Model predictive control of brushless doubly fed twin stator induction machine: A model reduction approach

Wei, X; Zhu, J; Cheng, M; Yang, H; Ma, B

Model predictive control of brushless doubly fed twin stator induction machine: A model reduction approach

Wei, X Zhu, J

Cheng, M Yang, H

Ma, B

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Publication Type:: Conference Proceeding
Citation:: 2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017
Issue Date:: 2017-10-02

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Field	Value	Language
dc.contributor.author	Wei, X	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Cheng, M	en_US
dc.contributor.author	Yang, H https://orcid.org/0000-0002-4987-5794	en_US
dc.contributor.author	Ma, B	en_US
dc.date.issued	2017-10-02	en_US
dc.identifier.citation	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017, 2017	en_US
dc.identifier.isbn	9781538632468	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126232
dc.description.abstract	© 2017 IEEE. This, paper presents a model predictive control (MPC) scheme and its model reduction approach for the brushless doubly fed twin stator induction machine (BDFTSIM). Firstly, the state-space equations of BDFTSIM, in terms of (power winding) PW flux, (rotor winding) RW flux, and (control winding) CW flux, is derived for developing the complete power model which is explicitly used to predict the future behavior. However, the MPC based on the complete model is too complicated for real time application. Therefore, a relevant model reduction approach is then developed for simplifying the power model while maintaining the accuracy under various conditions. Finally, by comparing the simulation results of the MPC based on the reduced model and complete model respectively, the effectiveness of the proposed method is verified.	en_US
dc.relation.ispartof	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017	en_US
dc.relation.isbasedon	10.1109/ICEMS.2017.8056433	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Model predictive control of brushless doubly fed twin stator induction machine: A model reduction approach	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. This, paper presents a model predictive control (MPC) scheme and its model reduction approach for the brushless doubly fed twin stator induction machine (BDFTSIM). Firstly, the state-space equations of BDFTSIM, in terms of (power winding) PW flux, (rotor winding) RW flux, and (control winding) CW flux, is derived for developing the complete power model which is explicitly used to predict the future behavior. However, the MPC based on the complete model is too complicated for real time application. Therefore, a relevant model reduction approach is then developed for simplifying the power model while maintaining the accuracy under various conditions. Finally, by comparing the simulation results of the MPC based on the reduced model and complete model respectively, the effectiveness of the proposed method is verified.

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http://hdl.handle.net/10453/126232