Computationally efficient data-driven model predictive control for modular multilevel converters

Abstract

The application of model predictive control (MPC) for the control of modular multilevel converters (MMCs) is widely explored because it offers flexibility in integrating multiobjective control and delivers superior dynamic response. Nonetheless, the increase in computational complexity due to the rise in the number of submodules (SMs) is one of the major drawbacks of this technique. This paper presents a finite control set model predictive control (FCS-MPC) that significantly reduces the computational complexity by employing sparse identification of non-linear systems (SINDy) to obtain a simplified linear model for the MMC. The SINDy model reduces the complexity of performing the prediction step by integrating input terms into the dynamics of load current and circulating current. This simplifies the implementation compared to the conventional FCS-MPC approaches by eliminating the need to evaluate the voltage dynamics. The computational burden is further reduced while maintaining $2N+1$ voltage levels at the output by restricting the number of combinations for the inserted SMs to only $\left(\frac{N}{3}+1\right)$ instead of ${\left(N+1\right)}^2$. A detailed comparison between the proposed technique and the existing strategies demonstrates that the proposed technique offers a more computationally efficient solution for implementing FCS-MPC on MMCs, while improving the circulating current suppression due to more accurate predictions. Simulation and experimental results are presented to validate the performance of the proposed approach.