Improved Model Predictive Control Method for Modular Multilevel Converter (MMC) based on Insertion Indexes

2018 ◽  
Author(s):  
Minh Hoang Nguyen ◽  
Sangshin Kwak ◽  
Jeihoon Baek
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Modular Multilevel Converter ◽  
Multilevel Converter ◽  
Improved Model

scholarly journals Circulating Current Suppression Strategy of Modular Multilevel Converter Based on Model Predictive Control

2021 ◽  
Vol 261 ◽  
pp. 01035
Author(s):  
kang Liu ◽  
Guige Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Control Method ◽  
Current Control ◽  
Switching Frequency ◽  
Modular Multilevel Converter ◽  
Multilevel Converter ◽  
Modulation Wave ◽  
Circulating Current

Modular Multilevel Converter (MMC) has the characteristics of high voltage level and low switching frequency. The traditional modular multilevel converter circulating current control strategy mostly adopts the PI control principle, and the parameter setting is complicated and the accuracy is not high, and the control process is more difficult. Model predictive control strategy is the optimal control method based on the model in the existing time domain. This paper proposes a Model Predictive Control (MPC) method based on carrier phase-shifted pulse width modulation (PSC-PWM) to suppress the circulating current, and output the optimal modulation wave through model prediction. Compared with the traditional control strategy, this strategy is simple to implement, does not require complex tuning calculations, and combines with the traditional capacitor voltage equalization strategy to obtain the output modulation wave. A 7-level MMC simulation control system is built in MATLAB / SIMLINK to verify the theory, comparing with existing control methods, it can be concluded that the proposed method has high calculation efficiency, good control accuracy and strong robustness.

scholarly journals Improved Indirect Model Predictive Control for Enhancing Dynamic Performance of Modular Multilevel Converter

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1405
Author(s):  
Minh Hoang Nguyen ◽  
Sangshin Kwak
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Transient State ◽  
Computational Time ◽  
Modular Multilevel Converter ◽  
Multilevel Converter ◽  
Computational Burden

Model predictive control has become a tremendously popular control method for power converters, notably a modular multilevel converter, owing to the ability to control various objectives at once with a particular cost function and prominent dynamic performance. However, the high number of submodules in cascaded control means that the model predictive control for the modular multilevel converter suffers from a computational burden. Several approaches focused on reducing the computational burden based on limiting the number of possible switching states (possible choices) to be evaluated at each sampling instant. The dynamic performance of the modular multilevel converter is degraded in a transient state, despite the reduced computational burden. This paper presents an improved indirect model predictive control method to reduce the computational burden and enhance the dynamic performance. The proposed approach considers the steady-state and transient state individually and applies a different range of choices for each specific case. The range of choices during the steady-state is limited in order to reduce the computational burden without deteriorating the output quality, whereas the number of choices will be increased during the transient state to guarantee dynamic performance. The results that were obtained by implementing an experiment on a laboratory setup of a single-phase modular multilevel converter are presented in order to verify the proposed approach’s effectiveness. From the experimental setup, the computational time in the proposed approach was reduced by about 75% when compared with the conventional indirect model predictive control, whereas keeping fast dynamic performance.

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