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

2021 ◽  
Author(s):  
Adam S. Wise ◽  
James M. T. Neher ◽  
Robert S. Arthur ◽  
Jeffrey D. Mirocha ◽  
Julie K. Lundquist ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Recirculation Zone ◽  
Atmospheric Stability ◽  
Mountain Wave ◽  
Stable Case ◽  
Flow Phenomena ◽  
Turbine Wake ◽  
Good Agreement ◽  
Wind Turbine Wake

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.

scholarly journals Automated Wind Turbine Wake Characterization in Complex Terrain

2019 ◽  
Author(s):  
Rebecca J. Barthelmie ◽  
Sara C. Pryor
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Atmospheric Stability ◽  
Average Height ◽  
Wind Speeds ◽  
Stable Conditions ◽  
The Mean ◽  
Low Wind Speeds ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract. An automated wind turbine wake characterization algorithm has been developed and applied to a dataset of over 19,000 scans measured by scanning Doppler lidar at Perdigão over the period January to June 2017. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, 46 % having a clear and well-defined wake centre while 16 % have split centres or multiple 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 2 rotor diameters, D) 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 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 and 17 m higher at 4.5 D downstream of the wind turbine.

scholarly journals Does the wind turbine wake follow the topography? – A multi-lidar study in complex terrain

2018 ◽  
Author(s):  
Robert Menke ◽  
Nikola Vasiljević ◽  
Kurt Hansen ◽  
Andrea N. Hahmann ◽  
Jakob Mann
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Short Range ◽  
Strong Dependence ◽  
Atmospheric Stability ◽  
Induced Gravity ◽  
Stable Conditions ◽  
Turbine Wake ◽  
Maximum Inclination ◽  
Wind Turbine Wake

Abstract. The wind turbine wake of a single turbine in complex terrain is analysed using measurements from lidars. A particular focus of this analysis is the wake deficit and propagation. Six scanning lidars, three short-range and three long-range WindScanners, were deployed during the Perdigão 2015 measurement campaign which took place at a double-ridge site in Portugal. Several scanning scenarios, including triple- and dual-Doppler scans, were designed to capture the wind turbine wake of a 2 MW turbine located on one of the ridges. Different wake displacements are categorised according to the diurnal cycle. The results show a strong dependence of the vertical wake propagation to the atmospheric stability. When a terrain-induced gravity wave is observed under stable conditions, the wake follows the terrain down the ridge with a maximum inclination of −28°. During unstable conditions, the wake is advected upwards by up to 29° above horizontal.

scholarly journals Does the wind turbine wake follow the topography? A multi-lidar study in complex terrain

2018 ◽  
Vol 3 (2) ◽  
pp. 681-691 ◽  
Author(s):  
Robert Menke ◽  
Nikola Vasiljević ◽  
Kurt S. Hansen ◽  
Andrea N. Hahmann ◽  
Jakob Mann
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Horizontal Plane ◽  
Strong Dependence ◽  
Atmospheric Stability ◽  
Atmospheric Wave ◽  
Stable Conditions ◽  
Turbine Wake ◽  
Maximum Inclination ◽  
Wind Turbine Wake

Abstract. The wake of a single wind turbine in complex terrain is analysed using measurements from lidars. A particular focus of this analysis is the wake deficit and propagation. Six scanning lidars (three short-range and three long-range WindScanners) were deployed during the Perdigão 2015 measurement campaign, which took place at a double-ridge site in Portugal. Several scanning scenarios, including triple- and dual-Doppler scans, were designed to capture the wind turbine wake of a 2 MW turbine located on one of the ridges. Different wake displacements are categorized according to the time of the day. The results show a strong dependence of the vertical wake propagation on the atmospheric stability. When an atmospheric wave is observed under stable conditions, the wake follows the terrain down the ridge with a maximum inclination of -28∘. During unstable conditions, the wake is advected upwards by up to 29∘ above the horizontal plane.

scholarly journals Volumetric Lidar Scanning of Wind Turbine Wakes under Convective and Neutral Atmospheric Stability Regimes

2014 ◽  
Vol 31 (10) ◽  
pp. 2035-2048 ◽  
Author(s):  
Giacomo Valerio Iungo ◽  
Fernando Porté-Agel
Keyword(s):  
Thermal Stability ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Velocity ◽  
Turbulence Intensity ◽  
Atmospheric Stability ◽  
Power Harvesting ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract Optimization of a wind farm’s layout is a strategic task to reduce wake effects on downstream turbines, thus maximizing wind power harvesting. However, downstream evolution and recovery of each wind turbine wake are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as the vertical profiles of the mean wind velocity and the turbulence intensity, which are in turn affected by the ABL thermal stability. Therefore, the characterization of the variability of wind turbine wakes under different ABL stability regimes becomes fundamental to better predict wind power harvesting and to improve wind farm efficiency. To this aim, wind velocity measurements of the wake produced by a 2-MW Enercon E-70 wind turbine were performed with three scanning Doppler wind lidars. One lidar was devoted to the characterization of the incoming wind—in particular, wind velocity, shear, and turbulence intensity at the height of the rotor disc. The other two lidars performed volumetric scans of the wind turbine wake under different atmospheric conditions. Through the evaluation of the minimum wake velocity deficit as a function of the downstream distance, it is shown that the ABL stability regime has a significant effect on the wake evolution; in particular, the wake recovers faster under convective conditions. This result suggests that atmospheric inflow conditions, and particularly thermal stability, should be considered for improved wake models and predictions of wind power harvesting.

scholarly journals Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

2020 ◽  
Vol 5 (2) ◽  
pp. 469-488
Author(s):  
James B. Duncan Jr. ◽  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Wind Turbine ◽  
Potential Benefit ◽  
Scale Validation ◽  
Control Strategies ◽  
Atmospheric Stability ◽  
Full Scale ◽  
Atmospheric Conditions ◽  
Turbine Wake ◽  
Plant Control ◽  
Wind Turbine Wake

Abstract. Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utility-scale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g., boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicates the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.

scholarly journals Exploring the complexities associated with full-scale wind plant wake mitigation control experiments

2019 ◽  
Author(s):  
James B. Duncan Jr. ◽  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Wind Turbine ◽  
Potential Benefit ◽  
Scale Validation ◽  
Control Strategies ◽  
Atmospheric Stability ◽  
Full Scale ◽  
Atmospheric Conditions ◽  
Turbine Wake ◽  
Plant Control ◽  
Wind Turbine Wake

Abstract. Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utility-scale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analysing wind plant control at full-scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g. boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicate the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.

scholarly journals Wind turbine wake measurement in complex terrain

2016 ◽  
Vol 753 ◽  
pp. 032013 ◽  
Author(s):  
KS Hansen ◽  
GC Larsen ◽  
R Menke ◽  
N Vasiljevic ◽  
N Angelou ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Turbine Wake ◽  
Wind Turbine Wake

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

2021 ◽  
Vol 13 (21) ◽  
pp. 4438
Author(s):  
Jeanie A. Aird ◽  
Eliot W. Quon ◽  
Rebecca J. Barthelmie ◽  
Mithu Debnath ◽  
Paula Doubrawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Test Model ◽  
Data Quality Control ◽  
Turbine Wake ◽  
Wake Characteristics ◽  
Wind Turbine Wake

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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