scholarly journals Field Measurements of Wind Characteristics Using LiDAR on a Wind Farm with Downwind Turbines Installed in a Complex Terrain Region

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5135
Author(s):  
Tetsuya Kogaki ◽  
Kenichi Sakurai ◽  
Susumu Shimada ◽  
Hirokazu Kawabata ◽  
Yusuke Otake ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Field Experiments ◽  
Wind Farms ◽  
Field Measurements ◽  
Horizontal Wind ◽  
Flow Distortion ◽  
Horizontal Wind Speed ◽  
Vertical Profiling ◽  
Wind Characteristics

Downwind turbines have favorable characteristics such as effective energy capture in up-flow wind conditions over complex terrains. They also have reduced risk of severe accidents in the event of disruptions to electrical networks during strong storms due to the free-yaw effect of downwind turbines. These favorable characteristics have been confirmed by wind-towing tank experiments and computational fluid dynamics (CFD) simulations. However, these advantages have not been fully demonstrated in field experiments on actual wind farms. In this study—although the final objective was to demonstrate the potential advantages of downwind turbines through field experiments—field measurements were performed using a vertical-profiling light detection and ranging (LiDAR) system on a wind farm with downwind turbines installed in complex terrains. To deduce the horizontal wind speed, vertical-profiling LiDARs assume that the flow of air is uniform in space and time. However, in complex terrains and/or in wind farms where terrain and/or wind turbines cause flow distortion or disturbances in time and space, this assumption is not valid, resulting in erroneous wind speed estimates. The magnitude of this error was evaluated by comparing LiDAR measurements with those obtained using a cup anemometer mounted on a meteorological mast and detailed analysis of line-of-sight wind speeds. A factor that expresses the nonuniformity of wind speed in the horizontal measurement plane of vertical-profiling LiDAR is proposed to estimate the errors in wind speed. The possibility of measuring and evaluating various wind characteristics such as flow inclination angles, turbulence intensities, wind shear and wind veer, which are important for wind turbine design and for wind farm operation is demonstrated. However, additional evidence of actual field measurements on wind farms in areas with complex terrains is required in order to obtain more universal and objective evaluations.

scholarly journals Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements

2020 ◽  
Vol 12 (6) ◽  
pp. 2467 ◽  
Author(s):  
Fei Zhao ◽  
Yihan Gao ◽  
Tengyuan Wang ◽  
Jinsha Yuan ◽  
Xiaoxia Gao
Keyword(s):  
Wind Speed ◽  
Wind Shear ◽  
Wind Farm ◽  
Wind Farms ◽  
Field Measurements ◽  
Development Trend ◽  
Asymmetric Distribution ◽  
Lidar Measurements ◽  
Wind Characteristics ◽  
The Development Trend

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.

scholarly journals Doppler wind lidar activities at Cabauw

2021 ◽  
Author(s):  
Steven Knoop ◽  
Fred Bosveld ◽  
Marijn de Haij ◽  
Arnoud Apituley
Keyword(s):  
Wind Speed ◽  
Wind Profile ◽  
Continuous Wave ◽  
Wind Farms ◽  
Data Availability ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Low Clouds ◽  
Wind Measurements ◽  
Wind Lidar

<p>Atmospheric motion and turbulence are essential parameters for weather and topics related to air quality. Therefore, wind profile measurements play an important role in atmospheric research and meteorology. One source of wind profile data are Doppler wind lidars, which are laser-based remote sensing instruments that measure wind speed and wind direction up to a few hundred meters or even a few kilometers. Commercial wind lidars use the laser wavelength of 1.5 µm and therefore backscatter is mainly from aerosols while clear air backscatter is minimal, limiting the range to the boundary layer typically.</p><p>We have carried out a two-year intercomparison of the ZephIR 300M (ZX Lidars) short-range wind lidar and tall mast wind measurements at Cabauw [1]. We have focused on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213m tall meteorological mast. We have found an overall availability of quality-controlled wind lidar data of 97% to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. This instrument is being deployed within North Sea wind farms.</p><p>Recently, a scanning long-range wind lidar Windcube 200S (Leosphere/Vaisala) has been installed at Cabauw, as part of the Ruisdael Observatory program [2]. The scanning Doppler wind lidars will provide detailed measurements of the wind field, aerosols and clouds around the Cabauw site, in coordination with other instruments, such as the cloud radar.</p><p>[1] Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219–2235, 2021</p><p>[2] https://ruisdael-observatory.nl/</p>

scholarly journals On wake modeling, wind-farm gradients and AEP predictions at the Anholt wind farm

2017 ◽  
Author(s):  
Alfredo Peña ◽  
Kurt Schaldemose Hansen ◽  
Søren Ott ◽  
Maarten Paul van der Laan
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Mesoscale Model ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Westerly Flow ◽  
Wake Model ◽  
Inflow Conditions ◽  
Speed Gradient

Abstract. We investigate wake effects at the Anholt offshore wind farm in Denmark. We perform the analysis with three commonly-used wake models; two engineering approaches (the Park and G. C. Larsen models) and a linearized Reynolds-averaged Navier-Stokes approach (Fuga). From analysis of SCADA and mesoscale model simulations, we show that for westerly flow in particular, there is a clear horizontal wind-speed gradient over the wind farm, which results from the effect of the land nearby. We also show that for annual energy production estimates, in which a wake model is run with inflow conditions derived from mesoscale model outputs, accounting for the horizontal wind-speed gradient gives nearly the same results as averaging all the wake-free wind climates at the turbines' positions or using the wind climate of a position in the middle of the wind farm. However, annual energy production estimates can largely differ when using wind climates that are strongly influenced by the wind-speed gradient. When looking at westerly flow wake cases, where the impact of the wind-speed gradient is largest, the wake models agree with the SCADA fairly well; when looking at a southerly flow case, where the wake losses are highest, they tend to underestimate the wake loss. With the mesoscale-wake model setup, we are also able to estimate the capacity factor of the wind farm rather well when compared to that derived from the SCADA. Finally, we estimate the uncertainty of the wake models and some of its variants by bootstrapping the SCADA. The models tend to underestimate the wake losses and the engineering wake models are as uncertain as Fuga. These results are specific for this wind farm, the available dataset, and the derived inflow conditions.

