Open AccessSimplified model predictive current control based on fast vector selection method in a VIENNA rectifier
Author(s) -
Song Weizhang,
Yang Yang,
Jiao Zhu,
Xu Shaojie,
Dang Chaoliang,
Wheeler Pat
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/pel2.12395
Subject(s) - model predictive control , control theory (sociology) , rectifier (neural networks) , total harmonic distortion , current (fluid) , voltage , computer science , power factor , set (abstract data type) , selection (genetic algorithm) , power (physics) , control (management) , engineering , artificial intelligence , artificial neural network , physics , stochastic neural network , quantum mechanics , recurrent neural network , electrical engineering , programming language
Abstract This paper presents a simplified finite‐control‐set model predictive control (S‐FCS‐MPC) based on fast vector selection method in a three‐level VIENNA rectifier. This method features a high‐power factor, a low input current THD as a well‐controlled DC‐link voltage with fewer redundant vectors and lower computational load. Moreover, the converter with the proposed control technique exhibits a faster dynamic response in terms of input current and DC‐link voltage compared with conventional finite‐control‐set model predictive control (C‐FCS‐MPC). In addition, the average switching frequency can be effectively reduced due to fewer switching times in a subset sector using the proposed method, which means fewer switching losses. Finally, the operation principle of the proposed algorithm has been analysed and an execution time comparison between S‐FCS‐MPC and C‐FCS‐M has been undertaken. The effectiveness of the proposed control technique has been validated using both simulation and experimental results.