In large wind farms, the wake effect and blockage effect are underestimated problems. The wind is slowed down during inflow, resulting in lower electricity production than expected.
Wind farm design, scenario analysis by governments, grid stability studies, hydrogen wind and energy island design need accurate wake models or digital twin models that are applicable to entire concession zones and make it possible to process monitoring data. The existing models use hyperparameters to do so.
Cloud4Wake will develop methods to define the hyperparameters on the basis of large data sets and thus optimise the accuracy of the models concerning the effects of spring and sheer. These data for the North Sea are currently collected from various sources: LIDAR at various locations, meteorological data and data from offshore wind farms.
Intended project results:
- A new method to estimate wake effects in a model for a zone of various offshore wind farms. This way, losses due to wake effects can be better estimated by the industry;
- A cloud-based framework to calibrate these models on large (field) data sets. It is important that farms collecting field data will use them to optimise the design of their wind farms.
Partners: VUB; KU Leuven; Sirris; and von Karman Institute for Fluid Dynamics
Advisory board: Norther; Parkwind; Elicio; Otary; Prophesea; AMDK; GEOxyz; ENGIE-Laborelec; Tractebel; Siemens Gamesa; and Ocean Winds.
With the support of: VLAIO (Flanders Innovation & Entrepreneurship)
Contact: Kinnie De Beule