构网型变流器动态模型预测控制

代毅; 郑涛; 杨畅; 汪艳霞

doi:10.7652/xjtuxb202603018

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构网型变流器动态模型预测控制

更新时间：2026-03-09

- 构网型变流器动态模型预测控制
- Dynamic Model Predictive Control of Grid-Forming Converters
- 西安交通大学学报 2026年60卷第3期页码：187-197
- 作者机构：
  
  西安交通大学电气工程学院，710049，西安
- 作者简介：
  
  作者简介：代毅（2000—），男，硕士生；
  郑涛（通信作者），男，副教授，博士生导师。
- 基金信息：
  
  国家自然科学基金企业创新发展联合基金重点支持项目(U23B20112);陕西省自然科学基金资助项目。(2024JC-YBMS-420)
- DOI：10.7652/xjtuxb202603018
  中图分类号： TM464
- 收稿：2025-06-15，
  
  纸质出版：2026-03-10
- 稿件说明：
移动端阅览
代毅, 郑涛, 杨畅, 等. 构网型变流器动态模型预测控制[J]. 西安交通大学学报, 2026,60(3):187-197.

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.
代毅, 郑涛, 杨畅, 等. 构网型变流器动态模型预测控制[J]. 西安交通大学学报, 2026,60(3):187-197. DOI： 10.7652/xjtuxb202603018.

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.

关键词

Keywords

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