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Open AccessArticle

Improved Direct Model Predictive Control for Grid-Connected Power Converters

Chair of Electrical Drive Systems and Power Electronics (EAL), Technische Universität München, 80333 München, Germany
Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt
Faculty of Engineering, University Andrés Bello, Santiago 8370146, Chile
Author to whom correspondence should be addressed.
Energies 2020, 13(10), 2597;
Received: 21 April 2020 / Revised: 13 May 2020 / Accepted: 15 May 2020 / Published: 20 May 2020
(This article belongs to the Section Electrical Power and Energy System)
This paper proposes a computationally efficient and robust direct model predictive control (DMPC) technique with enhanced steady-state performance for power converters tied to the electric utility. The discrete space vector modulation (DSVM) method is considered in the design of the suggested DMPC, where virtual voltage vectors (VVs) besides the real ones are utilized for improving the steady-state response of the proposed controller. Furthermore, for averting the high computational burden and making the proposed control technique simple, a deadbeat (DB) function is employed for calculating the reference VV based on the required reference current. Subsequently, a discrete-time integral term is combined with this DB function to enhance the robustness of the suggested DMPC technique against variations of the model parameters. Finally, the best virtual or real VV is chosen by a certain quality function, which will be applied to the power converter in the next sample. The suggested technique is verified by simulation results and its performance is compared with the classical DMPC and voltage-oriented control (VOC). View Full-Text
Keywords: predictive control; power converter; deadbeat; advanced control; robust control predictive control; power converter; deadbeat; advanced control; robust control
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Abdelrahem, M.; Rodríguez, J.; Kennel, R. Improved Direct Model Predictive Control for Grid-Connected Power Converters. Energies 2020, 13, 2597.

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