基于新型虚拟矢量调制方法的IPMSM模型预测电流控制方法

doi:10.19595/j.cnki.1000-6753.tces.230105

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1341 KB)
输出: BibTeX | EndNote (RIS)

摘要

模型预测控制方法已经被广泛应用于内置式永磁同步电机(interior permanent magnet synchronous motor,IPMSM)的控制之中,传统模型预测控制方法由于候选电压矢量数量有限,在电机运行过程中会产生较大的电流和转矩谐波。为了增加候选电压矢量数量,一些论文提出了构建虚拟电压矢量的方法。但是在应用虚拟矢量调制方法时,系统计算的负担会随虚拟矢量数量的增加而增加,因此控制效果会受到系统计算能力的限制。针对此问题,该文提出了一种新型的虚拟电压矢量调制方法,首先将电压扇区进行细分来增加虚拟电压矢量的数量,然后通过对指令电压矢量坐标进行标幺化处理的方法求得其角度,最后从基础和虚拟电压矢量之中由角度搜索选出最优电压矢量。在该方法中,系统的计算量大小与虚拟电压矢量的数量无关,因此可以在不增加系统计算负担的情况下提升系统的控制性能。实验结果表明,与虚拟矢量数量受到限制的虚拟矢量预测控制方法相比,该方法有效减少了三相电流谐波含量,并且减少了控制算法的执行时间。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	汪逸哲
	黄晟
	廖武
	张冀
	黄守道

关键词 ：永磁同步电机, 模型预测电流控制, 虚拟电压矢量, 占空比调制

Abstract：

The model predictive control method has been widely applied in the control of internal permanent magnet synchronous motor (IPMSM). Conventional model predictive control methods can only select voltage vectors from the basic voltage vector, so the number of candidate voltage vectors is limited, which will generate large current and torque harmonics during motor operation. In order to increase the number of candidate voltage vectors, some papers have proposed methods for constructing virtual voltage vectors. However, when applying virtual vector modulation methods, the computational burden of the system will increase with the increase of the virtual vectors number, so the control performance will be limited by the system's computing power. To address this problem, this article proposes a novel virtual voltage vector modulation method, which reduces the burden of system computation by calculating the angle of the voltage vector.
The specific steps of the proposed novel virtual voltage vector modulation method are as follows. First, coordinate transformation is performed on the command voltage vector, and the command voltage vector is normalized by incorporating the magnitude of the DC bus voltage. After obtaining the coordinates of the normalized command voltage vector, the sector where the command voltage vector is located is determined by the the coordinate values. And the composite relationship between the command voltage vector and the adjacent two basic voltage vectors is obtained. By obtaining the composite relationship, the angle of the command voltage vector in the sector is obtained, and then the processing of the voltage vector is equivalent to the first voltage sector. Subsequently, each voltage sector is evenly divided into N parts to construct virtual voltage vectors, and the virtual voltage vectors in the first voltage sector are numbered. Then the virtual voltage vector with the closest angle to the command voltage vector is selected through the rounding function. Finally, the optimal duty cycle is obtained through the cost function. The opening time of each phase of the three-phase switch is calculated based on the obtained optimal voltage vector number and duty cycle.
In order to verify the effectiveness and reliability of the proposed method, this article conducted experiments on an IPMSM with a rated power of 1.5kW. The experimental results show that under steady-state conditions, compared to the predictive control method using a limited number of voltage vectors, this method reduces the harmonic content of the three-phase current by up to 3.4%. Moreover, when the number of sector partitions N is large enough, the proposed method can achieve current control performance close to that of the deadbeat predictive current control using SVPWM modulation at different speeds without SVPWM modulation. In dynamic situations, the proposed method can achieve faster dynamic response and smaller current harmonics during sudden changes in torque and speed. In addition, due to the omission of the traversal optimization process, the proposed method effectively reduces the system execution time, and the execution time of the system is the same for different N values.
In conclusion, the proposed method simplifies the modulation process of the model predictive control method, avoids the complex calculation caused by traversal optimization, and achieves the goal of increasing the number of virtual voltage vectors unrestricted.

