Scoltock, J., Riar, B. and Gladwin, D.T. orcid.org/0000-0001-7195-5435 (2018) A comparison of extrapolation techniques for Model Predictive Direct Current Control. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 21 Oct 2018 - 23 Oct 2019, Washington, DC, USA. IEEE , pp. 853-858. ISBN 978-1-5090-6684-1
Abstract
The use of output bounds and trajectory extension enables Model Predictive Direct Current Control (MPDCC) to achieve low current distortion for a given switching frequency, making it very well-suited to high-power applications where switching frequencies in the range of a few hundred Hz are typical. One of the main drawbacks of MPDCC, however, is the large computational requirement associated with model-based extension of the output current trajectories. In order to overcome this, approximate extension techniques must be used for practical implementation, making the analysis of such techniques very important. This paper addresses the existing gap in the literature by providing a comprehensive review of Linear (LE) and Quadratic (QE) extrapolation techniques for MPDCC. Using a MATLAB-based simulation of a grid-connected Neutral-Point-Clamped (NPC) converter as a case study, the relative performance of the different extrapolation techniques is evaluated using the trade-off between switching frequency and current distortion. The execution times of the various strategies are also provided using a TI TMS320F280049 microcontroller as an example control platform.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Feb 2019 10:19 |
Last Modified: | 07 Feb 2019 10:22 |
Published Version: | https://doi.org/10.1109/iecon.2018.8595145 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/iecon.2018.8595145 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142261 |