Characterizing wind gusts in complex terrain

Letson, Frederick; Barthelmie, R.J.; Hu, Weifei; Pryor, Sara C.

doi:10.5194/acp-19-3797-2019

Cited by 26 publications

(17 citation statements)

References 78 publications

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“…6, not all the outliers are eliminated after the first stage. The second stage relies on an outlier detection algorithm relying on the absolute deviation around the median (Leys et al, 2013). A moving median filter with a window length of 200 s was applied to the time series.…”

Section: Lidar Data Processing For Coherence Analysismentioning

confidence: 99%

The COTUR project: remote sensing of offshore turbulence for wind energy application

et al. 2021

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Abstract. The paper presents the measurement strategy and data set collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and LidarPlanner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines. The preliminary results show limited variations of the lateral coherence with the scanning distance. A slight increase in the identified Davenport decay coefficient with the height is partly due to the limited pointing accuracy of the instruments. These results underline the importance of achieving pointing errors under 0.1∘ to study properly the lateral coherence of turbulence at scanning distances of several kilometres.

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Section: Lidar Data Processing For Coherence Analysismentioning

confidence: 99%

The COTUR project: remote sensing of offshore turbulence for wind energy application

et al. 2021

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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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“…Most research focussed on lidar-based wake characterization has employed case studies, either because the field campaign is relatively short, providing only a few cases (Banta et al, 2015), or because it is difficult to automate the process of identifying and quantifying wake deficits, shapes, meander and so forth (Bodini et al, 2017). Adding to the challenges of multiple wake characterization, the behaviour of wakes in complex terrain is more difficult to both measure and model, and important supporting information such as atmospheric stability and turbulence intensity that has direct and measurable effects on changes in power production due to wakes is frequently only available from a limited number of instruments deployed on meteorological masts (Machefaux et al, 2016).…”

Section: Remote Sensing Of Flow and Wakesmentioning

confidence: 99%

Automated wind turbine wake characterization in complex terrain

Barthelmie

Pryor

2019

Atmos. Meas. Tech.

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Abstract. An automated wind turbine wake characterization algorithm has been developed and applied to a data set of over 19 000 scans measured by a ground-based scanning Doppler lidar at Perdigão, Portugal, over the period January to June 2017. Potential wake cases are identified by wind speed, direction and availability of a retrieved free-stream wind speed. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, with 46 % having a clear and well-defined wake centre surrounded by a coherent area of lower wind speeds while 16 % have split centres or multiple lobes where the lower wind speed volumes are no longer in coherent areas but present as two or more distinct areas or lobes. Only 5 % of cases are not detected; the remaining 33 % could not be categorized either by the algorithm or subjectively, mainly due to the complexity of the background flow. Average wake centre heights categorized by inflow wind speeds are shown to be initially lofted (to two rotor diameters, D, downstream) except when the inflow wind speeds exceed 12 ms−1. Even under low wind speeds, by 3.5 D downstream of the wind turbine, the mean wake centre position is below the initial wind turbine hub height and descends broadly following the terrain slope. However, this behaviour is strongly linked to the hour of the day and atmospheric stability. Overnight and in stable conditions, the average height of the wake centre is 10 m higher than in unstable conditions at 2 D downstream from the wind turbine and 17 m higher at 4.5 D downstream.

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Characterizing wind gusts in complex terrain

Cited by 26 publications

References 78 publications

The COTUR project: remote sensing of offshore turbulence for wind energy application

The COTUR project: remote sensing of offshore turbulence for wind energy application

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

Automated wind turbine wake characterization in complex terrain

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