|
|
Model predictive current control based on series cost function for five-phase permanent magnet synchronous machines |
XIAO Haifeng1, XU Yuhao1, LI Wenzhen2, NING Dalong1, MA Zhao1 |
1. School of Electronics Engineering, Xi’an Aeronautical University, Xi’an 710077; 2. School of Vehicle Engineering, Xi’an Aeronautical University, Xi’an 710077 |
|
|
Abstract In the model predictive control for five-phase permanent magnet synchronous motor (PMSM), it is difficult to ensure the optimal control performance by using calculation method to obtain the weighting factor. To solve this problem, a model predictive current control based on series cost function is proposed for five-phase PMSM. Firstly, the principle of selecting the optimal voltage vector by the proposed method is analyzed in detail. Then, combined with the characteristics of five-phase PMSM, the control priority of controlled variables is set for designing two cost function schemes without weight factor. The maximum torque scheme can generate a trapezoidal stator voltage, improving the DC bus voltage utilization rate and the system loading capacity. The minimum harmonic current scheme can reduce the harmonic of stator current, obtaining small system noise and vibration. The experimental results indicate that the proposed method can ensure the optimality of the voltage vector applied. Therefore, the five-phase PMSM obtains good performance under different working operation, such as small torque ripple, fast dynamic response and small harmonic current.
|
Received: 17 May 2023
|
|
|
|
Cite this article: |
XIAO Haifeng,XU Yuhao,LI Wenzhen等. Model predictive current control based on series cost function for five-phase permanent magnet synchronous machines[J]. Electrical Engineering, 2023, 24(8): 1-11.
|
|
|
|
URL: |
http://dqjs.cesmedia.cn/EN/Y2023/V24/I8/1
|
[1] AHMED S M, ABU-RUB H, SALAM Z.Common-mode voltage elimination in a three-to-five-phase dual matrix converter feeding a five-phase open-end drive using space-vector modulation technique[J]. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6051-6063. [2] 陈隆, 周扬忠. 一种基于虚拟矢量的T型三电平并网逆变器改进型模型预测控制策略[J]. 电气技术, 2022, 23(11): 37-43. [3] BHOWATE A, AWARE M V, SHARMA S.Predictive torque control algorithm for a five-phase induction motor drive for reduced torque ripple with switching frequency control[J]. IEEE Transactions on Power Electronics, 2020, 35(7): 7282-7294. [4] TATTE Y N, AWARE M V.Torque ripple and harmonic current reduction in a three-level inverter-fed direct-torque-controlled five-phase induction motor[J]. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5265-5275. [5] PRIESTLEY M, FLETCHER J E, TAN C.Space-vector PWM technique for five-phase open-end winding PMSM drive operating in the overmodulation region[J]. IEEE Transactions on Industrial Electronics, 2018, 65(9): 6816-6827. [6] 齐昕, 苏涛, 周珂, 等. 交流电机模型预测控制策略发展概述[J]. 中国电机工程学报, 2021, 41(18): 6408-6419. [7] 刘国海, 宋成炎, 徐亮, 等. 基于SVPWM的五相永磁同步电机两相开路故障容错控制策略[J]. 电工技术学报, 2019, 34(1): 23-32. [8] VAZQUEZ S, RODRIGUEZ J, RIVERA M, et al.Model predictive control for power converters and drives: advances and trends[J]. IEEE Transactions on Industrial Electronics, 2017, 64(2): 935-947. [9] 张晓光, 闫康, 张文涵. 开绕组永磁同步电机混合双矢量模型预测控制[J]. 电工技术学报, 2021, 36(1): 96-106. [10] MAMDOUH M, ALI ABIDO M.Efficient predictive torque control for induction motor drive[J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 6757-6767. [11] XU Yuhao, HE Yuyao, LI Shengchao.Logical operation-based model predictive control for quasi-Z-source inverter without weighting factor[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9(1): 1039-1051. [12] 丁雄, 林国庆. 三相并网逆变器的改进模型预测控制研究[J]. 电气技术, 2020, 21(3): 16-21. [13] HE Yuyao, XU Yuhao.Dynamic model predictive current control based on deviation for permanent magnet synchronous motor[C]//IEEE 28th Inter-national Symposium on Industrial Electronics, Vancouver, 2019: 313-317. [14] 张晓光, 张亮, 侯本帅. 永磁同步电机优化模型预测转矩控制[J]. 中国电机工程学报, 2017, 37(16): 4800-4809. [15] 许嘉杰, 李锐华, 胡波. 基于三矢量模型预测控制的T型三电平整流器定频控制策略[J]. 电气技术, 2022, 23(6): 17-23. [16] VARGAS R, CORTES P, AMMANN U, et al.Predictive control of a three-phase neutral-point-clamped inverter[J]. IEEE Transactions on Industrial Electronics, 2007, 54(5): 2697-2705. [17] CORTES P, KOURO S, VARGAS R, et al.Guidelines for weighting design in model predictive control of power converters and drives[C]//2009 IEEE Inter-national Conference on Industrial Technology (ICIT), Churchill, Victoria, Australia, 2009. [18] NOVAK M, XIE Haotian, 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, 2021, 68(8): 7309-7319. [19] MAHCADO O, MARTIN P, RODRIGUEZ F J, et al.A neural network-based dynamic cost function for the implementation of a predictive current controller[J]. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2946-2955. [20] DASTJERDI R S, ALI ABBASIAN M, SAGHAFI H, et al.Performance improvement of permanent-magnet synchronous motor using a new deadbeat-direct current controller[J]. IEEE Transactions on Power Electronics, 2019, 34(4): 3530-3543. [21] ROJAS C A, RODRIGUEZ J, VILLARROEL F, et al.Predictive torque and flux control without weighting factors[J]. IEEE Transactions on Industrial Electronics, 2013, 60(2): 681-690. [22] NORAMBUENA M, RODRIGUEZ J, ZHANG Zhenbin, et al.A very simple strategy for high-quality per-formance of AC machines using model predictive control[J]. IEEE Transactions on Power Electronics, 2018, 34(1): 794-800. [23] 刘涛, 习金玉, 宋战锋, 等. 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(1): 107-119. |
|
|
|