Eddy currents in a passive magnetic axial thrust bearing for a flywheel energy storage system

Hedlund, Magnus; Abrahamsson, Johan; Perez-Loya, J. Jose; Lundin, Johan; Bernhoff, Hans

doi:10.3233/jae-160151

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…First, the flywheel rotor is located inside a chamber, and it is suspended by magnetic bearings that allows them to work at quasi-vacuum conditions. Therefore standby losses are minimized [65]. (b)…”

Section: Flywheel Modelmentioning

confidence: 99%

Marine Tidal and Wave Energy Converters

Benbouzid

Elbouchikhi²

2020

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Section: Flywheel Modelmentioning

confidence: 99%

Marine Tidal and Wave Energy Converters

Benbouzid

Elbouchikhi²

2020

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Section: Flywheel Modelmentioning

confidence: 99%

Wave Power Output Smoothing through the Use of a High-Speed Kinetic Buffer

et al. 2019

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In this paper, a new control strategy for power output smoothing in a hybrid wave energy installation coupled to a flywheel energy storage system (FESS) is proposed. The control scheme is composed by three stages: a wave generator clustering process at the farm connection point; a power filtering process; and the control of the flywheel energy storage in order to improve the power output of the hybrid wave farm. The proposed control is validated at the existing Lysekil Wave Energy Site located in Sweden, by using real generator measurements. Results show that the application of the flywheel energy storage system reduces the maximum peak power output from the wave energy installation by 85% and the peak/average power ratio by 76%. It is shown that the proposed system can reduce grid losses by 51%, consequently improving the energy efficiency of the power network. The application of the proposed control strategy allows the hybrid wave power plant to follow a power reference signal that is imposed by the grid operator. In addition, the study demonstrates that the application of the proposed control allows the hybrid wave power plant to follow a power reference signal that is imposed by the grid operator. In addition, the study demonstrates that the application of the proposed control enables a wave farm with flywheel energy storage to be a controllable, flexible resource in order to fulfill future grid code requirements for marine energy installations.

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“…Bearingless motor is a new type of alternating current (AC) motor that is proposed based on the stator structure similarity between magnetic bearing and common AC motor. 1–4 Comparing with magnetic bearing motor, 5–7 the bearingless motor has a series of advantages, such as shorter shaft, more compact structure and higher critical speed. 8–13 So it has broad application prospects in many fields, such as high-speed electric spindle drive, centrifugal equipment, large capacity electromechanical energy storage, military and space fields, and then it becomes a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy of a bearingless induction motor

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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In view of the problems that the analytical inverse system decoupling method of bearingless induction motor is sensitive to the change of motor parameters and is greatly affected by unmodeled dynamics, and that of traditional proportional–derivative controller lacking the self-adaptive regulation ability, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy is proposed for a bearingless induction motor system. First, under the conditions of considering the stator current dynamic of torque winding, and by neural network inverse system method, the bearingless induction motor system is decoupled into four pseudo-linear integral subsystems. Second, the traditional proportional–derivative controller is improved, and the fuzzy control algorithm is used to adjust the parameters of improved proportional–derivative controller adaptively. Thus, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control system is constructed for a bearingless induction motor. The simulation experimental results show that the proposed control strategy not only can effectively improve the stability, robustness and steady-state control accuracy of bearingless induction motor system, but also can significantly improve the dynamic response speed and the ability to resist the influences of motor parameter change and load disturbance.

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Eddy currents in a passive magnetic axial thrust bearing for a flywheel energy storage system

Cited by 8 publications

References 17 publications

Marine Tidal and Wave Energy Converters

Marine Tidal and Wave Energy Converters

Wave Power Output Smoothing through the Use of a High-Speed Kinetic Buffer

Neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy of a bearingless induction motor

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