A Reverse Model Predictive Control Strategy for a Modular Multilevel Converter

Weide Guan, Shoudao Huang, Derong Luo and Fei Rong
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Weide Guan: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Shoudao Huang: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Derong Luo: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Fei Rong: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Energies, 2019, vol. 12, issue 2, 1-15

Abstract: In recent years, modular multilevel converters (MMCs) have developed rapidly, and are widely used in medium and high voltage applications. Model predictive control (MPC) has attracted wide attention recently, and its advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. The main technical challenge of the conventional MPC for MMC is the reduction of computational complexity of the cost function without the reduction of control performance of the system. Some modified MPC scan decrease the computational complexity by evaluating the number of on-state sub-modules (SMs) rather than the number of switching states. However, the computational complexity is still too high for an MMC with a huge number of SMs. A reverse MPC (R-MPC) strategy for MMC was proposed in this paper to further reduce the computational burden by calculating the number of inserted SMs directly, based on the reverse prediction of arm voltages. Thus, the computational burden was independent of the number of SMs in the arm. The control performance of the proposed R-MPC strategy was validated by Matlab/Simulink software and a down-scaled experimental prototype.

Keywords: model predictive control (MPC); computational burden; reverse prediction; modular multilevel converter (MMC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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