Constrained Model Predictive Control for a Three-Phase PWM Rectifier

被引：0

作者：

Qian, Li ^{[1
]}

Zhang, Liyan ^{[1
]}

Chen, Qihong ^{[1
]}

Quan, Shuhai ^{[1
]}

机构：

[1] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Hubei, Peoples R China

来源：

2017 CHINESE AUTOMATION CONGRESS (CAC) | 2017年

关键词：

Three-phase PWM rectifier; constrained model predictive control; unity power factor;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, constrained model predictive control (MPC) based on parallel neural network optimization is proposed to apply to pulse width modulation (PWM) rectifier and improve power quality. An decoupled model of three-phase rectifier in abc coordinates is built. Then, the constrained MPC method is proposed. This method breaks the limits of predictive control with finite set and without constraints. Neural network optimization is used to solve online optimization of MPC and accelerate single step optimization. A approach is proposed to guarantee unity power factor and also provides a regulated output dc-voltage with fast dynamics response against sudden changes in the load. The simulation results demonstrate that the output dc-voltage error and the total harmonics distortion (THD) of the input current are small and the unity power factor rectifier performs very well.

引用

页码：6745 / 6749

页数：5

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