Constrained Model Predictive Control for Three-Phase Four-Leg Grid-Tied Inverter

被引：0
|
作者：
Chen Q. [1 ]
Luo X. [1 ]
Zhang L. [1 ]
机构：
[1] School of Automation, Wuhan University of Technology, Wuhan, 430070, Hubei Province
来源：
Dianwang Jishu/Power System Technology | 2018年 / 42卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Constrained model predictive control; Field programming gate array (FPGA); Grid-tied inverter; Neural network optimization; Real-time control;
D O I：
10.13335/j.1000-3673.pst.2017.1810
中图分类号：
学科分类号：
摘要：
In this paper, constrained model predictive control (MPC) based on parallel neural network optimization was proposed to apply to grid-tied inverter and improve power quality. An accurate model of three-phase four-leg inverter was built. Then, the principle of constrained MPC is designed and constraints, including output and control variable constraints, were added. This method broke limits of predictive control with finite set and without constraints. Neural network optimization was used to solve online MPC optimization and accelerate single step optimization. Neural network optimization was performed in FPGA utilizing its parallel computing capability. Experimental results showed that output current error and total harmonics distortion (THD) became smaller and grid-tied power quality was improved. These results verified feasibility and effectiveness of the method. © 2018, Power System Technology Press. All right reserved.
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页码：555 / 563
页数：8
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