Due to discrete nature of power converters, finite control set model predictive control (FCS-MPC) is considered as an attractive choice for these systems. Since this control technique provides advantages like improved dynamic performance and inclusion of several control objectives in a single cost function, it is especially appropriate for topologies like modular multilevel converters (MMCs), where multiple control goals, that is, output and circulating currents control, and cells' capacitor voltage balancing should be satisfied. Despite these advantages, huge computational burden and weighting factor selection are two serious obstacles. In this work, a simple two-stage weighting factor selection scheme is suggested for FCS-MPC applied to MMCs. At the first stage, some offline tests are conducted for different weighting factors; and a pre-optimal value is selected, based on minimizing the total harmonic distortion (THD) of output current and root mean square (RMS) value of the circulating current. However, since optimal performance at different operating conditions is achieved by different weighting factors, the pre-optimal value will be updated at the second stage by a simple online algorithm. Moreover, a technique is suggested for effectively decreasing the computational burden. Finally, by conducting several simulation and experimental tests, the satisfactory performance of proposed controller is validated, compared to fixed weighting factor FCS-MPC.

中文翻译：

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模块化多电平变换器有限控制集模型预测控制的简单两级加权因子设计

由于功率转换器的离散特性，有限控制集模型预测控制 (FCS-MPC) 被认为是这些系统的有吸引力的选择。由于这种控制技术具有改进动态性能和在单个成本函数中包含多个控制目标等优点，因此它特别适用于模块化多电平转换器 (MMC) 等拓扑，其中多个控制目标，即输出和循环电流控制，并且应满足电池的电容器电压平衡。尽管有这些优势，巨大的计算负担和权重因子选择是两个严重的障碍。在这项工作中，为应用于 MMC 的 FCS-MPC 建议了一个简单的两阶段加权因子选择方案。第一阶段针对不同的权重因子进行一些离线测试；并且基于最小化输出电流的总谐波失真（THD）和循环电流的均方根（RMS）值来选择预优化值。但是，由于不同工况下的最佳性能是通过不同的加权因子实现的，因此在第二阶段将通过简单的在线算法更新预优化值。此外，还提出了一种有效减少计算负担的技术。最后，通过进行多次模拟和实验测试，与固定加权因子 FCS-MPC 相比，验证了所提出控制器的令人满意的性能。由于不同工况下的最优性能是通过不同的加权因子实现的，因此在第二阶段将通过简单的在线算法更新预最优值。此外，还提出了一种有效减少计算负担的技术。最后，通过进行多次模拟和实验测试，与固定加权因子 FCS-MPC 相比，验证了所提出控制器的令人满意的性能。由于不同工况下的最优性能是通过不同的加权因子实现的，因此在第二阶段将通过简单的在线算法更新预最优值。此外，还提出了一种有效减少计算负担的技术。最后，通过进行多次模拟和实验测试，与固定加权因子 FCS-MPC 相比，验证了所提出控制器的令人满意的性能。