Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

Brugger, Peter; Fuertes, Fernando Carbajo; Vahidzadeh, Mohsen; Markfort, Corey D.; Porté‐Agel, Fernando

doi:10.3390/rs11192247

Cited by 29 publications

(24 citation statements)

References 33 publications

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“…The understanding of wind veer in the atmospheric boundary layer plays an essential role across various engineering applications, such as wind load on high-rise buildings [28,30,39] pedestrian-level wind environments [40][41][42], yacht aerodynamics [43,44], the modeling of microscale and mesoscale atmospheric process [12], and, in particular, the performance of wind turbine and wind power output. Brugger et al [45] found that a skewed and tilted wake structure can be observed when wind veer occurs, which is consistent with those predicted by simulation and wind tunnel tests. Churchfield and Sirnivas [46] also observed that the wake is noticeably skewed in the veered inflow condition as it moves downstream, which, however, is not a simple passive tracer-like process.…”

Section: Introductionsupporting

confidence: 75%

Investigation of Marine Wind Veer Characteristics Using Wind Lidar Measurements

Shu

et al. 2020

Atmosphere

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A proper understanding of marine wind characteristics is of essential importance across a wide range of engineering applications. While the offshore wind speed and turbulence characteristics have been examined extensively, the knowledge of wind veer (i.e., turning of wind with height) is much less understood and discussed. This paper presents an investigation of marine wind field with particular emphasis on wind veer characteristics. Extensive observations from a light detection and ranging (Lidar) system at an offshore platform in Hong Kong were examined to characterize the wind veer profiles up to a height of 180 m. The results underscored the occurrence of marine wind veer, with a well-defined two-fold vertical structure. The observed maximum wind veer angle exhibits a reverse correlation with mean wind speed, which decreases from 2.47° to 0.59° for open-sea terrain, and from 7.45° to 1.92° for hilly terrain. In addition, seasonal variability of wind veer is apparent, which is most pronounced during spring and winter due to the frequent occurrence of the low-level jet. The dependence of wind veer on atmospheric stability is evident, particularly during winter and spring. In general, neutral stratification reveals larger values of wind veer angle as compared to those in stable and unstable stratification conditions.

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Section: Introductionsupporting

confidence: 75%

Investigation of Marine Wind Veer Characteristics Using Wind Lidar Measurements

Shu

et al. 2020

Atmosphere

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“…From field measurements, Carbajo Fuertes, Markfort & Porté-Agel (2018) and Brugger et al. (2019) derived a similar empirical linear relation for the wake growth rate as a function of streamwise turbulence intensity, with a slope varying between and . More recently, Teng & Markfort (2020) proposed a calibration procedure for modelling wind farm wakes using a simple analytical approach and wind turbine operational data obtained from the Supervisory Control and Data Acquisition (SCADA) system.…”

Section: Introductionmentioning

confidence: 99%

A physics-based model for wind turbine wake expansion in the atmospheric boundary layer

Vahidi

Porté‐Agel

2022

J. Fluid Mech.

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Analytical wind turbine wake models are widely used to predict the wake velocity deficit. In these models, the wake growth rate is a key parameter specified mainly with empirical formulations. In this study, a new physics-based model is proposed and validated to predict the wake expansion downstream of a turbine based on the incoming ambient turbulence and turbine operating conditions. The new model utilises Taylor diffusion theory, the Gaussian wake model, turbulent mixing layer theory and the analogy between wind turbine wake expansion and scalar diffusion. These components ensure that the model conserves mass and momentum in the far wake and accounts for the ambient turbulence and turbine-induced turbulence effects on the wake expansion. To account for the turbulence relevant scales that contribute to the wake expansion, the model uses the root-mean-square of the low-pass filtered radial velocity component. A simplified version that only requires the unfiltered velocity standard deviation and turbulence integral scale is also proposed. In addition, a new relation for the near-wake length is derived. The model performance is validated using large-eddy simulation data of a wind turbine wake under neutral atmospheric conditions with a wide range of incoming turbulence levels. The results show that the proposed model yields reasonable predictions of the wake width, maximum velocity deficit and near-wake length. In the case with a relatively low incoming streamwise turbulence intensity of 0.05, the ambient and turbine-induced terms in the model contribute almost equally to the wake width, rendering them both crucial for reasonable wake predictions.

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“…where a wind veer of α tt − α bt > 7 • across the rotor area provides ∆y/D = 0.3 for the bottom and top tips at x/D = 5 (Abkar et al, 2018). The effect of wind veer is not further analysed here, because it has already been studied from field measurements in Bodini et al (2017) and Brugger et al (2019).…”

Section: Shape Of the Wakementioning

confidence: 99%

Lidar measurements of yawed wind turbine wakes: characterisation and validation of analytical models

Brugger¹,

Debnath²,

Scholbrock³

et al. 2020

Preprint

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Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a yawed full-scale wind turbine are used for the characterization of the wake flow and the validation of analytical wake models. Inflow scanning Doppler lidars, a meteorological mast and the data of the wind turbine control system complemented the set-up. Results showed an increase of the wake deflection with the yaw angle that agreed with two of the three compared models. For yawed cases, the predicted power of a waked downwind turbine estimated by the two previously successful models had an error of 17 % and 24 % compared to the SCADA data and 12 % and 13 % compared to the power estimated from the Doppler lidar measurements. Shortcomings of the method to compute the power coefficient in an inhomogeneous wind field are likely the reason for disagreement between estimates using the Doppler lidar data versus SCADA data. Further, it was found that some wake steering cases were detrimental to the power output due to errors of the inflow wind direction perceived by the yawed wind turbine and the wake steering design implemented. Lastly, it was observed that the spanwise cross-section of the wake is strongly affected by wind veer, masking the kidney-shaped wake cross-sections observed from wind-tunnel experiments and numerical simulations.

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Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer

Cited by 29 publications

References 33 publications

Investigation of Marine Wind Veer Characteristics Using Wind Lidar Measurements

Investigation of Marine Wind Veer Characteristics Using Wind Lidar Measurements

A physics-based model for wind turbine wake expansion in the atmospheric boundary layer

Lidar measurements of yawed wind turbine wakes: characterisation and validation of analytical models

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