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A modulated model predictive control scheme for the brushless doubly-fed induction machine

Li, Xuan; Peng, Tao; Dan, Hanbing; Zhang, Guanguan; Tang, Weiyi; Wheeler, Patrick

Authors

Xuan Li

Tao Peng

Hanbing Dan

Guanguan Zhang

Weiyi Tang

Patrick Wheeler pat.wheeler@nottingham.ac.uk



Abstract

This paper proposes a modulated model predictive control (MMPC) algorithm for a brushless double-fed induction machine. The Brushless Doubly-Fed Induction Machine has some important advantages over alternative solutions for brushless machine applications. The proposed modulation technique achieves a fixed switching frequency, which gives good system performance. The paper examines the design and implementation of the modulation technique and simulation results verify the operation of the proposed modulation technique.

Start Date Oct 1, 2017
Publication Date Oct 2, 2017
Peer Reviewed Peer Reviewed
Book Title 2017 IEEE Energy Conversion Congress and Exposition (ECCE)
APA6 Citation Li, X., Peng, T., Dan, H., Zhang, G., Tang, W., & Wheeler, P. (2017). A modulated model predictive control scheme for the brushless doubly-fed induction machine. In 2017 IEEE Energy Conversion Congress and Exposition (ECCE)doi:10.1109/ECCE.2017.8095945
DOI https://doi.org/10.1109/ECCE.2017.8095945
Keywords Brushless doubly-fed induction machine; Modulated model predictive control
Publisher URL http://ieeexplore.ieee.org/document/8095945/
Related Public URLs http://www.ieee-ecce.org/2017/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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