Grouping-Sorting-Optimized Model Predictive Control for Modular Multilevel Converter With Reduced Computational Load

Abstract

Featuring high efficiency, low harmonic distortion, high modularity and scalability, the modular multilevel converter (MMC) is particularly suitable for high voltage direct current transmission applications. As an advanced control strategy, model predictive control (MPC) has the advantage of direct modeling and fast dynamic response. It can simultaneously control multiple variables through an appropriate cost function. The conventional MPC can achieve an optimal control objective by evaluating all the candidate switching states for the MMC; however, with increasing number of submodules, there is an increasing number of candidate switching states that place an enormous burden on the control. In this paper, a grouping-sorting-optimized MPC (GSOMPC) strategy is proposed for the MMC with the number of submodules for each arm increases to hundreds. It divides all submodules of each arm into M groups, with each containing X submodules. By the implementation of the first level and second level optimized MPC between groups and submodules, respectively, the computational load of each phase decreases from $C_{2N}^N$ to $2X + M + 3(N = M \times X)$ . In addition, to reduce the strict requirements of control hardware for sorting and calculation, the proposed strategy is able to simultaneously control the submodule voltage, ac current, circulating current, and switching frequency. Applied to a 2.7-kV/60-kW MMC back-to-back dynamic test system, experimental results verify the feasibility and effectiveness of the proposed GSOMPC strategy.