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

PROJECT TITLE :

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

ABSTRACT:

That includes high potency, low harmonic distortion, high modularity and scalability, the modular multilevel converter (MMC) is notably suitable for high voltage direct current transmission applications. As an advanced control strategy, model predictive management (MPC) has the advantage of direct modeling and fast dynamic response. It will simultaneously control multiple variables through an acceptable value perform. The traditional MPC can achieve an optimal control objective by evaluating all the candidate switching states for the MMC; but, with increasing number of submodules, there's an increasing variety of candidate switching states that place an huge burden on the control. In this paper, a grouping-sorting-optimized MPC (GSOMPC) strategy is proposed for the MMC with the quantity of submodules for every arm will increase to lots. It divides all submodules of every arm into M teams, with every containing X submodules. By the implementation of the first level and second level optimized MPC between teams and submodules, respectively, the computational load of every phase decreases from $C_2N^N$ to $2X + M + three(N = M times X)$. Additionally, to reduce the strict requirements of control hardware for sorting and calculation, the proposed strategy is ready to simultaneously control the submodule voltage, ac current, circulating current, and switching frequency. Applied to a 2.seven-kV/sixty-kW MMC back-to-back dynamic check system, experimental results verify the feasibility and effectiveness of the proposed GSOMPC strategy.

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