TY - JOUR
T1 - Optimal Policy Gradient Based Type-2 Fuzzy Control for Multi-DC Terminal PEC Converter in 5G-Based Commercial Buildings
AU - Gheisarnejad, Meysam
AU - Fathollahi, Arman
AU - Sharifzadeh, Mohammad
AU - Laurendeau, Eric
AU - Andresen, Bjorn
AU - Al-Haddad, Kamal
PY - 2025
Y1 - 2025
N2 - Among various technologies being integrated into smart cities, electric vehicles (EVs) and advanced communication systems emerge as critical components. They require extensive distributed infrastructure for effective operation. Given their substantial energy footprint and widespread distribution, commercial buildings offer an ideal framework. They are well-suited for accommodating these technologies. This article introduces a dc power supply unit utilizing a nine-level packed E-cell (PEC9) converter, specifically designed for commercial buildings to efficiently support both EV charging stations and fifth generation (5G) communication infrastructure. A structured interval type-2 fuzzy proportional derivative plus integral (IT2F-PD + I) controller has been developed to stabilize this multi-dc terminal active power factor correction (APFC) multilevel rectifier. The primary objective of the proposed controller is to maintain the supplied dc voltages of PEC9 at their nominal values under unbalanced and variable dc loads. To optimize the input/output scaling factors of the IT2F-PD + I controller and intelligently regulate dc voltages, a deep deterministic policy gradient (DDPG) algorithm is employed. Two deep neural networks (DNNs), namely, actor and critic, are trained to determine the optimal policy by maximizing return signals received from PEC9 converter. Experimental implementation using DS1202 is conducted to evaluate the feasibility of the IT2F-PD + I controller-based DDPG approach in stabilizing the dc output voltages of the proposed multi-dc terminal multilevel-based power supply unit.
AB - Among various technologies being integrated into smart cities, electric vehicles (EVs) and advanced communication systems emerge as critical components. They require extensive distributed infrastructure for effective operation. Given their substantial energy footprint and widespread distribution, commercial buildings offer an ideal framework. They are well-suited for accommodating these technologies. This article introduces a dc power supply unit utilizing a nine-level packed E-cell (PEC9) converter, specifically designed for commercial buildings to efficiently support both EV charging stations and fifth generation (5G) communication infrastructure. A structured interval type-2 fuzzy proportional derivative plus integral (IT2F-PD + I) controller has been developed to stabilize this multi-dc terminal active power factor correction (APFC) multilevel rectifier. The primary objective of the proposed controller is to maintain the supplied dc voltages of PEC9 at their nominal values under unbalanced and variable dc loads. To optimize the input/output scaling factors of the IT2F-PD + I controller and intelligently regulate dc voltages, a deep deterministic policy gradient (DDPG) algorithm is employed. Two deep neural networks (DNNs), namely, actor and critic, are trained to determine the optimal policy by maximizing return signals received from PEC9 converter. Experimental implementation using DS1202 is conducted to evaluate the feasibility of the IT2F-PD + I controller-based DDPG approach in stabilizing the dc output voltages of the proposed multi-dc terminal multilevel-based power supply unit.
KW - Deep Neural Networks (DNNs)
KW - Deep Reinforcement Learning (DRL)
KW - EV Charger
KW - Interval Type-2 Fuzzy Proportional Derivative Plus Integral (IT2F-PD+I)
KW - Multi-DC Terminal PEC Converter
KW - Packed E-Cell
KW - Power Supplies
KW - interval type-2 fuzzy proportional derivative plus integral (IT2F-PD + I)
KW - multi-dc terminal packed E-cell (PEC) converter
KW - power supplies
KW - Deep neural networks (DNNs)
KW - electric vehicle (EV) charger
KW - deep reinforcement learning (DRL)
UR - https://www.scopus.com/pages/publications/105005941087
U2 - 10.1109/JESTPE.2025.3573043
DO - 10.1109/JESTPE.2025.3573043
M3 - Journal article
AN - SCOPUS:105005941087
SN - 2168-6777
VL - 13
SP - 5149
EP - 5161
JO - IEEE Journal of Emerging and Selected Topics in Power Electronics
JF - IEEE Journal of Emerging and Selected Topics in Power Electronics
IS - 4
ER -