Key words： Permanent magnet synchronous motor model predictive current control virtual voltage vector duty cycle modulation

PACS:

TM351

基金资助:

国家自然科学基金（52277047）资助项目

通讯作者: 黄晟(1988),男,博士,教授,研究方向为风电机组群协调优化控制技术等。E-mail：huangsheng319@hnu.edu.cn

作者简介: 汪逸哲(1994),男,博士研究生,研究方向为永磁同步电机设计与控制技术等。E-mail：wangyizhe927@hnu.edn.cn

引用本文:

汪逸哲, 黄晟, 廖武, 张冀, 黄守道. 基于新型虚拟矢量调制方法的IPMSM模型预测电流控制方法[J]. 电工技术学报, 0, (): 8920-. Wang Yizhe, Huang Sheng, Liao Wu, Zhang Ji, Huang Shoudao. IPMSM Model Predictive Current Control Method Based on a Novel Virtual Vector Modulation Method. Transactions of China Electrotechnical Society, 0, (): 8920-.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.230105 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/8920

[1] 梅三冠, 卢闻州, 樊启高, 等. 基于滑模观测器误差补偿的永磁同步电机无位置传感器控制策略[J].电工技术学报, 2023,38(02):398-408.
Mei Sanguan, Lu Wenzhou, Fan Qigao, et al.Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Error Compensation Estimated by Sliding Mode Observer[J]. Transactions of China Electrotechnical Society, 2023, 38(02):398-408.
[2] 章回炫, 范涛, 边元均, 等. 永磁同步电机高性能电流预测控制[J]. 电工技术学报, 2022,37(17): 4335-4345. Zhang Huixuan, Fan Tao, Bian Yuanjun, et al. Predictive Current Control Strategy of Permanent Magnet Synchronous Motors with High Performance [J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4335-4345.
[3] 李思毅, 苏健勇, 杨贵杰. 基于自抗扰控制的永磁同步电机弱磁控制策略[J]. 电工技术学报, 2022, 37(23):6135-6144. Li Siyi, Su Jianyong, Yang Guijie. Flux Weakening Control Strategy of Permanent Magnet Synchronous Motor Based on Active Disturbance Rejection Control [J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6135-6144.
[4] 谷鑫, 鲁金月, 王志强, 等. 基于无差拍电流预测控制的永磁同步电机谐波电流抑制策略[J]. 电工技术学报, 2022, 37(24): 6345-6356.
Gu Xin, Lu Jinyue, Wang Zhiqiang, et al.Harmonic Current Suppression Strategy for Permanent Magnet Synchronous Motor Based on Deadbeat Current Prediction Control[J]. Transactions of China Electrotechnical Society, 2022, 37(24): 6345-6356.
[5] 李祥林, 薛志伟, 阎学雨, 等. 基于电压矢量快速筛选的永磁同步电机三矢量模型预测转矩控制[J].电工技术学报, 2022,37(07): 1666-1678. Li Xianglin, Xue Zhiwei, Yan Xueyu, et al. Voltage Vector Rapid Screening-Based Three-Vector Model Predictive Torque Control for PermanentMagnet Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2022, 37(07): 1666-1678.
[6] Li Xinyue, Tian Wei, Gao Xiaonan, et al.A Generalized Observer-Based Robust Predictive Current Control Strategy for PMSM Drive System[J]. IEEE Transactions on Industrial Electronics, 2022, 69(2): 1322-1332.
[7] Karamanakos P, Geyer T.Guidelines for the design of finite control set model predictive controllers[J]. IEEE Transactions on Power Electronics, 2020, 35(7): 7434-7450.
[8] 姚绪梁, 麻宸伟, 王景芳, 等. 基于预测误差补偿的鲁棒型永磁同步电机模型预测电流控制[J]. 中国电机工程学报, 2021, 41(17): 6071-6081.
Yao Xuliang, Ma Chenwei, Wang Jingfang, et al.Robust Model Predictive Current Control for PMSM Based on Prediction Error Compensation[J]. Proceedings of the CSEE, 2021, 41(17): 6071-6081.
[9] Zhang Xiaoguang, Hou Benshuai, Yang Mei.Deadbeat Predictive Current Control of Permanent-Magnet Synchronous Motors with Stator Current and Disturbance Observer[J]. IEEE Transactions on Power Electronics, 2017,32(5):3818-3834, May 2017.
