电力系统保护与控制2026，Vol.54Issue(6)：45-57,13.DOI:10.19783/j.cnki.pspc.250860
基于卡尔曼滤波-准谐振扩张状态观测器的MMC无模型预测控制策略
Model-free predictive control strategy of MMC based on a Kalman filtering-quasi-resonant extended state observer
摘要
Abstract
Traditional finite-control-set model predictive control is widely applied to complex nonlinear systems such as modular multilevel converters(MMC)due to its capability for multi-objective control.However,its performance deteriorates under parameter mismatch and sensor noise conditions.To address these issues,this paper proposes a model-free predictive control strategy for MMC based on a Kalman filtering-quasi-resonant extended state observer(KF-QRESO)to enhance system robustness against parameter mismatch and sampling disturbances.First,the discrete mathematical model of MMC under parameter mismatch is analyzed,and a composite KF-QRESO observer is constructed.The Kalman filter(KF)is used to suppress sampling noise,while the QRESO accurately estimates periodic AC state variables and compensates them within the KF state equations.Then,the composite observer reduces the parameter dependence of the control system,achieving precise estimation.The tracking ability and stability of the observer for periodic signals are also studied.Next,the composite observer is integrated with model-free predictive control to improve performance under parameter mismatch and sampling noise conditions.Finally,MATLAB/Simulink simulations and prototype experiments validate the method's effectiveness and correctness.关键词
无模型预测控制/参数失配/卡尔曼滤波/扩张状态观测器/模块化多电平换流器Key words
model-free predictive control/parameter mismatch/Kalman filtering/extended state observer/modular multilevel converter引用本文复制引用
梁备,马文忠,王玉生,孟令彤,宋曙光,郑绍通..基于卡尔曼滤波-准谐振扩张状态观测器的MMC无模型预测控制策略[J].电力系统保护与控制,2026,54(6):45-57,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.52277208). 国家自然科学基金项目资助(52277208) （No.52277208）
中国石油重大科技攻关专项资助(2023ZZ31YJ01,2023ZZ31YJ03) （2023ZZ31YJ01,2023ZZ31YJ03）
