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Illustration showing the surrounding topography impacting the offshore wind farm. The case study wind farm is visualized in the centre of the figure with a blue rectangle, while the neighbouring wind farms are depicted in yellow. The low-rise coastline is illustrated in green, and the high-rise coastline is outlined in red.
Source publication
This work presents a robust methodology for calibrating analytical wake models, as demonstrated on the velocity deficit parameters of the Gauss–curl hybrid model using 4 years of time series supervisory control and data acquisition (SCADA) data from an offshore wind farm, with a tree-structured Parzen estimator employed as a sampler. Initially, a s...
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Citations
... These models are combined with turbine and site specifications and are run using a numerical solver, yielding an energy production estimate for steady-state flow given the pre-specified wind conditions. To increase the accuracy of these methods, several calibration approaches have been proposed, 20 by e.g. using wind tunnel data (Campagnolo et al., 2022), real world wind farm measurement data (Liu et al., 2018;Keim, 2024;Teng and Markfort, 2020;van Binsbergen et al., 2024) and model data results from computational fluid dynamics (CFD) simulations, such as Large Eddy Simulation (LES) or Reynolds-averaged Navier-Stokes (RANS) results (Cathelain et al., 2020). ...
Whereas engineering wake models can be used to efficiently provide energy production estimates for wind turbine sites, recent studies indicate the importance of a global blockage effect becomes manifest for larger assets. This global blockage effect is caused by site-scale interactions with the atmospheric boundary layer, and results in a wind speed deficit upstream of the asset. This paper presents an efficient and accurate parametrized global blockage model which integrates into existing engineering wake models. The central idea behind this global blockage model is to interpret the wind farm site as a parametrized porous object, subjected to an ambient flow field. We calibrate and benchmark our model through high-resolution LES model data for a representative offshore site using a calibrated wake deficit shape parameter. Results show significant improvements in turbine-level energy production prediction accuracy when compared to results obtained without any blockage model and results obtained with the local self-similar blockage model. The parametrized global blockage model has a significantly lower computational footprint compared to local blockage models. We conclude that not taking (global) blockage into account sufficiently can yield a tendency to overestimate the strength of the turbine wake deficit effects when calibrating wake deficit shape parameters. Finally, we note that the spatial distribution of (global) blockage and wake deficit errors can easily lead to error cancellation when aggregating over binned wind directions.
... The wake deflection and velocity models are configured as Gaussian and the combination model is the sum of squares freestream superposition model (SOSFS) [25]. A fixed turbulence intensity of I ∞ = 6% is assumed with wake expansion coefficients k a = 0.10, k b = 0.004, transition point near-far wake coefficients α w = 0.58, β w = 0.077, and no lateral wake deflection [26]. ...
Power loss due to wake effects from upstream wind turbines is an important factor in the design of floating wind farms. These wake disturbances also increase fatigue loads on downstream units. Economic-driven wind farm layout optimization may culminate in non-standard, irregular configurations of offshore wind farms that deviate from conventional engineering practices. Consequently, wind farm developers are inclined towards a more geometrically coherent layout that are sub-optimal. Floating wind turbines horizontal offsets can be used to overcome wake losses. In this study, a novel method suggesting various mooring orientation methods to passively reposition the wind turbines in the farm to maximize annual energy yield. Preliminary parametric optimization demonstrates the effectiveness of the proposed method for standard floating wind farm designs to reduce wake effects by as much as 30% at rated wind speed compared to a baseline case, while preserving the farm’s overall uniformity.
