电源学报2024，Vol.22Issue(z1)：186-196,11.DOI:10.13234/j.issn.2095-2805.2024.S1.186
基于改进有限状态多步模型预测控制的MMC-APF技术研究
Research on MMC-APF Technology Based on Improved Finite-state Multistep Model Predictive Control
摘要
Abstract
The modular multilevel converter-based active power filter ( MMC-APF ) is one of the most effective topologies used to deal with the pollution problem brought by nonlinear loads to the grid.In this paper,an MMC-APF based on improved finite-state multistep model predictive control is proposed,which achieves the control of all harmonics only through the fundamental synchronous rotating coordinate system.First,the number of pre-inserted sub-modules ( SMs ) of upper and lower bridge arms is obtained by the current loop composite control of PI and repetitive control.On this basis,the multistep model prediction is performed for the AC-side current,and the optimal solution for the number of inserted SMs of bridges arms is obtained.The search range for finding the optimal level of the objective function is narrowed without designing the weighting factor.The total number of inserted SMs per phase of the bridge arm is[N-1,N+1],and the maximum output level on AC side can reach 2N+1,thus increasing the compensation accuracy for the MMC-APF AC-side current and improving the system's dynamic performance.Finally,an MMC-APF platform was built,and the simulation and experimental results were consistent with the theoretical analysis,which further validates the feasibility and effectiveness of the proposed research scheme.关键词
模块化多电平变换器/有源电力滤波器/非线性负载/PI加重复控制/改进有限状态多步模型预测控制Key words
Modular multilevel converter ( MMC )/active power filter ( APF )/nonlinear load/PI and repetitive control/improved finite-state multistep model predictive control分类
信息技术与安全科学引用本文复制引用
文翌铖,陈亦文,童筱涵,江加辉,监浩军..基于改进有限状态多步模型预测控制的MMC-APF技术研究[J].电源学报,2024,22(z1):186-196,11.基金项目
福建省自然科学基金资助项目(2018J01757) （2018J01757）
山东省自然科学基金资助项目(ZR2023ME082)This work is supported by Natural Science Foundation of Fujian Province under the grant 2018J01757 （ZR2023ME082）
Natural Science Foundation of Shandong Province under the grant ZR2023ME082 （）
