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Current Sensorless Model Predictive Control of Matrix Converter With Zero Common-Mode Voltage

Sarajian, Ali; Guan, Quanxue; Wheeler, Patrick; Khaburi, Davood Arab; Kennel, Ralph; Rodriquez, Jose

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Authors

Ali Sarajian

Quanxue Guan

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PATRICK WHEELER pat.wheeler@nottingham.ac.uk
Professor of Power Electronic Systems

Davood Arab Khaburi

Ralph Kennel

Jose Rodriquez



Abstract

To eliminate the common-mode voltage (CMV) for matrix converters, this paper proposes a current sensorless model predictive control with reduced calculation overhead. In contrast to other traditional CMV-reducing methods which use all permissible switching configurations, this method synthesizes the output voltage and the input current with only six rotating vectors that lead to zero CMV. The proposed technique does not need to predict future load currents and source currents for those six rotating vectors, which provides another advantage in term of computation efficiency. Additionally, all current sensors are removed by using a Luenberger state observer instead in the control loop for cost reduction. The effectiveness of the proposed method is evaluated through simulation in different operation conditions.

Citation

Sarajian, A., Guan, Q., Wheeler, P., Khaburi, D. A., Kennel, R., & Rodriquez, J. (2022). Current Sensorless Model Predictive Control of Matrix Converter With Zero Common-Mode Voltage. In 48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022). https://doi.org/10.1109/iecon49645.2022.9968351

Conference Name IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
Conference Location Brussels, Belgium
Start Date Oct 17, 2022
End Date Oct 20, 2022
Acceptance Date Jul 10, 2022
Online Publication Date Oct 17, 2022
Publication Date Oct 17, 2022
Deposit Date Sep 5, 2022
Publicly Available Date Oct 17, 2022
Publisher IEEE
Series Title Annual Conference of the IEEE Industrial Electronics Society
Series ISSN 1553-572X
Book Title 48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022)
ISBN 9781665480260
DOI https://doi.org/10.1109/iecon49645.2022.9968351
Public URL https://nottingham-repository.worktribe.com/output/10908696
Publisher URL https://ieeexplore.ieee.org/document/9968351
Related Public URLs https://iecon2022.org/

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