Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution

Syuan Yi Chen; Min Han Song

doi:10.1007/978-3-319-61824-1_51

Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution

Syuan Yi Chen^*
, Min Han Song

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This study proposes an adaptive differential evolution (ADE)-based variable bias current control strategy to improve the energy efficiency of an active magnetic bearing (AMB) system. In the AMB system, the drive current is composed of a control current and a superimposed bias current in which the former is controlled by an external controller used to regulate the rotor position while the latter is set as a pre-designed constant used to improve the linearity and dynamic performance. Generally, the bias current causes power loss even if no force is required. In this regard, the ADE-based variable bias current control strategy is proposed to minimize the energy consumption of the AMB control system without altering the control performance. Experimental results demonstrate the high-accuracy control and significant energy saving performances of the proposed method. The energy improvements compared to baseline were 20.24% and 17.65% for the operation periods of 10 s and 50 s, respectively.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
Editors	Ying Tan, Hideyuki Takagi, Yuhui Shi
Publisher	Springer Verlag
Pages	466-474
Number of pages	9
ISBN (Print)	9783319618234
DOIs	https://doi.org/10.1007/978-3-319-61824-1_51
Publication status	Published - 2017
Event	8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan Duration: 2017 Jul 27 → 2017 Aug 1

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10385 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Conference on Swarm Intelligence, ICSI 2017
Country/Territory	Japan
City	Fukuoka
Period	2017/07/27 → 2017/08/01

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Differential evolution
Energy-saving
Magnetic bearing
Variable bias current optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-61824-1_51

Cite this

Chen, S. Y., & Song, M. H. (2017). Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution. In Y. Tan, H. Takagi, & Y. Shi (Eds.), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings (pp. 466-474). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61824-1_51

Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution. / Chen, Syuan Yi; Song, Min Han.
Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. ed. / Ying Tan; Hideyuki Takagi; Yuhui Shi. Springer Verlag, 2017. p. 466-474 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10385 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, SY & Song, MH 2017, Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution. in Y Tan, H Takagi & Y Shi (eds), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10385 LNCS, Springer Verlag, pp. 466-474, 8th International Conference on Swarm Intelligence, ICSI 2017, Fukuoka, Japan, 2017/07/27. https://doi.org/10.1007/978-3-319-61824-1_51

Chen SY, Song MH. Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution. In Tan Y, Takagi H, Shi Y, editors, Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Springer Verlag. 2017. p. 466-474. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61824-1_51

Chen, Syuan Yi ; Song, Min Han. / Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution. Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. editor / Ying Tan ; Hideyuki Takagi ; Yuhui Shi. Springer Verlag, 2017. pp. 466-474 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This study proposes an adaptive differential evolution (ADE)-based variable bias current control strategy to improve the energy efficiency of an active magnetic bearing (AMB) system. In the AMB system, the drive current is composed of a control current and a superimposed bias current in which the former is controlled by an external controller used to regulate the rotor position while the latter is set as a pre-designed constant used to improve the linearity and dynamic performance. Generally, the bias current causes power loss even if no force is required. In this regard, the ADE-based variable bias current control strategy is proposed to minimize the energy consumption of the AMB control system without altering the control performance. Experimental results demonstrate the high-accuracy control and significant energy saving performances of the proposed method. The energy improvements compared to baseline were 20.24\% and 17.65\% for the operation periods of 10 s and 50 s, respectively.",

keywords = "Differential evolution, Energy-saving, Magnetic bearing, Variable bias current optimization",

author = "Chen, \{Syuan Yi\} and Song, \{Min Han\}",

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Y1 - 2017

N2 - This study proposes an adaptive differential evolution (ADE)-based variable bias current control strategy to improve the energy efficiency of an active magnetic bearing (AMB) system. In the AMB system, the drive current is composed of a control current and a superimposed bias current in which the former is controlled by an external controller used to regulate the rotor position while the latter is set as a pre-designed constant used to improve the linearity and dynamic performance. Generally, the bias current causes power loss even if no force is required. In this regard, the ADE-based variable bias current control strategy is proposed to minimize the energy consumption of the AMB control system without altering the control performance. Experimental results demonstrate the high-accuracy control and significant energy saving performances of the proposed method. The energy improvements compared to baseline were 20.24% and 17.65% for the operation periods of 10 s and 50 s, respectively.

AB - This study proposes an adaptive differential evolution (ADE)-based variable bias current control strategy to improve the energy efficiency of an active magnetic bearing (AMB) system. In the AMB system, the drive current is composed of a control current and a superimposed bias current in which the former is controlled by an external controller used to regulate the rotor position while the latter is set as a pre-designed constant used to improve the linearity and dynamic performance. Generally, the bias current causes power loss even if no force is required. In this regard, the ADE-based variable bias current control strategy is proposed to minimize the energy consumption of the AMB control system without altering the control performance. Experimental results demonstrate the high-accuracy control and significant energy saving performances of the proposed method. The energy improvements compared to baseline were 20.24% and 17.65% for the operation periods of 10 s and 50 s, respectively.

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KW - Energy-saving

KW - Magnetic bearing

KW - Variable bias current optimization

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Energy-saving variable bias current optimization for magnetic bearing using adaptive differential evolution

Abstract

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UN SDGs

Keywords

ASJC Scopus subject areas

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