TY - JOUR
T1 - Model Predictive Control of High-Power Modular Multilevel Converters - An Overview
AU - Dekka, Apparao
AU - Wu, Bin
AU - Yaramasu, Venkata
AU - Fuentes, Ricardo Lizana
AU - Zargari, Navid R.
N1 - Funding Information:
Manuscript received June 20, 2018; revised September 23, 2018; accepted October 30, 2018. Date of publication November 9, 2018; date of current version February 11, 2019. This work was supported by the Fondecyt Iniciación 2016 under Project 11160227. Recommended for publication by Associate Editor H. Bai. (Corresponding author: Apparao Dekka.) A. Dekka and B. Wu are with the Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada (e-mail: dapparao@ieee.org; bwu@ee.ryerson.ca).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - Model predictive control (MPC) has emerged as a promising approach to control a modular multilevel converter (MMC). With the help of a cost function, the control objectives of an MMC are achieved easily by using an MPC approach. However, the MPC has several technical challenges and issues including the need of accurate system models, computational complexity, and variable switching frequency operation and weighting factor selection, when it comes to the control of an MMC. In the past few years, several research studies are conducted to address some of the challenges and issues in an MPC and developed several model predictive algorithms for an MMC. In this paper, the importance of each challenge and its impact on the system performance is discussed. Also, the MMC mathematical models used in the implementation of MPC are presented. Furthermore, some of the popular MPC algorithms are discussed briefly, and their features and performance are highlighted through case studies. Finally, summary and future trends of MPC for an MMC are presented.
AB - Model predictive control (MPC) has emerged as a promising approach to control a modular multilevel converter (MMC). With the help of a cost function, the control objectives of an MMC are achieved easily by using an MPC approach. However, the MPC has several technical challenges and issues including the need of accurate system models, computational complexity, and variable switching frequency operation and weighting factor selection, when it comes to the control of an MMC. In the past few years, several research studies are conducted to address some of the challenges and issues in an MPC and developed several model predictive algorithms for an MMC. In this paper, the importance of each challenge and its impact on the system performance is discussed. Also, the MMC mathematical models used in the implementation of MPC are presented. Furthermore, some of the popular MPC algorithms are discussed briefly, and their features and performance are highlighted through case studies. Finally, summary and future trends of MPC for an MMC are presented.
KW - Capacitor voltage control
KW - circulating current (CC)
KW - common-mode voltage (CMV)
KW - dc-link current ripple
KW - model predictive control (MPC)
KW - modular multilevel converter (MMC)
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U2 - 10.1109/JESTPE.2018.2880137
DO - 10.1109/JESTPE.2018.2880137
M3 - Article
AN - SCOPUS:85056309060
SN - 2168-6777
VL - 7
SP - 168
EP - 183
JO - IEEE Journal of Emerging and Selected Topics in Power Electronics
JF - IEEE Journal of Emerging and Selected Topics in Power Electronics
IS - 1
M1 - 8529274
ER -