Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics

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.

Characterization of wind turbine flow through nacelle-mounted lidars: a review

“…The increasing complexity of the three benchmark cases is intended to allow for the identification of shortcomings in model performance. The benchmark data set has been used in several cases in literature for comparison to LES (e.g., Doubrawa et al, 2020;Yang et al, 2020;Hsieh et al, 2021;Kale et al, 2022), to higher-order Gaussian wake models (Blondel and Cathelain, 2020), and for calibration and validation of the DWM model (Conti et al, 2021a).…”

Improvements to the Dynamic Wake Meandering Model by incorporating the turbulent Schmidt number

“…The DWMM model has seen validation efforts in literature, which are reviewed in the following. The underlying passive scalar assumption of the DWMM has been accepted with the exception of the downstream transport velocity of wake meandering, which is slower than the mean wind speed (Bingöl et al, 2010;Keck et al, 2014b;Machefaux et al, 2015;Conti et al, 2021;Brugger et al, 2022). Machefaux et al (2015) additionally investigated the lateral transport velocity of the wake while it is meandering, but they had no measurements of the lateral velocity of the inflow for comparison.…”

“…However, the description of their data processing mentions low-pass filtering only for the modelled wake of the DWMM and not the observed wake, which could mask the overestimation. Other validations might not have observed this issue previously, because using the mean wind speed for the downstream advection and a temporally averaged validation approach masks the issue (Reinwardt et al, 2018(Reinwardt et al, , 2020Conti et al, 2021).…”

A multi-fidelity approach for wind farm simulations and comparison with field data

“…a curled wake model is implemented recently in FAST.Farm to include the wake asymmetry [13], or performing extra validations against LES simulations and field data [4,7,14]. In addition to this, a careful calibration process is also required to estimate the user-specified parameters inside the DWM model, in order to match high-fidelity LES simulations [15,16] or lidar measurements [17,18]. Nevertheless, Hanssen-Bauer et al [12] found the DWM model of NREL generally underestimates wake deficits, leading to an overestimation of power and thrust for the downstream turbines.…”