NDLI: Fully FPGA based direct model predictive power control for grid-tied AFEs with improved performance

Content Provider	IEEE Xplore Digital Library
Author	Zhenbin Zhang Hui Fang Kennel, R.
Copyright Year	2015
Description	Author affiliation: Inst. of Electr. Drive Syst. & Power Electron., Tech. Univ. Munchen, Munich, Germany (Zhenbin Zhang; Hui Fang; Kennel, R.)
Abstract	Single-switching-vector-per-sampling-interval character of the classical Direct Model Predictive Power Control (DMPPC) technique leads to big ripples of the control variables. Therefore, with a similar sampling frequency, its steady state performance is not satisfying. This work proposed a Revised Direct Model Predictive Power Control (R-MPDPC) concept for grid-tied AFEs. Instead of one single switching vector, two adjacent vectors are utilized to minimize a modified cost function based on time-optimal concept, which yields a synthesized equivalent voltage vector with an arbitrary phase. Therefore, more freedoms and precise tracking possibilities are included into the predictive controller design. The proposed control scheme is realized with a fully FPGA based solution on a lab-constructed AFE platform. Its performances are compared with the classical DMPPC with all experimental data. The results confirm that the control performances are greatly improved using the proposed R-DMPPC scheme.
Sponsorship	IEEE
Starting Page	003881
Ending Page	003886
File Size	1225777
Page Count	6
File Format	PDF
e-ISBN	9781479917624
DOI	10.1109/IECON.2015.7392705
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power control Switches Cost function Predictive models Prediction algorithms Field programmable gate arrays Voltage control FPGA Digital Control Grid-Tied Active Front End Direct Model Predictive Control Performance-Enhanced Direct Power Control Time-Optimal Control
Content Type	Text
Resource Type	Article

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