Cascaded Open-Circuit Fault Ride-Through of Modular Multilevel Converters with Model Predictive Control

2018 ◽  
Author(s):  
Dehong Zhou ◽  
Pengfei Tu ◽  
Huan Qiu ◽  
Yi Tang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Open Circuit ◽  
Multilevel Converters ◽  
Fault Ride Through ◽  
Modular Multilevel Converters

scholarly journals Model Predictive Control of Modular Multilevel Converters using Quadratic Programming

2020 ◽  
pp. 1-1
Author(s):  
Jiapeng Yin ◽  
Jose I. Leon ◽  
Marcelo A Perez ◽  
Leopoldo Garcia Franquelo ◽  
Abraham Marquez Alcaide ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Quadratic Programming ◽  
Predictive Control ◽  
Multilevel Converters ◽  
Modular Multilevel Converters

Model Predictive Control of High-Power Modular Multilevel Converters—An Overview

2019 ◽  
Vol 7 (1) ◽  
pp. 168-183 ◽  
Author(s):  
Apparao Dekka ◽  
Bin Wu ◽  
Venkata Yaramasu ◽  
Ricardo Lizana Fuentes ◽  
Navid R. Zargari
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Power ◽  
Multilevel Converters ◽  
Modular Multilevel Converters

scholarly journals Disturbance-Observer-Based Model Predictive Control for Battery Energy Storage System Modular Multilevel Converters

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2285 ◽  
Author(s):  
Yantao Liao ◽  
Jun You ◽  
Jun Yang ◽  
Zuo Wang ◽  
Long Jin
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Current Control ◽  
Multilevel Converters ◽  
Battery Energy Storage ◽  
Circulating Current ◽  
Modular Multilevel Converters ◽  
Ac Current ◽  
Battery Energy

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters

2020 ◽  
pp. 1-1
Author(s):  
Songda Wang ◽  
Tomislav Dragicevic ◽  
Gustavo Figueiredo Gontijo ◽  
Sanjay K. Chaudhary ◽  
Remus Teodorescu
Keyword(s):  
Machine Learning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Multilevel Converters ◽  
Modular Multilevel Converters

scholarly journals A Reverse Model Predictive Control Strategy for a Modular Multilevel Converter

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 297 ◽  
Author(s):  
Weide Guan ◽  
Shoudao Huang ◽  
Derong Luo ◽  
Fei Rong
Keyword(s):  
Computational Complexity ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Performance ◽  
Multilevel Converter ◽  
Multilevel Converters ◽  
Computational Burden ◽  
Modular Multilevel Converters ◽  
The Cost ◽  
Fast Dynamic

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.

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