TY - JOUR
T1 - Precise Voltage Regulation of Unbalanced Bipolar DC-Grid-Based Charging Stations
T2 - A New Multiobjective Predictive Control Approach
AU - Nguyen, Hoach The
AU - Al Hosani, Khalifa
AU - Al-Sumaiti, Ameena Saad
AU - Alsawalhi, Jamal Yousuf
AU - Al Jaafari, Khaled Ali
AU - Nguyen, Thanh Hai
AU - El Moursi, Mohamed Shawky
N1 - Funding Information:
This work was supported in part by Khalifa University, Abu Dhabi, UAE under Award KKJRC-2019-Trans2 and in part by ASPIRE, The Technology Program Management Pillar of Abu Dhabi's Advanced Technology Research Council, via the ASPIRE Virtual Research Institute Award. Recommended for publication by Associate Editor Z. Zhang.
Publisher Copyright:
© 2022 IEEE.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - This article proposes a new multiobjective predictive control approach and an optimized switching scheme to precisely regulate the dc-voltages in bipolar dc-grid-based charging stations working under critical conditions. An angular region is preselected to reduce the size of control sets to seven candidates within minimized angle error of 30°. Then, two subregions are located to further simplify finite sets without losing options for multiobjective optimization. Lastly, a hybrid switching scheme is proposed to improve current regulation working under a reduced sampling frequency. Delayed-signal-cancellation technique and instantaneous power theory are also employed to ensure a resilient operation under grid-voltage distortions. Consequently, the proposed control strategy can precisely balance dc-voltages, track voltage references, and regulate grid-injected currents against unbalanced dc-loads and ac-grid distortions. MATLAB/Simulink and an experimental prototype are utilized to investigate the comparative performances between the proposed approach and the recent predictive control methods. The simulation and experiments show significant improvements in dc-link voltages including reference tracking, dc-voltage balance, fast dynamic response, and harmonic reduction of ac-currents under a reduced sampling frequency.
AB - This article proposes a new multiobjective predictive control approach and an optimized switching scheme to precisely regulate the dc-voltages in bipolar dc-grid-based charging stations working under critical conditions. An angular region is preselected to reduce the size of control sets to seven candidates within minimized angle error of 30°. Then, two subregions are located to further simplify finite sets without losing options for multiobjective optimization. Lastly, a hybrid switching scheme is proposed to improve current regulation working under a reduced sampling frequency. Delayed-signal-cancellation technique and instantaneous power theory are also employed to ensure a resilient operation under grid-voltage distortions. Consequently, the proposed control strategy can precisely balance dc-voltages, track voltage references, and regulate grid-injected currents against unbalanced dc-loads and ac-grid distortions. MATLAB/Simulink and an experimental prototype are utilized to investigate the comparative performances between the proposed approach and the recent predictive control methods. The simulation and experiments show significant improvements in dc-link voltages including reference tracking, dc-voltage balance, fast dynamic response, and harmonic reduction of ac-currents under a reduced sampling frequency.
KW - Bipolar dc-grid
KW - converters
KW - distortion
KW - electric vehicles (EVs) charging station
KW - harmonics
KW - neural-point clamped (NPC) converter
KW - predictive control
UR - http://www.scopus.com/inward/record.url?scp=85147293677&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2023.3238054
DO - 10.1109/TPEL.2023.3238054
M3 - Article
AN - SCOPUS:85147293677
SN - 0885-8993
VL - 38
SP - 5858
EP - 5874
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 5
ER -