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

Syuan Yi Chen, Min Han Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
EditorsYing Tan, Hideyuki Takagi, Yuhui Shi
PublisherSpringer Verlag
Pages466-474
Number of pages9
ISBN (Print)9783319618234
DOIs
Publication statusPublished - 2017 Jan 1
Event8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan
Duration: 2017 Jul 272017 Aug 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10385 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Swarm Intelligence, ICSI 2017
CountryJapan
CityFukuoka
Period17/7/2717/8/1

Fingerprint

Magnetic Bearing
Magnetic bearings
Bias currents
Active Magnetic Bearing
Differential Evolution
Energy Saving
Energy conservation
Electric current control
Optimization
Control Strategy
Dynamic Performance
Energy Efficiency
Linearity
Rotor
Energy Consumption
Energy efficiency
Baseline
High Accuracy
Energy utilization
Rotors

Keywords

  • Differential evolution
  • Energy-saving
  • Magnetic bearing
  • Variable bias current optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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 proceedingConference 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, 17/7/27. 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)). https://doi.org/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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