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Aerial view of the experimental field. Lidar 1 and Lidar 2 are represented with a triangle and a square respectively. White circles indicate the considered wind farm while the dark ones other turbines in the vicinity. The position of the instrumented mast can be identified by a white cross.
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Standard anemometry or vertical profiling remote sensing are not always a convenient approach to study the dynamics of wind turbines wake. One or more lidar windscanner can be applied for this purpose. In this paper a measurement strategy is presented, which permits the characterization of the wake dynamics using two long range wind lidars operated...
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... experiments took place in a wind farm located in the countryside at the border between Germany and Den- mark. The wind farm consists of three 6 MW turbines with a 126 m rotor diameter D mounted on a 100 m high tower. They are indicated with white circles in Fig. 1, while dark circles are adopted for other turbines in the area. Two long range scanning lidars of type Windcube WLS200S, manufactured by Leosphere and specified in Table 1 were deployed to this wind farm. They are indicated as Lidar 1 and Lidar 2 and can be identified in Fig. 1 by a triangle and a square respectively. The former is ...
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... on a 100 m high tower. They are indicated with white circles in Fig. 1, while dark circles are adopted for other turbines in the area. Two long range scanning lidars of type Windcube WLS200S, manufactured by Leosphere and specified in Table 1 were deployed to this wind farm. They are indicated as Lidar 1 and Lidar 2 and can be identified in Fig. 1 by a triangle and a square respectively. The former is located at about 1040 m from the central turbine of the considered wind farm, the latter at about 740 ...
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... at which the strongest signal is retrieved is even- tually compared to the reference azimuth between the li- dar scanner and the turbine obtained from the GPS mea- surement. This method is expected to provide an uncer- tainty in the order of ±1 ° for the data acquired during this measurement campaign. In the wind farm, marked as a white cross in Fig. 1, a 100 m high meteorologi- cal mast was available too. Its instrumentation included a top cup-anemometer, a series of four cup anemome- ters and three wind vanes. In this research only the top anemometer and the wind vane mounted on the boom at 95.9 m directed to 103 ° from North were applied. A detailed description of these ...
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Citations
... Commercial scanning lidar systems allow to measure the line-ofsight (LOS) component of the wind vector on several hundred positions along the emitted laser beam and to orientate the beam 80 in any direction. Scanning lidars have enabled many new insights in different fields of wind energy research, like wind turbine wakes (Käsler et al., 2010;Trabucchi et al., 2014), wind farm cluster wakes , resource assessment in complex terrain (Menke et al., 2020) and minute-scale wind power forecasts (Theuer et al., 2020b). ...
The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global blockage effect, in relation to atmospheric stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global blockage effect which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a significant decrease in wind speed at platform height in front of the wind farm of 4.5 % within an accuracy range between 2.5 % and 6.5 % with the turbines operating at high thrust coefficients in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions we identified no wind speed deficit. We discussed the significance of our measurements, possible sources of error in long-range scanning lidar campaigns and give recommendations how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We conclude that global blockage is dependant on atmospheric stratification.
... One of the most promising measuring devices for understanding wakes in free-field conditions is wind-speed Doppler light detection and ranging (LiDAR). LiDARs are configured as continuous-wave (CW) devices for short distances (<200 m) [13] or as pulsed systems for longer distances (<6000 m) [14]. Though LiDAR is commonly used for free-field measurements, Van Dooren et al. [15] took LiDAR measurements in a wind tunnel under controlled conditions in order to investigate wake behaviour using a synchronised dual-short-range LiDAR setup. ...
... They found that the error in the determination of the radial velocity consists of a random error due to measurement inaccuracies caused by the speckle effect and detector noise, a systematic error due to the frequency shift of the laser, non-linear amplifiers, digitising errors and non-ideal noise statistics, and direction errors due to the imperfect adjustment of the LiDAR system and/or scanner movement. Together, these errors cause a projection error, like that described in Equation (14). Träumner et al. [35] used simulated data to investigate the ability of dual-Doppler LiDAR systems to estimate turbulence length scales. ...
