RESEARCH ON MODEL PREDICTIVE CONTROL STRATEGY OF VIENNA RECTIFIER BASED ON OPTIMAL SELECTION OF VOLTAGE VECTORS

Dang Chaoliang; Zhai Jiahao; Jiang Zehao; Han Sipeng; Song Weizhang

doi:10.19912/j.0254-0096.tynxb.2025-0279

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 610-618. DOI: 10.19912/j.0254-0096.tynxb.2025-0279

RESEARCH ON MODEL PREDICTIVE CONTROL STRATEGY OF VIENNA RECTIFIER BASED ON OPTIMAL SELECTION OF VOLTAGE VECTORS

Dang Chaoliang^1~3, Zhai Jiahao¹, Jiang Zehao¹, Han Sipeng¹, Song Weizhang¹

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Abstract

To enhance the power quality and conversion efficiency of the grid interface of the new energy power generation system and its energy storage unit, this paper proposes two model predictive control strategies （MPCOVS） based on the optimal selection of voltage vectors for the VIENNA rectifier, aiming at the problems of fluctuating switching frequency, difficulty in selecting the weighting coefficient for the neutral point potential regulation, and large grid-side current ripple in the traditional finite set model predictive control （FCS-MPC）. Firstly, based on the sector division method, the search and optimization scale of the candidate vector set is reduced from 27 to 3, and the vector set is optimally selected by the pre-judgment of the upper and lower DC busbars to achieve the neutral point potential balance control without weighting coefficients. Secondly, two constant-frequency control methods using the synthesis of two vectors and three vectors are utilized to improve the grid-side current ripple, achieving smooth switching of the switch states and effectively enhancing the output performance of the grid-side current through duty cycle optimization. Finally, the proposed methods are verified through simulation and experimental analysis in multiple dimensions such as static characteristics, transient response, and neutral point potential balance control. The results show that the proposed methods can effectively improve the quality of the grid-connected current, reduce the computational complexity and switching losses, and have good steady-state and dynamic performance.

Key words

Vienna rectifier / model predictive control / neutral-point-potential balance / weightless factor / fixed switching frequency

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Dang Chaoliang, Zhai Jiahao, Jiang Zehao, Han Sipeng, Song Weizhang. RESEARCH ON MODEL PREDICTIVE CONTROL STRATEGY OF VIENNA RECTIFIER BASED ON OPTIMAL SELECTION OF VOLTAGE VECTORS[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 610-618 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0279

