Grid voltage sensorless model-based predictive power control of PWM rectifiers based on sliding mode virtual flux observer

Liang, J; Wang, H; Yan, Z

Grid voltage sensorless model-based predictive power control of PWM rectifiers based on sliding mode virtual flux observer

Liang, J

Wang, H Yan, Z

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 24007 - 24016
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Liang, J https://orcid.org/0000-0001-8549-0261	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Yan, Z	en_US
dc.date.accessioned	2020-03-30T04:40:48Z
dc.date.available	2020-03-30T04:40:48Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 24007 - 24016	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139618
dc.description.abstract	© 2013 IEEE. In this paper, a grid voltage sensorless model predictive control is proposed based on a sliding mode virtual flux observer (SMVFO). The proposed SMVFO shows good inherent filtering capacity, and thus there is no high-frequency chattering problem. In addition, the proposed SMVFO is designed based on the closed-loop current estimation. Not only is DC-drift issue solved but also dynamic response is enhanced when compared with the prior open-loop virtual flux observer. To verify the effectiveness of the presented SMVFO, it is further integrated into finite control set-model predictive control (FCS-MPC) for pulse width modulator (PWM) rectifiers. The whole control algorithm features simplicity and improved cost-effectiveness due to the absence of modulator and grid voltage sensors. As the SMVFO can predict current at the next sampling instant while estimating virtual flux accurately, the proposed SMVFO assisted FCS-MPC is comparable to its counterpart using measured grid voltage. The simulation and experimental tests were carried out on a two-level voltage source PWM rectifier to validate the effectiveness of the proposed method.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2898687	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Grid voltage sensorless model-based predictive power control of PWM rectifiers based on sliding mode virtual flux observer	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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 Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2013 IEEE. In this paper, a grid voltage sensorless model predictive control is proposed based on a sliding mode virtual flux observer (SMVFO). The proposed SMVFO shows good inherent filtering capacity, and thus there is no high-frequency chattering problem. In addition, the proposed SMVFO is designed based on the closed-loop current estimation. Not only is DC-drift issue solved but also dynamic response is enhanced when compared with the prior open-loop virtual flux observer. To verify the effectiveness of the presented SMVFO, it is further integrated into finite control set-model predictive control (FCS-MPC) for pulse width modulator (PWM) rectifiers. The whole control algorithm features simplicity and improved cost-effectiveness due to the absence of modulator and grid voltage sensors. As the SMVFO can predict current at the next sampling instant while estimating virtual flux accurately, the proposed SMVFO assisted FCS-MPC is comparable to its counterpart using measured grid voltage. The simulation and experimental tests were carried out on a two-level voltage source PWM rectifier to validate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/139618