scholarly journals Automated wind turbine wake characterization in complex terrain

2019 ◽  
Vol 12 (6) ◽  
pp. 3463-3484 ◽  
Author(s):  
Rebecca J. Barthelmie ◽  
Sara C. Pryor
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Atmospheric Stability ◽  
Average Height ◽  
Data Set ◽  
Wind Speeds ◽  
Stable Conditions ◽  
Low Wind Speeds ◽  
Turbine Wake ◽  
Wind Turbine Wake

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.

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 Lidar Investigation of Atmosphere Effect on a Wind Turbine Wake

2013 ◽  
Vol 30 (11) ◽  
pp. 2554-2570 ◽  
Author(s):  
I. N. Smalikho ◽  
V. A. Banakh ◽  
Y. L. Pichugina ◽  
W. A. Brewer ◽  
R. M. Banta ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Maximum Velocity ◽  
Turbulent Energy ◽  
Energy Dissipation Rate ◽  
Longitudinal Dimension ◽  
Wind Speeds ◽  
Wind Structure ◽  
Coherent Doppler Lidar ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract An experimental study of the spatial wind structure in the vicinity of a wind turbine by a NOAA coherent Doppler lidar has been conducted. It was found that a working wind turbine generates a wake with the maximum velocity deficit varying from 27% to 74% and with the longitudinal dimension varying from 120 up to 1180 m, depending on the wind strength and atmospheric turbulence. It is shown that, at high wind speeds, the twofold increase of the turbulent energy dissipation rate (from 0.0066 to 0.013 m2 s−3) leads, on average, to halving of the longitudinal dimension of the wind turbine wake (from 680 to 340 m).

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 Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2101
Author(s):  
Takanori Uchida ◽  
Tadasuke Yoshida ◽  
Masaki Inui ◽  
Yoshihiro Taniyama
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wind Speeds ◽  
Wind Speed Distribution ◽  
Wake Model ◽  
Turbine Wake ◽  
Swept Area

Many bottom-mounted offshore wind farms are currently planned for the coastal areas of Japan, in which wind speeds of 6.0–10.0 m/s are extremely common. The impact of such wind speeds is very relevant for the realization of bottom-mounted offshore wind farms. In evaluating the feasibility of these wind farms, therefore, strict evaluation at wind speeds of 6.0–10.0 m/s is important. In the present study, the airflow characteristics of 2 MW-class downwind wind turbine wake flows were first investigated using a vertically profiling remote sensing wind measurement device (lidar). The wind turbines used in this study are installed at the point where the sea is just in front of the wind turbines. A ground-based continuous-wave (CW) conically scanning wind lidar system (“ZephIR ZX300”) was used. Focusing on the wind turbine near-wakes, the detailed behaviors were considered. We found that the influence of the wind turbine wake, that is, the wake loss (wind velocity deficit), is extremely large in the wind speed range of 6.0–10.0 m/s, and that the wake loss was almost constant at such wind speeds (6.0–10.0 m/s). It was additionally shown that these results correspond to the distribution of the thrust coefficient of the wind turbine. We proposed a computational fluid dynamics (CFD) porous disk (PD) wake model as an intermediate method between engineering wake models and CFD wake models. Based on the above observations, the wind speed range for reproducing the behavior of the wind turbine wakes with the CFD PD wake model we developed was set to 6.0–10.0 m/s. Targeting the vertical wind speed distribution in the near-wake region acquired in the “ZephIR ZX300”, we tuned the parameters of the CFD PD wake model (CRC = 2.5). We found that in practice, when evaluating the mean wind velocity deficit due to wind turbine wakes, applying the CFD PD wake model in the wind turbine swept area was very effective. That is, the CFD PD wake model can reproduce the mean average wind speed distribution in the wind turbine swept area.

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