Dynamic Model Predictive Control of Grid-Forming Converters

DAI Yi; ZHENG Tao; YANG Chang; WANG Yanxia

doi:10.7652/xjtuxb202603018

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Dynamic Model Predictive Control of Grid-Forming Converters

更新时间：2026-03-09

- Dynamic Model Predictive Control of Grid-Forming Converters
- Journal of Xi'an Jiaotong University Vol. 60, Issue 3, Pages: 187-197(2026)
- 作者机构：
  
  西安交通大学电气工程学院，710049，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202603018
  CLC： TM464
- Received：15 June 2025，
  
  Published：10 March 2026
- 稿件说明：
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DAI Yi, ZHENG Tao, YANG Chang, et al. Dynamic Model Predictive Control of Grid-Forming Converters[J]. Journal of Xi'an Jiaotong University, 2026, 60(3): 187-197.
DOI：

DAI Yi, ZHENG Tao, YANG Chang, et al. Dynamic Model Predictive Control of Grid-Forming Converters[J]. Journal of Xi'an Jiaotong University, 2026, 60(3): 187-197. DOI： 10.7652/xjtuxb202603018.

摘要

为解决构网型变流器模型预测控制对滤波器参数高度依赖的问题，提出了一种基于系统动态稀疏回归的模型预测控制策略。该方法通过施加短时低幅值阶梯型扰动信号，激发系统动态响应并获取少量训练数据。基于非线性动力学稀疏辨识算法原理，将状态变量与控制输入组合构建备选函数池，并通过带正则化约束的稀疏回归优化求解系数矩阵，辨识出结构简洁、具有物理意义且具备良好泛化能力的状态空间模型。将该模型离散化后嵌入模型预测控制框架，以输出电压跟踪误差与控制输入能量消耗构建目标函数，结合物理边界约束，通过二次规划在每个控制周期内滚动求解最优控制序列。结果表明：在参数存在较大偏移时，所提策略的建模误差低于0.7%，能够在5ms内完成负载扰动响应，输出电压稳态误差控制在±0.1%以内，且计算效率显著优于传统方法。所提策略在建模精度、鲁棒性与实时性方面具有综合优势，展现出良好的工程可行性。

Abstract

To address the high dependence of model predictive control for grid-forming converters on filter parameters

a predictive control strategy based on dynamic sparse regression is proposed.The proposed method excites the system’s dynamic response by applying a short-term

low-amplitude step perturbation signal to acquire a small training dataset.Based on the Sparse Identification of Nonlinear Dynamics algorithm

a candidate function pool is constructed by combining state variables and control inputs.The coefficient matrix is then solved through sparse regression optimization with regularization constraints

thereby identifying a concise and physically meaningful state-space model with strong generalization capability.After being discretized

this model is embedded into the model predictive control framework.By formulating an objective function based on the output voltage tracking error and control input energy consumption

and incorporating physical boundary constraints

the optimal control sequence is iteratively solved over a rolling horizon within each control cycle via quadratic programming. The results show that under significant parameter deviations

the modeling error of the proposed strategy remains below 0.7%

while the load disturbance response is completed within 5 ms

the steady-state error of the output voltage is controlled within ±0.1%

and the computational efficiency is significantly higher than that of traditional methods.The proposed strategy demonstrates comprehensive advantages in modeling accuracy

robustness

and real-time performance

exhibiting strong potential for practical engineering applications.

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references

郭方洪，冯秀荣，杨淏，等.基于数据模型双驱动的新能源微电网分布鲁棒优化调度[J].电力系统自动化，2024，48(20)：36-47.

GUO Fanghong，FENG Xiurong，YANG Hao，et al. Dual-data-model-driven distributionally robust optimal scheduling of renewable energy microgrid[J].Automation of Electric Power Systems，2024，48(20)：36-47.

许诘翊，刘威，刘树，等.电力系统变流器构网控制技术的现状与发展趋势[J].电网技术，2022，46(9)：3586-3594.

XU Jieyi，LIU Wei，LIU Shu，et al. Current state and development trends of power system converter gridforming control technology[J].Power System Technology，2022，46(9)：3586-3594.

DRAGIˇCEVICˊ T, VAZQUEZ S, WHEELER P.Advanced control methods for power converters in DG systems and microgrids[J].IEEE Transactions on Industrial Electronics, 2021, 68(7)：5847-5862.

LI Hongfeng, SHAO Jianyu, LIU Zhengyu.Incremental model predictive current control for PMSM with online compensation for parameter mismatch[J].IEEE Transactions on Energy Conversion, 2023, 38(2)：1050-1059.

