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FCS-MPC Strategy for PV Grid-Connected Inverter Based on MLD Model

Xiaojuan Lu, Qingbo Zhang*

Lanzhou Jiaotong University, Lanzhou, 730070, China

* Corresponding Author: Qingbo Zhang. Email: email

Energy Engineering 2021, 118(6), 1729-1740. https://doi.org/10.32604/EE.2021.014938

Abstract

In the process of grid-connected photovoltaic power generation, there are high requirements for the quality of the power that the inverter breaks into the grid. In this work, to improve the power quality of the grid-connected inverter into the grid, and the output of the system can meet the grid-connected requirements more quickly and accurately, we exhibit an approach toward establishing a mixed logical dynamical (MLD) model where logic variables were introduced to switch dynamics of the single-phase photovoltaic inverters. Besides, based on the model, our recent efforts in studying the finite control set model predictive control (FCS-MPC) and devising the output current full state observer are exciting for several advantages, including effectively avoiding the problem of the mixed-integer quadratic programming (MIQP), lowering the THD value of the output current of the inverter circuit, improving the quality of the power that the inverter breaks into the grid, and realizing the current output and the grid voltage same frequency and phase to meet grid connection requirements. Finally, the effectiveness of the mentioned methods is verified by MATLAB/Simulink simulation.

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APA Style
Lu, X., Zhang, Q. (2021). FCS-MPC strategy for PV grid-connected inverter based on MLD model. Energy Engineering, 118(6), 1729-1740. https://doi.org/10.32604/EE.2021.014938
Vancouver Style
Lu X, Zhang Q. FCS-MPC strategy for PV grid-connected inverter based on MLD model. Energ Eng. 2021;118(6):1729-1740 https://doi.org/10.32604/EE.2021.014938
IEEE Style
X. Lu and Q. Zhang, "FCS-MPC Strategy for PV Grid-Connected Inverter Based on MLD Model," Energ. Eng., vol. 118, no. 6, pp. 1729-1740. 2021. https://doi.org/10.32604/EE.2021.014938



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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