基于多变量校正的MMC快速有限集模型预测控制策略

有限集模型预测控制方法(finite control set model predictive control, FCS-MPC)因其能够实现多目标的控制，在模块化多电平变换器(modular multilevel converter, MMC)中得到广泛应用。随着子模块数量增加，模型预测控制方法计算量呈指数增长，面临计算复杂度高、权重因子难以整定等问题。为了解决上述问题，提出了一种基于多变量校正控制集的MMC模型预测控制策略(multi-variate adjusting set predictive control, MAS-MPC)。该策略基于输出电流与桥臂电压差对子模块投入控制集进行快速校正，通过评估两个成本函数得到最优开关矢量。此外，提出了一种基于分化中项的电容电压平衡方案，可以有效降低排序算法的复杂度。为了验证所提策略的有效性，使用Matlab/Simulink软件平台搭建了10电平的三相MMC系统，并与传统方案进行比较。所提方案在降低输出电流与环流的谐波含量的同时，大幅减少了系统的计算量，使得系统具有更快速的动态响应速度。

Finite control set model predictive control strategy for an MMC based on multi-variable fast adjusting

Finite control set model predictive control (FCS-MPC) is widely used in modular multilevel converters (MMC) because of its ability to achieve multi-objective control. As the number of submodules increases, model predictive control (MPC) faces exponential computational growth, high computational complexity, and difficulty in integrating the weighting factors. To solve the above problems, a multi-variate adjusting set model predictive control (MAS-MPC) method is proposed. This method performs a fast correction of the submodule control set by output current and arm voltage difference, and evaluates two cost functions to obtain the optimal switching vectors. In addition, a capacitor voltage balance algorithm based on differentiated medium term is proposed. This can effectively reduce the complexity of the sorting algorithm. To verify effectiveness, a three-phase MMC system with 10 voltage levels is built based on Matlab/Simulink, and compared with the traditional strategy. The proposed strategy drastically reduces the computational effort of the system while reducing the harmonic content of the output current and the circulating current. A system with faster dynamic response is achieved.