A Low-Computation Indirect Model Predictive Control for Modular Multilevel Converters

The modular multilevel converter (MMC) has become a promising topology for high-voltage direct current (HVDC) transmission systems. To control a MMC system properly, the ac-side current, circulating current and submodule (SM) capacitor voltage are taken into consideration. This paper proposes a low-computation indirect model predictive control (IMPC) strategy that takes advantages of the conventional MPC and has no weighting factors. The cost function and duty cycle are introduced to minimize the tracking error of the ac-side current and to eliminate the circulating current. An optimized merge sort (OMS) algorithm is applied to keep the SM capacitor voltages balanced. The proposed IMPC strategy effectively reduces the controller complexity and computational burden. In this paper, a discrete-time mathematical model of a MMC system is developed and the duty ratio of switching state is designed. In addition, a simulation of an eleven-level MMC system based on MATLAB/Simulink and a five-level experimental setup are built to evaluate the feasibility and performance of the proposed low-computation IMPC strategy.

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Cite this article

[IEEE Style]

W. Ma, P. Sun, G. Zhou, G. Sailijiang, Z. Zhang, Y. Liu, "A Low-Computation Indirect Model Predictive Control for Modular Multilevel Converters," Journal of Power Electronics, vol. 19, no. 2, pp. 529-539, 2019. DOI: 10.6113/JPE.2019.19.2.529.

[ACM Style]

Wenzhong Ma, Peng Sun, Guanyu Zhou, Gulipali Sailijiang, Ziang Zhang, and Yong Liu. 2019. A Low-Computation Indirect Model Predictive Control for Modular Multilevel Converters. Journal of Power Electronics, 19, 2, (2019), 529-539. DOI: 10.6113/JPE.2019.19.2.529.