[10] Vafaie M H, Dehkordi B M, Moallem P, et al.Improving the steady-state and transient-state performances of PMSM through an advanced deadbeat direct torque and flux control system[J]. IEEE Transactions on Power Electronics, 2017, 32(4): 2964-2975.
[11] 颜宁, 曹鑫, 张蕾, 等. 基于直接转矩控制的开关磁阻电机模型预测控制方法[J]. 中国电机工程学报, 2017, 37(18): 5446-5453.
Yan Ning, Cao xin, Zhang Lei, et al. Direct Torque Control-Based Model Predictive Control of Switched Reluctance Motors[J]. Proceedings of the CSEE, 2017, 37(18): 5446-5453.
[12] Zhang Yongchang, Bai Yuning, Yang Haitao.A universal multiple-vector-based model predictive control of induction motor drives[J]. IEEE Transactions on Power Electronics, 2018, 33(8): 6957-6969.
[13] Yan Liming, Dou Manfeng, Hua Zhiguang, et al.Optimal duty cycle model predictive current control of high-altitude ventilator induction motor with extended minimum stator current operation[J]. IEEE Transactions on Power Electronics, 2018, 33(8): 7240-7251.
[14] 刘佳敏,葛召炎,吴轩,等.基于占空比调制的永磁同步电机预测电流控制[J].中国电机工程学报,2020,40(10):3319-3327.
Liu Jiamin, Ge Zhaoyan, Wu Xuan, et al.Predictive Current Control of Permanent Magnet Synchronous Motor Based on Duty-cycle Modulation[J]. Proceedings of the CSEE, 2020, 40(10): 3319-3327.
[15] 夏长亮, 仇旭东, 王志强, 等. 基于矢量作用时间的新型预测转矩控制[J]. 中国电机工程学报, 2016, 36(11): 3045-3053.
Xia Changliang, Qiu Xudong, Wang Zhiqiang, et al.Predictive Torque Control Based on Optimal Operating Time of Vector[J]. Proceedings of the CSEE, 2016, 36(11): 3045-3053.
[16] Vazquez S, Leon J I, Franquelo L G, et al.Model Predictive Control with constant switching frequency using a Discrete Space Vector Modulation with virtual state vectors[C]. IEEE International Conference on Industrial Technology, 2009.
[17] 徐艳平, 张保程, 周钦. 永磁同步电机双矢量模型预测电流控制[J]. 电工技术学报, 2017, 32(20): 222-230.
Xu Yanping, Zhang Baocheng, Zhou qin. Two-Vector Based Model Predictive Current Control for Permanent Magnet Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2017, 32(20): 222-230.
[18] 史婷娜, 张维, 肖萌, 等. 基于矢量作用时间的永磁同步电机预测电流控制[J]. 电工技术学报, 2017, 32(19): 1-10.
Shi Tingna, Zhang Wei, Xiao Meng, et al.Predictive current control for permanent magnet synchronous motor based on operating time of vector[J]. Transactions of China Electrotechnical Society, 2017, 32(19): 1-10.
[19] Zhou Zhanqing, Xia Changliang, Yan Yan, et al.Torque Ripple Minimization of Predictive Torque Control for PMSM With Extended Control Set[J]. IEEE Transactions on Industrial Electronics, 2017, 69(9): 6930-6939.
[20] Wang Yuanlin, Wang Xiaocan, Xie Wei, et al.Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives[J]. IEEE Transactions on Industrial Electronics, 2017, 64(5): 3537-3547.
[21] Agoro S and Husain I. Robust Deadbeat Finite-Set Predictive Current Control With Torque Oscillation and Noise Reduction for PMSM Drives[J]. IEEE Transactions on Industry Applications, 2022, 58(1): 365-374.
[22] Zhang Xiaoguang and Wang Ziwei. Simple Robust Model Predictive Current Control for PMSM Drives Without Flux-Linkage Parameter[J]. IEEE Transactions on Industrial Electronics, 2023, 70(4): 3515-3524.
[23] Morel F, Lin-Shi X, Retif J, et al.A Comparative Study of Predictive Current Control Schemes for a Permanent-Magnet Synchronous Machine Drive[J]. IEEE Transactions on Industrial Electronics, 2009, 56(7): 2715-2728.