification technology, which can achieve the calculation accuracy of the weighting factor and the prediction model. The proposed analytical weighting factor-model predictive torque control (AWF-MPTC) is compared with traditional methods, including dynamic performance, steady performance and robust performance. The experimental results show that AWF-MPTC has smaller stator current THD and electromagnetic torque ripple than traditional MPTC (T-MPTC). In steady state, stator current THD of AWF-MPTC is 11.52%, while that of T-MPTC is 16.13%. T-MPTC stator flux ripple is 0.12 Wb, while AWF-MPTC is 0.068 Wb. At the same time, AWF-MPTC has better robustness than T-MPTC. There are two main reasons for the improvement of robustness performance. First, the analytical weighting factor calculation method realizes the coordinated control of electromagnetic torque and stator flux. Secondly, the online identification of induction motor parameters not only improves the accuracy of the prediction model, but also improves the accuracy of the weighting factor.
Compared with T-MPTC, the proposed AWF-MPTC has better dynamic performance, steady performance and robustness. Under different operating conditions, the optimal coordinated control of electromagnetic torque and stator flux is realized.

Key words： Model predictive control induction motor predictive torque control weighting factor cost function

PACS:

TM346

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yan Liming
	Guo Xin
	Xu Xisheng
	Weiwei

Cite this article:

Yan Liming,Guo Xin,Xu Xisheng等. Model predictive torque control of induction machine drives using novel analytic weighting factor assignment[J]. Transactions of China Electrotechnical Society, 0, (): 62-62.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.221465 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/62

