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Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms

Abstract : The growing size of wind turbines and wind farms in the offshore environment, eventually occupying tens of kilometers and extending beyond 200 m in height, has challenged traditional wind farm models to incorporate larger atmospheric scales with greater influence from the full extent of the atmospheric boundary layer (ABL). The modeling system is subject to variability from mesoscale weather phenomena like land-sea transitions or farm-farm effects that produce horizontal gradients in the wind resource, as well as phenomena like low-level jets, gravity waves, etc, that modify the turbulence structure of the ABL as it interacts with the wind farm [1][2]. The transition to multi-scale wind farm modeling requires a systematic methodology that allows determining the relative importance of these effects in wind farm performance and the predictive capacity of models [3]. This is especially important for offshore wind developers that face significant financial and operational costs due to uncertainties in wind resource assessment [4]. Understanding how these uncertainties originate from wind farm design tools is of fundamental importance to mitigate these losses.
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https://hal-ifp.archives-ouvertes.fr/hal-03010396
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Submitted on : Wednesday, December 9, 2020 - 10:27:38 AM
Last modification on : Friday, January 8, 2021 - 3:14:53 AM
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Javier Rodrigo, Fernando Guillén, Pedro Fernandes Correia, Bibiana García Hevia, Wolfgang Schlez, et al.. Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms. Journal of Physics: Conference Series, IOP Publishing, 2020, 1618 (6), pp.062044. ⟨10.1088/1742-6596/1618/6/062044⟩. ⟨hal-03010396⟩

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