Abstract
Abstract. We study the calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, USA. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence, and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds, and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows for both model implementation and uncertainty assessment. We validate the resulting fully resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far-wake region beyond four rotor diameters as long as properly calibrated parameters are used, and wake meandering time series are accurately replicated. We show that the current DWM model parameters in the IEC standard lead to conservative wake deficit predictions for ambient turbulence intensities above 12 % at the SWiFT site. Finally, we provide practical recommendations for reliable calibration procedures.
Highlights
Wake effects are perceived as one of the largest sources of uncertainty in energy production and load estimates of onshore and offshore wind farms (Walker et al, 2016)
We study the calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, USA
Where ym and zm denote the spatial coordinates of the wake meandering time series, and ym, and zm, are measures of their relative uncertainties. We introduce these errors to account for incorrect wake tracking positions that can arise due to the adopted wake tracking algorithm; ym, and zm, are assumed to be uncorrelated and to follow a normal distribution with zero mean and standard deviation such that the 95 % percentile corresponds to approximately 4 m, which is twice the resolution adopted to interpolate SpinnerLidar measurements onto the regular grid
Summary
Wake effects are perceived as one of the largest sources of uncertainty in energy production and load estimates of onshore and offshore wind farms (Walker et al, 2016). Within an iterative design process and/or optimization study, wake effects on aeroelastic turbine responses are predicted using engineering wake models, e.g. the Dynamic Wake Meandering (DWM) (Madsen et al, 2010) and Frandsen (Frandsen, 2007) models, which can be used within simple and fast design tools (Braunbehrens and Segalini, 2019) Their main limitation is their reduced ability to fully resolve the turbulence structures of the wake field, which often leads to an inaccurate representation of the flow field and biased power and load predictions (Reinwardt et al, 2018). Several studies have demonstrated the superior performance of the DWM model compared to other engineering wake models that only predict steady wake features (Thomsen et al, 2007; Larsen et al, 2013; Reinwardt et al, 2018), the accuracy of both the DWM-simulated wake flow fields and the resultant turbine power and load predictions is still to be assessed
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