Design and assessment of a LIDAR-based model predictive wind turbine control
Bao, Jie and Yue, Hong (2022) Design and assessment of a LIDAR-based model predictive wind turbine control. Energies, 15 (17). 6429. ISSN 1996-1073 (https://doi.org/10.3390/en15176429)
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Abstract
The development of the Light Detection and Ranging (LIDAR) technology has enabled wider options for wind turbine control, in particular regarding disturbance rejection. The LIDAR measurements provide a spatial, preview wind information, based on which the controller has a better chance to cope with the wind disturbance before it affects the turbine operation. In this paper, a model predictive controller for above-rated wind turbine control was developed with the use of pseudo-LIDAR wind measurements data. A predictive control algorithm was developed based on a linearised wind turbine model, in which the disturbance from the incoming wind was computed by the LIDAR simulator. The optimal control action was applied to the nonlinear turbine model. The developed controller was compared with the baseline control and a previously developed LIDAR-assisted control combining a feedback-and-feedforward design. Our simulation studies on a 5 MW nonlinear wind turbine model, under different wind conditions, demonstrated that the developed LIDAR-based predictive control achieved improved performance in the presence of small variations in the out-of-plane rotor torque and fore-aft tower acceleration, as well as a smoother generator speed regulation and satisfied pitch activity control constraints.
ORCID iDs
Bao, Jie ORCID: https://orcid.org/0000-0001-8967-0729 and Yue, Hong ORCID: https://orcid.org/0000-0003-2072-6223;-
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Item type: Article ID code: 82219 Dates: DateEvent2 September 2022Published30 August 2022Accepted11 August 2022SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 06 Sep 2022 11:19 Last modified: 11 Nov 2024 13:37 URI: https://strathprints.strath.ac.uk/id/eprint/82219