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.

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 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.

scholarly journals Hybrid Modulated Model Predictive Control in a Modular Multilevel Converter for Multi-Terminal Direct Current Systems

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1861 ◽  
Author(s):  
Zhi Wu ◽  
Jiawei Chu ◽  
Wei Gu ◽  
Qiang Huang ◽  
Liang Chen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Direct Current ◽  
Control Strategy ◽  
Power Balance ◽  
Modular Multilevel Converter ◽  
Multilevel Converter ◽  
Dc Voltage ◽  
Passive Networks ◽  
Ac Systems

In this paper a hybrid modulated model predictive control (HM2PC) strategy for modular-multilevel-converter (MMC) multi-terminal direct current (MTDC) systems is proposed for supplying power to passive networks or weak AC systems, with the control objectives of maintaining the DC voltage, voltage stability and power balance of the proposed system. The proposed strategy preserves the desired characteristics of conventional model predictive control method based on finite control set (FCS-MPC) methods, but deals with high switching frequency, circulating current and steady-state error in a superior way by introducing the calculation of the optimal output voltage level in each bridge arm and the specific duty cycle in each Sub-Module (SM), both of which are well-suited for the control of the MMC system. In addition, an improved multi-point DC voltage control strategy based on active power balanced control is proposed for an MMC-MTDC system supplying power to passive networks or weak AC systems, with the control objective of coordinating the power balance between different stations. An MMC-HVDC simulation model including four stations has been established on MATLAB/Simulink (r2014b MathWorks, Natick, MA, USA). Simulations were performed to validate the feasibility of the proposed control strategy under both steady and transient states. The simulation results prove that the strategy can suppress oscillations in the MMC-MTDC system caused by AC side faults, and that the system can continue functioning if any one of the converters are tripped from the MMC-MTDC network.

scholarly journals Current Prediction-Based Three-Vector Voltage Optimization System for the Induction Motor with Current Static Error Correction

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Haixia ◽  
Lin Jican
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Control Method ◽  
Tracking Error ◽  
Current Control ◽  
Static Error ◽  
Control Stability ◽  
Steady State Performance

In the present study, the current control method of the model predictive control is applied to the field-oriented control induction motor. The augmentation model of the motor is initially established based on the stator current equation, which performs the current predictive control and formulates the new cost function by means of tracking error. Then, the influence of parameter error on the current control stability in the prediction model is analysed, and the current static error is corrected according to the correlation between the input and feedback. Finally, a simple and effective three-vector control strategy is proposed. Moreover, three adjacent basic voltage vectors are utilized, and then six candidate voltage vectors are synthesized in each sector to replace eight basic voltage vectors in the conventional model predictive control (MPC). The obtained results show that synthesized vectors, which have arbitrary amplitude and direction, significantly expand the coverage of the system’s control set, reduce the torque and flux pulsation in the conventional MPC, and improve the steady-state performance of the system. Finally, the dSPACE platform is employed to validate the performed experiment. It is concluded that the proposed method can reduce the torque and flux pulse, perform the induction motor current control, and improve the steady-state performance of the system.

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