Open AccessModel predictive control with improved discrete space vector modulation for three‐level Vienna rectifier
Author(s) -
Zhu Wenjie,
Chen Changsong,
Duan Shanxu
Publication year - 2019
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/iet-pel.2019.0040
Subject(s) - ripple , control theory (sociology) , rectifier (neural networks) , model predictive control , computer science , trajectory , converters , set (abstract data type) , current (fluid) , voltage , control (management) , engineering , machine learning , artificial intelligence , physics , stochastic neural network , astronomy , recurrent neural network , artificial neural network , electrical engineering , programming language
This article proposes a model predictive control (MPC) with the discrete space vector modulation (DSVM) to reduce input current ripple of three‐level Vienna rectifier. The finite control set MPC (FCS‐MPC) applied in power converters has variable switching frequency and needs high sampling frequency for better input current quality. With the proposed approach, more feasible vectors can be used to improve the input current's performance with the DSVM using the virtual vectors. The DSVM‐MPC features fixed switching frequency and should implement large numbers of virtual vectors with the calculation burden. The improved DSVM‐MPC is proposed to optimise the MPC algorithm by selecting the adjacent feasible vectors according to the reference voltage vector trajectory. The effectiveness of the proposed method is verified by the experimental results in a Vienna rectifier platform, which shows that the proposed method possesses better input current performance compared with the FCS‐MPC method.