Model Predictive PWM for Hybrid Active Power Filter

Model Predictive PWM for Hybrid Active Power Filter

Yudong Li Shaotong Du Tianyu Zhang Xingguo Tan 

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, Henan Province, China

School of Mechanical and Electrical Engineering, Henan Vocational College of Industry and Information Technology, Jiaozuo 454003, Henan Province, China

Corresponding Author Email: 
lyd@hpu.edu.cn; gdj4321@163.com; zyj@hpu.edu.cn; tanxg@hpu.edu.cn
Page: 
98-116
|
DOI: 
https://doi.org/10.18280/mmc_a.900108
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

Model predictive pulse-width modulation (MP-PWM) is applied to hybrid active power filter (HAPF) in this paper. The configuration, control, carried-based PWM (CPWM) and filtering performance of HAPF are described in this paper. To realize the MP-PWM, the discrete-time model of the inverter is presented and a cost function is built. The flow chart of realizing the MP-PWM is presented. The PWM is assumed as a sampling-hold circuit with an equivalent control period to compare MP-PWM and CPWM. To compare the equivalent control period of PWM, an inverter simulation with R-L-C load is carried out. The inverter simulation results verify that the equivalent control period of MP-PWM is smaller than the CPWM, while the average switching frequency is lower than the CPWM. The simulation results of HAPF with MP-PWM and CPWM show that the filter performance of HAPF with the MP-PWM is better. An experiment study of HAPF with MP-PWM has been performed to validate the MP-PWM for HAPF.

Keywords: 

harmonic suppression, hybrid active power filter, model predictive, pulse width modulation (PWM)

1. Introduction
2. System Description
3. Filtering Characteristic Analysis
4. Model Predictive PWM Algorithm
5. Simulation Results
6. Experiment Results
7. Conclusions
Acknowledgments

This work is supported by Open Project of Key Laboratory of Control Engineering of Henan Province (KG2016-05) and Key scientific research project of Henan higher education(Grant No.16A470009).

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