scholarly journals On wake modeling, wind-farm gradients, and AEP predictions at the Anholt wind farm

2018 ◽  
Vol 3 (1) ◽  
pp. 191-202 ◽  
Author(s):  
Alfredo Peña ◽  
Kurt Schaldemose Hansen ◽  
Søren Ott ◽  
Maarten Paul van der Laan
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Wind Farm ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Westerly Flow ◽  
Wind Climate ◽  
Mesoscale Simulations ◽  
Inflow Conditions ◽  
Speed Gradient

Abstract. We investigate wake effects at the Anholt offshore wind farm in Denmark, which is a farm experiencing strong horizontal wind-speed gradients because of its size and proximity to land. Mesoscale model simulations are used to study the horizontal wind-speed gradients over the wind farm. From analysis of the mesoscale simulations and supervisory control and data acquisition (SCADA), we show that for westerly flow in particular, there is a clear horizontal wind-speed gradient over the wind farm. We also use the mesoscale simulations to derive the undisturbed inflow conditions that are coupled with three commonly used wake models: two engineering approaches (the Park and G. C. Larsen models) and a linearized Reynolds-averaged Navier–Stokes approach (Fuga). The effect of the horizontal wind-speed gradient on annual energy production estimates is not found to be critical compared to estimates from both the average undisturbed wind climate of all turbines' positions and the undisturbed wind climate of a position in the middle of the wind farm. However, annual energy production estimates can largely differ when using wind climates at positions that are strongly influenced by the horizontal wind-speed gradient. When looking at westerly flow wake cases, where the impact of the horizontal wind-speed gradient on the power of the undisturbed turbines is largest, the wake models agree with the SCADA fairly well; when looking at a southerly flow case, where the wake losses are highest, the wake models tend to underestimate the wake loss. With the mesoscale-wake model setup, we are also able to estimate the capacity factor of the wind farm rather well when compared to that derived from the SCADA. Finally, we estimate the uncertainty of the wake models by bootstrapping the SCADA. The models tend to underestimate the wake losses (the median relative model error is 8.75 %) and the engineering wake models are as uncertain as Fuga. These results are specific for this wind farm, the available dataset, and the derived inflow conditions.

scholarly journals Documenting Wind Speed and Power Deficits behind a Utility-Scale Wind Turbine

2013 ◽  
Vol 52 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Brian D. Hirth ◽  
John L. Schroeder
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Large Variability ◽  
Cost Of Energy ◽  
The Mean ◽  
Utility Scale ◽  
Turbine Wake

AbstractHigh-spatial-and-temporal-resolution radial velocity measurements surrounding a single utility-scale wind turbine were collected using the Texas Tech University Ka-band mobile research radars. The measurements were synthesized to construct the first known dual-Doppler analyses of the mean structure and variability of a single turbine wake. The observations revealed a wake length that subjectively exceeded 20 rotor diameters, which far exceeds the typically employed turbine spacing of 7–10 rotor diameters. The mean horizontal wind speed deficits found within the turbine wake region relative to the free streamflow were related to potential reductions in the available power for a downwind turbine. Mean wind speed reductions of 17.4% (14.8%) were found at 7 (10) rotor diameters downwind, corresponding to a potential power output reduction of 43.6% (38.2%). The wind speed deficits found within the wake also exhibit large variability over short time intervals; this variability would have an appreciable impact on the inflow of a downstream turbine. The full understanding and application of these newly collected data have the potential to alter current wind-farm design and layout practices and to affect the cost of energy.

scholarly journals Applications of satellite winds for the offshore wind farm site Anholt

2018 ◽  
Vol 3 (2) ◽  
pp. 573-588 ◽  
Author(s):  
Tobias Ahsbahs ◽  
Merete Badger ◽  
Patrick Volker ◽  
Kurt S. Hansen ◽  
Charlotte B. Hasager
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Farms ◽  
Offshore Wind ◽  
Horizontal Wind ◽  
Offshore Wind Energy ◽  
Site Assessment ◽  
Scada System

Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other and in the lee of large land masses. Synthetic aperture radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open-access data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free-stream and waked conditions. Spatial wind speed variations along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

scholarly journals Comparison of Large Eddy Simulations of a convective boundary layer with wind LIDAR measurements

2012 ◽  
Vol 8 (1) ◽  
pp. 83-86 ◽  
Author(s):  
J. G. Pedersen ◽  
M. Kelly ◽  
S.-E. Gryning ◽  
R. Floors ◽  
E. Batchvarova ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Geostrophic Wind ◽  
Large Eddy Simulations ◽  
Horizontal Wind ◽  
Convective Boundary ◽  
Horizontal Wind Speed ◽  
Large Eddy ◽  
Lidar Measurements ◽  
Wind Lidar

Abstract. Vertical profiles of the horizontal wind speed and of the standard deviation of vertical wind speed from Large Eddy Simulations of a convective atmospheric boundary layer are compared to wind LIDAR measurements up to 1400 m. Fair agreement regarding both types of profiles is observed only when the simulated flow is driven by a both time- and height-dependent geostrophic wind and a time-dependent surface heat flux. This underlines the importance of mesoscale effects when the flow above the atmospheric surface layer is simulated with a computational fluid dynamics model.

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