Open AccessDynamic event‐triggered model predictive control of Vienna rectifiers in DC charging system
Author(s) -
Du Yudong,
Zhang Aimin,
Huang Jingjing,
Zhou Yunhong,
Zhang Hang
Publication year - 2022
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/pel2.12316
Subject(s) - correctness , model predictive control , control theory (sociology) , computer science , state (computer science) , event (particle physics) , control (management) , system dynamics , engineering , control engineering , algorithm , artificial intelligence , physics , quantum mechanics
Here, a dynamic event‐triggered model predictive control (DETMPC) of Vienna rectifiers in the DC charging system is proposed to solve the excessive calculation burden of the conventional MPC method while maintaining the system performance. The status of the system is constantly updated, and the next model predictive control calculation is performed only when the system state meets the event‐triggered condition. Otherwise, the original switch state signal is maintained, which can greatly reduce the computational burden. A dynamic variable is introduced into the trigger condition to holdover the steady‐state performance as the system state changes. An experimental comparative analysis is carried out to verify the correctness and effectiveness of the proposed DETMPC method. The results demonstrate that the proposed method can effectively reduce calculation burden and switching loss while maintaining satisfactory system performance at the same time.