[1] 夏长亮, 方红伟. 永磁无刷直流电机及其控制[J].电工技术学报,2012,27(03):25-34.
Xia Changliang, Fang Hongwei.Permanent-magnet brushless DC motor and its control[J]. Transactions of China Electrotechnical Society, 2012, 27(03): 25-34.
[2] De Klerk M L, Saha A K. A comprehensive review of advanced traction motor control techniques suitable for electric vehicle applications[J]. IEEE Access, 2021.
[3] Sun Yuanxiang, Li Zhen, Zhang Zhenbin.Hybrid predictive control with simple linear control based circulating current suppression for modular multilevel converters[J].CES Transactions on Electrical Machines and Systems, 2019, 3(4).
[4] Zhu Z.Q, Cai S. Hybrid excited permanent magnet machines for electric and hybrid electric vehicles[J]. CES Transactions on Electrical Machines and Systems, 2019, 3(3).
[5] 赵凯辉,周瑞睿,冷傲杰,戴旺坷,黄刚.一种永磁同步电机的有限集无模型容错预测控制算法[J].电工技术学报,2021,36(01):27-38.
Zhao Kaihui, Zhou Ruirui, Leng Aojie, et al.Finite control set model-free fault-tolerant predictive control for permanent magnet synchronous motor[J]. Transactions of China Electrotechnical Society, 2021, 36(01): 27-38.
[6] 滕青芳,李国飞,朱建国,郭有光.三相四开关容错逆变器的PMSM驱动系统FCS-MPC策略[J].电机与控制学报,2016,20(10):15-22.
Teng Qingfang, Li Guofei, Zhu Jianguo, et al.Finite-control-set model predictive control for PMSM systems driven by three-phase four-switch fault-tolerant inverter[J]. Electric Machines and Control, 2016, 20(10): 15-22.
[7] 史婷娜,杨雨要,周湛清,耿强,夏长亮.基于二次型价值函数的双电机转矩同步系统有限集模型预测控制[J].中国电机工程学报,2019,39(15):4531-4541.
Shi Tingna, Yang Yuyao Zhou Zhanqing, et al. FCS-MPC for dual-motor torque synchronization system based on quadratic form cost function[J]. Proceedings of the CSEE, 2019, 39(15): 4531-4541.
[8] Khalilzadeh M, Vaez-Zadeh S, Rodriguez J, et al.Model-free predictive control of motor drives and power converters: A review[J]. IEEE Access, 2021.
[9] 陈卓易,屈稳太,邱建琪.一种开关频率可控的有限集模型预测控制[J].电工技术学报,2022,37(16):4134-4142.
Chen Zhuoyi, Qu Wentai, Qiu Jianqi.A switching-frequency-controlled finite-control-set model predictive control method[J]. Transactions of China Electrotechnical Society. 2022, 37(16): 4134-4142.
[10] Rodriguez J, Garcia C, Mora A, et al.Latest advances of model predictive control in electrical drives—Part II: Applications and benchmarking with classical control methods[J]. IEEE Transactions on Power Electronics, 2021, 37(5): 5047-5061.
[11] Vazquez S, Rodriguez J, Rivera M, et al.Model predic-tive control for power converters and drives: Advances and trends[J]. IEEE Transactions on Industrial Elec-tronics, 2016, 64(2): 935-947.
[12] 宋文胜,郭永琪,余彬,谢宗楚,李建新.五相永磁同步电机全速度范围占空比优化模型预测电流控制[J].中国电机工程学报,2022,42(12):4561-4571.
Song Wensheng, Guo Yongqi, Yu Bin, et al.A model predictive current control of five-phase PMSM with duty ratio optimization in whole speed range[J]. Proceedings of the CSEE, 2022, 42(12): 4561-4571.
[13] Yan L, Wang F, Dou M, et al.Active disturbance-rejection-based speed control in model predictive con-trol for induction machines[J]. IEEE Transactions on Industrial Electronics, 2019, 67(4): 2574-2584.
[14] 史涔溦,马红如,陈卓易,邱建琪.永磁同步电机模糊代价函数预测转矩控制[J].电机与控制学报, 2022, 26(01): 1-8.
Shi Cenwei, Ma Hongru, Chen Zhuoyi, et al.Fuzzy tuning of weight coefficient in model predictive torque control of PMSM[J]. Electric Machines and Control, 2022, 26(01): 1-8.
[15] 刘涛,习金玉,宋战锋,王萌.基于多核并行计算的永磁同步电机有限集模型预测控制策略[J].电工技术学报,2021,36(01):107-119.
Liu Tao, Xi Jinyu, Song Zhanfeng, et al.Finite control set model predictive control of permanent magnet synchronous motor based on multi-core parallel computing[J]. Transactions of China Electrotechnical Society, 2021, 36(01): 107-119.
[16] Wang F, Xie H, Chen Q, et al.Parallel predictive torque control for induction machines without weighting factors[J]. IEEE Transactions on Power Electronics, 2019, 35(2): 1779-1788.
[17] 柳志飞,杜贵平,杜发达.有限集模型预测控制在电力电子系统中的研究现状和发展趋势[J].电工技术学报,2017,32(22):58-69.
Liu Zhifei, Du Guiping, Du Fada.Research status and development trend of finite control set model predictive control in power electronics[J]. Transactions of China Electrotechnical Society, 2017, 32(22): 58-69.
[18] Zhang Y, Yang H, Xia B.Model-predictive control of induction motor drives: Torque control versus flux con-trol[J]. IEEE Transactions on Industry Applications, 2016, 52(5): 4050-4060.
[19] Hu Yuansheng, Hu Cungang, Zhang Pinjia, et al.A novel hybrid seven-level converter for permanent magnet synchronous motor driving system based on model predictive control[J]. CES Transactions on Electrical Machines and Systems, 2019, 3(4).
[20] Zhang K, Fan M, Yang Y, et al.Tolerant sequential model predictive direct torque control of permanent magnet synchronous machine drives[J]. IEEE Transac-tions on Transportation Electrification, 2020, 6(3): 1167-1176.
[21] Rojas C A, Rodriguez J R, Kouro S, et al.Multiobjec-tive fuzzy-decision-making predictive torque control for an induction motor drive[J]. IEEE Transactions on Power Electronics, 2016, 32(8): 6245-6260.
[22] Novak M, Xie H, Dragicevic T, et al.Optimal cost function parameter design in predictive torque control (PTC) using artificial neural networks (ANN)[J]. IEEE Transactions on Industrial Electronics, 2020, 68(8): 7309-7319.
[23] 李家祥,汪凤翔,柯栋梁,李政,何龙.基于粒子群算法的永磁同步电机模型预测控制权重系数设计[J].电工技术学报, 2021, 36(01): 50-59+76.
Li Jiaxiang, Wang Fengxiang, Ke Dongliang, He long weighting factors design of model predictive control for permanent magnet synchronous machine using particle swarm optimization[J]. Transactions of China Electrotechnical Society, 2021, 36(01): 50-59+76.
[24] 张虎,张永昌,夏波,杨海涛.基于空间矢量调制的感应电机无速度传感器模型预测磁链控制[J].电工技术学报,2017,32(03):97-104.
Zhang Hu, Zhang Yongchang, Xia Bo, et al.Speed sensorless model predictive flux control of induction motor drives based on space vector modulation[J]. Transactions of China Electrotechnical Society, 2017, 32(03): 97-104.
[25] 谢昊天,汪凤翔,柯栋梁,唐莹,Kennel Ralph.基于集成优化的感应电机无权重系数预测转矩控制[J].电工技术学报, 2022, 37(12): 2992-3003.
Xie Haotian, Wang Fengxiang, Ke Dongliang, et al.Ensemble optimization based weighting factor-less predictive torque control for induction machines[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 2992-3003.
[26] 张瑞林,卢子广,甘霖,李嘉鑫.感应电机无权重系数模型预测转矩控制[J].电气传动, 2020, 50(10): 102-106.
Zhang Ruilin, Lu Ziguang, Gan Lin, et al.Model predictive torque control of induction motor without weighting factor[J]. Electric drive, 2020, 50(10): 102-106.
[27] 颜黎明,赵冬冬,焦宁飞.基于鲁棒模型的航空交流感应电机预测转矩控制[J].航空学报,2021,42(09):462-472.
Yan Liming, Zhao Dongdong, Jiao Ningfei.Robust model based-predictive torgue control of aviation AC induction motor[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(09): 462-472.
[28] 尹华杰,郭志宇,曾君.滑模无速度传感器感应电机的简化预测转矩控制[J].华南理工大学学报(自然科学版), 2020, 48(12): 18-26.
Yin Huajie, Guo Zhiyu, Zeng Jun.Simplified predictive torque control for induction motor with sliding mode speed-sensorless[J]. Journal of South China University of Technology (Natural Science Edition) 2020, 48(12): 18-26.