References

[1] 吴笛. 新能源接入智能电网的技术分析[J]. 太阳能学报, 2023, 44(9): 554.
Wu D.Technical analysis of new energy access to smart grid[J]. Acta Energiae Solaris Sinica, 2023, 44(9): 554.
[2] 李东东, 郭天洋, 刘庆飞, 等. 计及光伏发电的新能源电力系统惯量评估[J]. 太阳能学报, 2021, 42(5): 174-179.
Li D D, Guo T Y, Liu Q F, et al.Inertia estimation of renewable power system considering photovoltaics[J]. Acta Energiae Solaris Sinica, 2021, 42(5): 174-179.
[3] Shi Z C, Wu Y W, Gao X, et al.A novel suppression method for input current zero-crossing distortion of the Vienna rectifier based on negative-sequence current regulation under the unbalanced grid[J]. IEEE Journal of Emerging and Selected Topics In Power Electronics, 2024, 12(4): 3699-3714.
[4] 朱文杰, 任聪, 许赢, 等. 基于滑模控制的维也纳整流器电流谐波抑制[J]. 电工技术, 2022(11): 36-39.
Zhu W J, Ren C, Xu Y, et al.Current harmonic suppression of Vienna rectifier based on sliding mode control[J]. Electric Engineering, 2022(11): 36-39.
[5] 洪剑峰, 张兴, 曹仁贤, 等. 三电平并网逆变器的改进型有限集模型预测控制[J]. 太阳能学报, 2022, 43(8): 67-74.
Hong J F, Zhang X, Cao R X, et al.Improved finite control set model predictive control of three-level grid-connected inverter[J]. Acta Energiae Solaris Sinica, 2022, 43(8): 67-74.
[6] 万劭琦, 汪波, 关文卿, 等. 改进虚拟空间矢量法的PMSM模型预测控制策略[J]. 电气传动, 2024, 54(12): 8-15.
Wan S Q, Wang B, Guan W Q, et al.PMSM model predictive control strategy based on improved virtual space vector method[J]. Electric Drive, 2024, 54(12): 8-15.
[7] 李圣清, 邓娜, 颜石, 等. 基于改进蚁群动态规划的光储微网容量优化配置[J]. 太阳能学报, 2023, 44(2): 468-476.
Li S Q, Deng N, Yan S, et al.Optimal configuration optimization of PV energy storage microgrid using improved ant colony dynamic programming[J]. Acta Energiae Solaris Sinica, 2023, 44(2): 468-476.
[8] 邹继斌, 李炳均, 张文韬, 等. 双三相同步磁阻电机单相开路故障下的容错控制策略[J]. 中国电机工程学报, 2024, 44(14): 5736-5746.
Zou J B, Li B J, Zhang W T, et al.Fault-tolerant control strategy for dual three-phase synchronous reluctance motor under one-phase open-circuit fault[J]. Proceedings of the CSEE, 2024, 44(14): 5736-5746.
[9] 张艳峰, 杨锡运, 徐恩慧. 漂浮式海上风电机组无模型自适应容错控制策略[J]. 太阳能学报, 2024, 45(10): 467-477.
Zhang Y F, Yang X Y, Xu E H.Model-free adaptive fault-tolerant control strategy for floating offshore wind turbines[J]. Acta Energiae Solaris Sinica, 2024, 45(10): 467-477.
[10] 张泽华, 宋桂英, 侯明宣, 等. 基于无源性的直流微电网并联变换器控制策略[J]. 太阳能学报, 2022, 43(11): 501-507.
Zhang Z H, Song G Y, Hou M X, et al.Control strategy of parallel converters in DC microgrid based on passivity[J]. Acta Energiae Solaris Sinica, 2022, 43(11): 501-507.
[11] 程启明, 王海伦, 沈治超, 等. 不平衡电网下Y源双级矩阵变换器的无源滑模控制策略[J]. 高电压技术, 2024, 50(11): 4789-4801.
Cheng Q M, Wang H L, Shen Z C, et al.Passivity-based sliding-mode control strategy based on Y-source two-stage matrix converter under unbalanced power grid[J]. High Voltage Engineering, 2024, 50(11): 4789-4801.
[12] 樊启高, 吕华阳, 毕恺韬, 等. 面向直流储能系统的飞跨电容三电平双向升降压变换器及其模型预测控制策略[J]. 电工技术学报, 2022, 37(16): 4169-4179.
Fan Q G, Lyu H Y, Bi K T, et al.Flying capacitor three-level bi-directional buck-boost converter and its model predictive control strategy for DC energy storage system[J]. Transactions of China Electrotechnical Society, 2022, 37(16): 4169-4179.
[13] 陈荣, 翟凯淼, 舒胡平. 永磁同步电机双矢量固定开关频率模型预测控制研究[J]. 电工技术学报, 2023, 38(14): 3812-3823.
Chen R, Zhai K M, Shu H P.Predictive control of dual vector fixed switching frequency model for permanent magnet synchronous motor[J]. Transactions of China Electrotechnical Society, 2023, 38(14): 3812-3823.
[14] 高曼曼, 龙珊珊, 王飞, 等. 基于双矢量的三电平PFC中点电位平衡无权重型模型预测控制[J]. 电源学报, 2024, 22(1): 41-48.
Gao M M, Long S S, Wang F, et al.Neutral-point potential balance unweighted factor model predictive control of three-level PFC based on double vectors[J]. Journal of Power Supply, 2024, 22(1): 41-48.
[15] Tang X M, Niu S X, Yuan X.Low complexity model predictive control method for three-level converters with fixed switching frequency mode[C]//IECON 2023-49th Annual Conference of the IEEE Industrial Electronics Society. Singapore, 2023: 1-6.
[16] Huang X P, Yang Z K, Chen G T, et al.Model predictive control of current with fixed switching frequency on VEINNA rectifier[C]//2020 Chinese Control and Decision Conference (CCDC). Hefei, China, 2020: 2392-2397.
[17] Liu C, Xing X Y, Du C S, et al.An improved model predictive control method using optimized voltage vectors for Vienna rectifier with fixed switching frequency[J]. IEEE Transactions on Power Electronics, 2023, 38(1): 358-371.
[18] Pei Y S, Tang Y, Xu H C, et al.Optimized carrier-based discontinuous PWM without regulating NP voltage for three-level Vienna rectifier[J]. IEEE Journal of Emerging and Selected Topics In Power Electronics, 2024, 12(1): 107-117.
[19] 许萌, 郭辉. NPC三电平逆变器目标合成固定开关频率MPC[J]. 电力电子技术, 2021, 55(5): 136-140.
Xu M, Guo H.Npc three-level inverter multi-objective synthesis MPC with fixed switching frequency[J]. Power Electronics, 2021, 55(5): 136-140.
[20] 张笛, 崔晶, 林辉, 等. 改进的单相三电平PWM整流器中点电位平衡模型预测控制[J]. 太阳能学报, 2024, 45(6): 125-131.
Zhang D, Cui J, Lin H, et al.Improved midpoint potential balance model prediction control for single-phase three-level PWM rectifier[J]. Acta Energiae Solaris Sinica, 2024, 45(6): 125-131.
[21] Zhang N N, Li S X, Qiu J T, et al.A fast dual vector model predictive voltage control strategy for the permanent magnet synchronous generator based on T-type three-level converter[C]//2024 IEEE 7th International Electrical and Energy Conference (CIEEC). Harbin, China, 2024: 4068-4073.
[22] 徐艳平, 王极兵, 王建渊, 等. 考虑预测误差的改进双矢量模型预测电流控制[J]. 电气传动, 2018, 48(9): 62-66.Xu Y P, Wang J B, Wang J Y, et al. Improved two-vector model predictive current control considering prediction errors[J]. Electric Drive, 2018, 48(9): 62-66.