TY - JOUR
T1 - Model Predictive Control of a Three-Phase Two-Level Four-Leg Grid-Connected Converter Based on Sphere Decoding Method
AU - Long, Bo
AU - Cao, Tianxu
AU - Fang, Wenting
AU - Chong, Kil To
AU - Guerrero, Josep M.
N1 - Funding Information:
Manuscript received December 31, 2019; revised April 16, 2020; accepted June 28, 2020. Date of publication July 2, 2020; date of current version September 22, 2020. This work was supported in part by the Fundamental Research Funds for the Central Universities of China (ZYGX2019J033), in part by the National Natural Science Foundation of China under Grant 51975453, in part by Key R&D Plan of Science and Technology Department of Sichuan Province (20ZDYF2816), in part by the State Key Laboratory of Control and Simulation of Power System Generation Equipment, Tsinghua University, China (SKLD20M11), and in part by The VELUX FOUNDATIONS under the VILLUM Investigator Grant Center for Research on Microgrids under Award No. 25920. Recommended for publication by Associate Editor J. Rodriguez. (Corresponding author: Bo Long.) Bo Long, Tianxu Cao, and Wenting Fang are with the School of Mechanical and Electrical Engineering, Institute of Electric Vehicle Driving System and Safety Technology, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: longbouestc1980@126.com; 1011901845@qq.com; 2494176858@qq.com).
Publisher Copyright:
© 1986-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/2
Y1 - 2021/2
N2 - To achieve optimal control of four-leg grid-connected converters in terms of switching frequency and current tracking, the finite-control-set model-predictive-control method (FCS-MPC) can be applied. In this method, the cost function (CF) considers tracking control of grid current, filter capacitance voltage, and converter-side current as well as switching frequency before allocating different weights in its calculations. Thus, multiobjective optimization is achieved by trying to find the optimal switching sequence that minimizes the CF. However, as the horizon length is increased, the solution search enlarges exponentially, soon requiring an exhaustive search through each of many candidates. To alleviate this computational burden, this article presents an FCS-MPC method that is based on a new node-comparison sphere decoding method (NC-SDM), thereby reformulating the CF minimization problem into an integral-least-squares problem. The proposed NC-SDM reduces the computational burden associated with longer horizons by excluding as many suboptimal solutions from the candidates as possible. It does this by continuously comparing the length of two paths corresponding to each node of the search tree, and always taking the branch with shorter length. The final length of the total path is set as the initial radius after superposing all the path lengths. As a result, the initial radius estimation is much smaller than that in the Babai method and the computational cost is further reduced. Finally, simulation and experimental results validate the feasibility and suitability of the proposed method.
AB - To achieve optimal control of four-leg grid-connected converters in terms of switching frequency and current tracking, the finite-control-set model-predictive-control method (FCS-MPC) can be applied. In this method, the cost function (CF) considers tracking control of grid current, filter capacitance voltage, and converter-side current as well as switching frequency before allocating different weights in its calculations. Thus, multiobjective optimization is achieved by trying to find the optimal switching sequence that minimizes the CF. However, as the horizon length is increased, the solution search enlarges exponentially, soon requiring an exhaustive search through each of many candidates. To alleviate this computational burden, this article presents an FCS-MPC method that is based on a new node-comparison sphere decoding method (NC-SDM), thereby reformulating the CF minimization problem into an integral-least-squares problem. The proposed NC-SDM reduces the computational burden associated with longer horizons by excluding as many suboptimal solutions from the candidates as possible. It does this by continuously comparing the length of two paths corresponding to each node of the search tree, and always taking the branch with shorter length. The final length of the total path is set as the initial radius after superposing all the path lengths. As a result, the initial radius estimation is much smaller than that in the Babai method and the computational cost is further reduced. Finally, simulation and experimental results validate the feasibility and suitability of the proposed method.
KW - Cost function (CF)
KW - finite-control-set (FCS) model predictive control
KW - node comparison method
KW - three-phase four-leg grid-connected converter (GCC)
UR - http://www.scopus.com/inward/record.url?scp=85092575246&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2020.3006432
DO - 10.1109/TPEL.2020.3006432
M3 - Journal article
AN - SCOPUS:85092575246
SN - 0885-8993
VL - 36
SP - 2283
EP - 2297
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 2
M1 - 9132685
ER -