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Met‐ocean conditions influence on floating offshore wind farms power production
Authors:Michele Martini  Raúl Guanche  José A Armesto  Iñigo J Losada  César Vidal
Affiliation:Environmental Hydraulics Institute “IH Cantabria”, Universidad de Cantabria Parque Científico y Tecnológico de Cantabria, Santander, Cantabria, Spain
Abstract:Met‐ocean conditions may affect the performance of a floating wind turbine, since a harsh climate could lead the system to exceed its operating thresholds and thus to force the machine shutdown. In this paper, it is a proposed methodology to evaluate the effect of met‐ocean conditions on the long‐term dynamic behaviour, and energy production, of a floating wind farm. For a sample of 500 MW farm located off the coast of Aberdeen (Scotland), 20 years of met‐ocean data are generated by means of meteorological reanalysis techniques. A subset of 1000 hourly conditions is selected, by means of a maximum dissimilarity algorithm, and input to a dynamic floating wind turbine model. Numerical results are then interpolated for the whole set of met‐ocean data, using radial basis functions. This approach allows to dramatically reduce the global computation time. Tower inclination and hub acceleration are chosen as relevant operating parameters: the former mainly depends on mean wind speed and direction, being largest at rated wind speed. The latter is also affected by significant wave height, and reaches its highest values when wind and waves are aligned. For each simulation, any machine exceeding the selected safety threshold is considered to be shut down. Assuming continuous operation, the average lifespan capacity factor of the farm is 50.2%; more restrictive tolerances result in a non‐linear reduction of the energy production. This approach may help both at the design and the operational stage, in determining the best trade‐off between energy production and safe operation. Copyright © 2015 John Wiley & Sons, Ltd.
Keywords:floating wind turbine  met‐ocean conditions  operating thresholds  longterm production  scattered data  wind farm  dynamic model
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