Automated wind turbine wake characterization in complex terrain

Barthelmie, R.J.; Pryor, Sara C.

doi:10.5194/amt-12-3463-2019

Cited by 20 publications

(17 citation statements)

References 56 publications

(72 reference statements)

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“…A limited amount of flow observations have been made using anemometers deployed on tall masts (of up to 80 m) [23] and/or using remote sensing instruments [39]. Lidar or sodar measurements are typically acquired during relatively short-duration field experiments and have been employed, for example, to reduce short-term wind forecasting errors via data assimilation [40], to characterize wind extremes and spatial coherence [41] and to quantify WT wakes [42]. However, they are of limited value to characterize long-term wind speed profiles.…”

Section: Wrf Model Simulationsmentioning

confidence: 99%

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

et al. 2020

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Continued growth of wind turbine physical dimensions is examined in terms of the implications for wind speed, power and shear across the rotor plane. High-resolution simulations with the Weather Research and Forecasting model are used to generate statistics of wind speed profiles for scenarios of current and future wind turbines. The nine-month simulations, focused on the eastern Central Plains, show that the power scales broadly as expected with the increase in rotor diameter (D) and wind speeds at hub-height (H). Increasing wind turbine dimensions from current values (approximately H = 100 m, D = 100 m) to those of the new International Energy Agency reference wind turbine (H = 150 m, D = 240 m), the power across the rotor plane increases 7.1 times. The mean domain-wide wind shear exponent (α) decreases from 0.21 (H = 100 m, D = 100 m) to 0.19 for the largest wind turbine scenario considered (H = 168 m, D = 248 m) and the frequency of extreme positive shear (α > 0.2) declines from 48% to 38% of 10-min periods. Thus, deployment of larger wind turbines potentially yields considerable net benefits for both the wind resource and reductions in fatigue loading related to vertical shear.

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Section: Wrf Model Simulationsmentioning

confidence: 99%

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

et al. 2020

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“…They identified four different cases for the stratification: "stable + mountain wave" where the wake advected downwards following the terrain, "stable" and "neutral" cases where the wake remained at a constant height above sea level, and "unstable" cases where the wake advected upwards. Barthelmie and Pryor (2019) similarly characterized wake behavior at Perdigão based on atmospheric stability. Using measurements averaged over longer time periods (10 minutes compared to 24 seconds), they infer that all wakes were initially lofted and then strongly influenced by stability, with wake centers moving downwards in unstable conditions and also generally moving downwards but remaining at greater heights during stable conditions.…”

Section: Introductionmentioning

confidence: 97%

“…The test location chosen in this study is that of the Perdigão field campaign (Fernando et al, 2019), which took place in 2017 in Portugal. The Perdigão experiment characterized the flow over two parallel ridges with a wind turbine located on the southwest ridge and provided valuable data for characterizing wind turbine wakes in complex terrain (Menke et al, 2018;Barthelmie and Pryor, 2019;Wildmann et al, 2018Wildmann et al, , 2019. Menke et al (2018) classified wind turbine wake behavior based on atmospheric stability using scanning Doppler lidars at Perdigão.…”

Section: Introductionmentioning

confidence: 99%

Meso- to micro-scale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain

Wise¹,

Neher²,

Arthur³

et al. 2021

Preprint

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Abstract. Most detailed modeling and simulation studies of wind turbine wakes have considered flat terrain scenarios. Wind turbines, however, are commonly sited in mountainous or hilly terrain to take advantage of accelerating flow over ridgelines. In addition to topographic acceleration, other turbulent flow phenomena commonly occur in complex terrain, and often depend upon the thermal stratification of the atmospheric boundary layer. Enhanced understanding of wind turbine wake interaction with these terrain-induced flow phenomena can significantly improve wind farm siting, optimization, and control. In this study, we simulate conditions observed during the Perdigão field campaign in 2017, consisting of flow over two parallel ridges with a wind turbine located on top of one of the ridges. We use the Weather Research and Forecasting model (WRF) nested down to micro-scale large-eddy simulation (LES) at 10 m resolution, with a generalized actuator disk (GAD) wind turbine parameterization to simulate turbine wakes. Two case studies are selected, a stable case where a mountain wave occurs and a convective case where a recirculation zone forms in the lee of the ridge with the turbine. The WRF-LES-GAD model is validated against data from meteorological towers, soundings, and a tethered lifting system, showing good agreement for both cases. Comparisons with scanning Doppler lidar data for the stable case show that the overall characteristics of the mountain wave are well-captured, although the wind speed is underestimated. For the convective case, the size of the recirculation zone within the valley shows good agreement. The wind turbine wake behavior shows dependence on atmospheric stability, with different amounts of vertical deflection from the terrain and persistence downstream for the stable and convective conditions. For the stable case, the wake follows the terrain along with the mountain wave and deflects downwards by nearly 100 m below hub-height at four rotor diameters downstream. For the convective case, the wake deflects above the recirculation zone over 50 m above hub-height at the same downstream distance. This study demonstrates the ability of the WRF-LES-GAD model to capture the expected behavior of wind turbine wakes in regions of complex terrain, and thereby to potentially improve wind turbine siting and operation in hilly landscapes.

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“…Even under these relatively simple datasets, methodologies still exhibit difficulties in distinguishing the wind turbine wake from local turbulent flows, particularly at distances far behind the rotor [7]. Further, variations in the local orography in and around wind farms enhance turbulence and flow complexity, thus rendering wind turbine wake identification, characterization and tracking more difficult [8].…”

Section: Introductionmentioning

confidence: 99%

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

et al. 2021

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We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

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Automated wind turbine wake characterization in complex terrain

Cited by 20 publications

References 56 publications

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

Meso- to micro-scale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

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