SIAMI M, KHABURI D A, ABBASZADEH A, et al. Robustness improvement of predictive current control using prediction error correction for permanentmagnet synchronous machines[J].IEEE Transactions on Industrial Electronics, 2016, 63(6)：3458-3466.

ZHANG Yongchang, LIU Xiang, LI Bingyu, et al. Robust predictive current control of PWM rectifier under unbalanced and distorted network[J].IET Power Electronics, 2021 , 14(4)：797-806.

LAMMOUCHI Z, LABIOD C, SRAIRI K, et al. Enhanced model-free predictive control for voltage source inverters using an adaptive observer[J].Revue Roumaine des Sciences Techniques-Série Électrotechnique EtÉnergétique, 2024, 69(3)：321-326.

HUANG Min, ZHANG Huiying, WANG Kangan, et al. Model-free predictive control based on RLS algorithm for grid-forming inverters with virtual synchronous generator[J].IEEE Access, 2025, 13：57921-57931 .

BRUNTON S L, PROCTOR J L, KUTZ J N.Sparse identification of nonlinear dynamics with control (SINDYc)[J].IFAC-PapersOnLine, 2016, 49 (18)：710-715.

FASEL U, KUTZ J N, BRUNTON B W, et al. Ensemble-SINDy：robust sparse model discovery in the low-data, high-noise limit, with active learning and control[J].Proceedings of the Royal Society：A Mathematical, Physical and Engineering Sciences, 2022, 478(2260)：20210904.

NANDAKUMAR A, LI Yan, ZHENG Honghao, et al. Data-driven modeling of microgrid transient dynamics through modularized sparse identification[J]. IEEE Transactions on Sustainable Energy, 2024, 15 (1)：109-122.

CAI Yaojie, WANG Xiaozhe, JOÓS G, et al. An online data-driven method to locate forced oscillation sources from power plants based on sparse identification of nonlinear dynamics (SINDy)[J].IEEE Transactions on Power Systems, 2023, 38(3)：2085-2099.

SHADAEI M, KHAZAEI J, MOAZENI F.Data-driven nonlinear model predictive control for power sharing of inverter-based resources[J].IEEE Transactions on Energy Conversion, 2024, 39(3)：2018-2031 .

SAEED KANDEZY R, JIANG J, WU Di.On SINDy approach to measure-based detection of nonlinear energy flows in power grids with high penetration inverterbased renewables[J].Energies, 2024, 17(3)：711.

YAZDANI A, IRAVANI R.Two-level, three-phase voltage-sourced converter[M]//YAZDANI A, IRAVANI R.Voltage-Sourced Converters in Power Systems：Modeling, Control, and Applications.Piscataway, NJ, USA：Wiley-IEEE Press, 2010：115-126.

LIU Tao, WANG Qingguo, HUANG H P.A tutorial review on process identification from step or relay feedback test[J].Journal of Process Control, 2013, 23(10)：1597-1623.

KATAYAMA T, KAWAUCHI H, PICCI G.Subspace identification of closed loop systems by the orthogonal decomposition method[J]. Automatica, 2005 , 41(5)：863-872.

MITTAL R, MIAO Zhixin.Analytical model of a grid-forming inverter[C]//2022 IEEE Power & Energy Society General Meeting (PESGM).Piscataway, NJ, USA：IEEE, 2022：1-5 .

MITRA A, CHOWDHURI S.Analysis of single phase PWM rectifier for different applications[J].Journal of the Institution of Engineers (India)：Series B, 2017, 98(2)：161-169.

李双，施建强.基于新型双环控制的LC型逆变器研究[J].上海交通大学学报，2022，56(9)：1139-1147.

LI Shuang，SHI Jianqiang.An LC inverter based on novel dual-loop control[J].Journal of Shanghai Jiao Tong University，2022，56(9)：1139-1147.

MOHAMED I S, ZAID S A, ABU-ELYAZEED M F, et al. Implementation of model predictive control for three-phase inverter with output LC filter on eZdsp F28335 Kit using HIL simulation[J].International Journal of Modelling Identification and Control, 2016, 25(4)：301-312.

ZHAO Tao, ZHANG Mingzhou, WANG Chunlin, et al. Model-free predictive current control of three-level grid-connected inverters with LCL filters based on Kalman filter[J].IEEE Access, 2023, 11：21631-21640.

RICHTER S, JONES C N, MORARI M.Computational complexity certification for real-time MPC with input constraints based on the fast gradient method[J]. IEEE Transactions on Automatic Control, 2012, 57(6)：1391-1403.

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