Wind Predictions Upstream Wind Turbines from a LiDAR Database

Soledad Le Clainche, Luis S. Lorente and José M. Vega
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Soledad Le Clainche: ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain
Luis S. Lorente: ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain
José M. Vega: ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain

Energies, 2018, vol. 11, issue 3, 1-15

Abstract: This article presents a new method to predict the wind velocity upstream a horizontal axis wind turbine from a set of light detection and ranging (LiDAR) measurements. The method uses higher order dynamic mode decomposition (HODMD) to construct a reduced order model (ROM) that can be extrapolated in space. LiDAR measurements have been carried out upstream a wind turbine at six different planes perpendicular to the wind turbine axis. This new HODMD-based ROM predicts with high accuracy the wind velocity during a timespan of 24 h in a plane of measurements that is more than 225 m far away from the wind turbine. Moreover, the technique introduced is general and obtained with an almost negligible computational cost. This fact makes it possible to extend its application to both vertical axis wind turbines and real-time operation.

Keywords: Light detection and ranging (LiDAR); wind turbines; prediction; higher order dynamic mode decomposition (HODMD); reduced order model (ROM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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