Modulated Model Predictive Control for Modular Multilevel Converters With Easy Implementation and Enhanced Steady-State Performance
Jinyu Wang, Xiong Liu, Qian Xiao, Dehong Zhou, Huan Qiu, Yi Tang
Abstract
Model predictive control (MPC) is widely employed in voltage-source converters due to its fast-dynamic response, straightforward realization, and flexible inclusion of multiobjective regulation. However, the MPC has several technical challenges when it comes to the control of modular multilevel converters, such as computational complexity, variable switching frequency, poor steady-state performance as well as tedious weighting factor selection. To solve these problems, this article proposes a new modulated MPC method, which can directly acquire the optimal modulation references at every sampling period based on the duty cycles of two fixed voltage levels. The optimal modulation references are then fed into the modulation stage to generate gate signals. Therefore, the proposed method is easy to implement with a very low calculation burden, which is independent of the number of submodules (SMs). The switching state or voltage-level evaluation, cost function calculation, and weighting factor selection are eliminated as well. Because of the inherent circulating current suppression capability without limitations and modulation stage, this method has a satisfactory steady-state performance and evenly distributed switching loss for every SM. The effectiveness and performance of the proposed method are verified by experimental results.