... Below, we will calculate the average velocity and its standard deviation in the u direction. This calculation applies Equation (14) to the time series of LOS velocities of each point within the measurement area. The resulting statistics do not allow us to reconstruct the exact streamwise wind-speed component statistics. ...
Measurement campaigns in wind energy research are becoming increasingly complex, which has exacerbated the difficulty of taking optimal measurements using light detection and ranging (LiDAR) systems. Compromises between spatial and temporal resolutions are always necessary in the study of heterogeneous flows, like wind turbine wakes. Below, we develop a method for space-time conversion that acts as a temporal fluid-dynamic interpolation without incurring the immense computing costs of a 4D flow solver. We tested this space-time conversion with synthetic LiDAR data extracted from a large-eddy-simulation (LES) of a neutrally stable single-turbine wake field. The data was synthesised with a numerical LiDAR simulator. Then, we performed a parametric study of 11 different scanning velocities. We found that temporal error dominates the mapping error at low scanning speeds and that spatial error becomes dominant at fast scanning speeds. Our space-time conversion method increases the temporal resolution of the LiDAR data by a factor 2.4 to 40 to correct the scan-containing temporal shift and to synchronise the scan with the time code of the LES data. The mean-value error of the test case is reduced to a minimum relative error of 0.13% and the standard-deviation error is reduced to a minimum of 0.6% when the optimal scanning velocity is used. When working with the original unprocessed LiDAR measurements, the space-time-conversion yielded a maximal error reduction of 69% in the mean value and 58% in the standard deviation with the parameters identified with our analysis.
... As the temporal resolution of Doppler lidars is of the order of seconds or even less, it is even possible to derive all three components of atmospheric turbulence in an air volume depending on the scan strategy. The dual-or triple-Doppler lidar retrieval has been utilized, e.g., in wind energy applications to study wakes behind wind turbines (e.g., [112,118,119]) and for the assessment of wind conditions at a bridge in a Norwegian fjord, (e.g., [120]). During the recent ScaleX [25] and Perdigão [121] field campaigns, the technique was applied to study flow patterns over mountainous terrain. ...
Mountainous areas require appropriate measurement strategies to cover the full spectrum of details concerning the energy exchange at the Earth's surface and to capture the spatiotemporal distribution of atmospheric dynamic and thermodynamic fields over them. This includes the range from turbulence to mesoscale processes and its interaction. The surface energy balance needs appropriate measurement strategies as well. In this paper, we present an overview of important experiments performed over mountainous terrain and summarize the available techniques for flow and energy measurements in complex terrain. The description includes ground-based and airborne in situ observations as well as ground-based and airborne remote sensing (passive and active) observations. Emphasis is placed on systems which retrieve spatiotemporal information on mesoscale and smaller scales, fitting mountainous terrain research needs. Finally, we conclude with a short list summarizing challenges and gaps one faces when dealing with measurements over complex terrain.
... The measurements described in the previous section are suitable for the evaluation of wake models. For instance, Trabucchi et al. [22] and Aitken et al. [12] used simple analytical models to estimate the main characteristics of wakes measured with lidars. In our study, we addressed the model proposed by Bastankhah and Porté-Agel [17]. ...
... Similarly as in [12,22,23], we fit the model of Eq. 5 to the normalized wake deficit v D obtained subtracting the average horizontal wind speed v H from the corresponding inflow vertical profile v in : ...
Commonly, wake models are calibrated in wind tunnels or using flow simulations with a wide degree of physical details. In general, it is assumed that these methods cannot fully reproduce the real operating conditions of wind turbines. This research aims at investigating the calibration of an analytical single wake model in relation to full-scale measurements. Within this scope, we fitted the wake model to wake measurements realised with a lidar installed on the nacelle of an offshore wind turbine. We studied the parameters returned by the fit separating cases at different levels of atmospheric turbulence and thrust on the wind turbine rotor. Comparing the results with a published calibration based on few LES wind fields representative for partial load conditions, we achieved good agreement when the considered wind turbines operated in similar conditions. For other situations, i.e. at full load, we found different calibrations of the model parameters. Our results show that and how nacelle-based lidar measurements can be complementary in the development of wake models.
... Recording measurement data needs a carefully planned measurement campaign, the selection of suitable instruments with sufficient resolution for the desired purpose and an adequate measurement period. In recent years, the scanning aerosol heterodyne Doppler LiDAR-hereafter LiDAR-has become a standard device when flexible, versatile measurements are needed that go beyond standard point measurements in the wind energy sector [1][2][3][4][5][6]. Due to the measurement method of pulsed devices, it is possible to capture a plurality of quasi-instantaneously measurements along the laser beam. ...
... In the framework of the German research project "GW Wakes", three scanning long-range Doppler LiDAR systems of type Leosphere Windcube WLS-200S [6] were operated in the offshore wind farm "alpha ventus" in the German North Sea. The wind farm comprises six 5 MW wind turbines Senvion 5 M with rotor diameter of D S = 126 m and hub height of h S = 92 m that are located in the northerly two rows and six 5 MW wind turbines Adwen AD5-116, formerly called M5000-116, with rotor diameter of D A = 116 m and hub height of h A = 90 m in the two southerly rows ( Figure 6). ...
Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.
... This results in a fluctuation of the wind component aligned with the rotor axis which is related to the wake dynamics. An approach to characterize the described fluctuations is proposed in [10], where long range lidar measurements are performed staring the laser beam at about hub height, diagonally across the wake. With this configuration, the necessity of an inclination about the axial wind direction has the drawback that only the wind turbulent fluctuations aligned with the lidar line of sight (LOS) can be measured. ...
... At the opposite edge, about 4 D downstream, the range characterized by the increased PSD is almost twice as wide. This results are in accordance with [10], where a similar behaviour has been observed between 1.5 D and 4 D downstream. Figure 11 offers a closer view of the PSD of the LOS measurements evaluated at the center of the wake 3.5 D downstream and at the two opposite sides of the wake located at 2.7 D and 4.2 D respectively. The same graphic includes the PSD calculated in the free stream. ...
The validation of dynamic wake meandering models by full field measurements is an important but challenging task. Recently a new approach has been proposed, where a long range lidar was employed to analyse the power spectral density of the line of sight wind component measured across the wake trajectory. The method is promising, but the number of useful time series within a measurement campaign depends to a large extent on a favourable geometrical setup of the lidar position and the wind direction. In the first part of the paper the approach is further investigated. To this avail, lidar simulations based on large eddy simulation results are analysed. In the second part, the approach is applied to real measurement data from a campaign with a long range lidar windscanner in the offshore wind farm "alpha ventus". Eventually results about the wake dynamics are discussed.
The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole,
i.e. the global-blockage effect, in relation to atmospheric-stability estimates and wind farm operational states. We measured the inflow of a
400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical
variability of different lidar scans at comparable measurement conditions was introduced, and an extensive uncertainty assessment of the averaged
wind fields was performed to be able to identify the global-blockage effect, which is small compared to e.g. wind turbine wake effects and ambient
variations in the inflow. Our results showed a 4 % decrease in wind speed (accuracy range of 2 % to 6 %) at transition piece height
(24.6 m) upwind of the wind farm with the turbines operating at high thrust coefficients above 0.8 in a stably stratified atmosphere, which
we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions and for situations with low thrust
coefficients (i.e. approx. 0 for not operating turbines and ≤ 0.3 for turbines operating far above rated wind speed) we identified no wind
speed deficit. We discussed the significance of our measurements and possible sources of error in long-range scanning lidar campaigns and give
recommendations on how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for
the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by
planar lidar wind field measurements. We further conclude that global blockage is dependent on atmospheric stratification.
To assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Department of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling the assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range but provide more data during nonprecipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as radio acoustic sounding systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. Finally, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling the resolution of finescale processes critical to understanding inhomogeneous boundary layer flows.
