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Riso Wake Lidar

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Data Provider: 

DTU Wind Energy / Stuttgart University, Department of Wind Energy (SWE)

Data accesibility: 

The test case is offered to participants of the IEA Task 31 Wakebench

Site Description: 

Wake velocity measurements have been recorded by a pulsed lidar system as part of a measurement campaign conducted from June 2011 to early January 2012 at the DTU Wind Energy, Risø Campus test site located on the south-east side of Roskilde Fjord in Denmark. It is a fairly flat  and homogeneous onshore terrain mainly characterized by grassland. This test site is made of 3 stall regulated turbines: a Tellus 95kW, a Vestas V27 and a Nordtank 500kW. A satellite picture of the terrain with nearby obstacles, and centered on the lidar mounted Nordtank 500kW is  shown in Fig. 1.

Figure 1: Satellite picture of the DTU Wind Energy, Risø Campus test site, centered on a lidar mounted 500 kW stall regulated Nordtank turbine. The turbine is equipped with 19.1 LM blades and a has a total rotor diameter of 41 m. Major obstacles are located in red, main flat terrain in green, the nearby fjord in blue and the main roads in yellow. Concentric circles, representing the five focus distances that the pulsed lidar measures simultaneously (as described in the Instrumentation section), are also drawn. The meteorological mast is indicated as ”M.M”, whereas the lidar mounted Nordtank turbine and other nearby turbines are also indicated. Source: Google Maps. Google and the Google logo are registered trademarks of Google Inc., used with permission.

Instrumentation: 

The Nordtank 500 kW turbine is equipped with a pulsed lidar mounted on the back of the nacelle and recording simultaneously 5 downstream wake cross sections ranging from 40 m to 200 m downstream. It corresponds to approximately 1 to 5 rotor diameters downstream. Each cross section is made of a Cartesian pattern of 7 by 7 measurement locations. An example of the mean wind field recorded by the pulsed lidar is shown in Fig. 2. A complete description of the lidar system and adaptation of the scanning pattern is available in [1] and [2].

Figure 2: Example of mean line-of-sight wind field recorded by the pulsed lidar system in the wake of a Nordtank 500 kW stall regulated turbine.

The ambient condition is monitored by the nearby and highly instrumented 56 m met mast located at bearing 283◦ and 91 m away from the Nordtank turbine. On this met mast, 6 cup anemometers, 3 three-dimensional sonics anemometers as well as several wind vanes and absolute temperature sensors are mounted at various heights covering the full extent of the Nordtank rotor. The 3D sonic anemometer sare further used to obtain an estimation of the atmospheric stability using the Obukhov length. Therefore, the impact of atmospheric stability on the wake profile may be investigated as part of the benchmarks associated with this test case. Additional relevant turbine characteristics such as generator type, nominal rpm, measured thrust and power curves, as well as design chord, twist and lift/drag coefficients distributions are available for the benchmark. All the sensors mounted on the met mast and turbine operational sensors are 35 hz Daqwin data stream synchronized and organized in a database located at DTU Wind Energy. A similar database gathers all the wake lidar measurements collected during the campaign.

Measurement Campaign: 

The wake measurements presented in this test case were recorded as part of a test campaign for a new pulsed lidar system developed at the Wind Energy Department of the University of Stuttgart (SWE). During this campaign, more than 11000 - 10 min datasets were collected with different purposes: wake deficit analysis, lidar calibration and validation through met mast correlation analysis, characterization of atmospheric turbulence, etc. There are several steps involved in processing the raw lidar measurements and producing the validation dataset in the present benchmark. These steps can be summarized as followed.

1) Filtering of raw measurements.
All wake recordings from the campaign are filtered in order to exclude 10 min time series that are not fulfilling a certain set of criteria. Three main criteria are used in this filtering: 1) the delay between reaching the scanner position and the center of acquisition should not exceed a quarter of the acquisition duration; 2) the Carrier to Noise Ratio (CNR) should remain above a certain threshold value which varies with the number of pulses per measurements and 3); the gaps between two valid measurements is too large or the overall data availability with a 10 min block is less than 90%. A complete description of filtering procedure and the resulting overall data availability is proposed in [3].

2) Line-of-sight velocity projection.
The line-of-sight velocities are projected along the main flow direction using a technique described in [4]. This projection assumes the flow to be horizontally homogeneous and that the vertical and lateral velocity components are very small compared to the streamwise one. It further allows a direct comparison of the streamwise velocity component with model predictions and a calibration/validation of the lidar measurements against met mast data. Results of the comparison between the lidar and the met mast measurements are available in [5], where good correlation is  observed. The results presented in [5] serve as validation of the current lidar set up.

3) Yaw / mounting misalignment.
Uncertainties related to the combined yaw / mounting misalignment are investigated. Such investigation is highly relevant in order to assess whether a correction of the wake central position is required. First of all, an estimation of the lidar mounting error angle, defined as the angle between the normal to the rotor and the laser beam at 0◦ tilt and pan angles, is performed. The methodology is based on the Dynamic Wake Meandering assumptions that the distribution of the lateral meandering of the wake follows a Gaussian distribution. This assumption is only valid for a large observation period, since wake meandering relates to the large scale atmospheric turbulence. Once the distribution of the lateral
wake center position is extracted using a tracking procedure similar to the one described in [6], a normal distribution is fitted and its mean and standard deviation are derived. A perfect mounting would result in a fitted normal distribution of zero mean. For the present campaign, the analysis in [7] revealed that the mounting error angle is in the order of 1◦. Additionally, the typical yaw misalignment of the Nordtank turbine is of the order of ±5◦ . Both yaw and mounting misalignments are assumed to be canceled out for long averaging time of the wake velocity and consequently no correction on the wake central position is performed in the present test case.

4) Normalization of wake velocity.
The determination of a reliable undisturbed inflow wind speed is crucial for the normalization of the wake velocity. The method that ensure the best time and spatial correlation is based on the direct use of the lidar measurements. Due to the restriction in lateral extent of the scanning area, the undisturbed velocity is extracted at the two most downstream cross section, where the lateral extent of the scanning region is much larger than the actual wake region. The mean wind speed at hub height is then interpolated and use for the normalization.

5) Wind speed bin averaging.
A wind speed bin averaging method is conducted on the valid wake lidar measurements with inflow direction ranging from 120◦ to 150◦ and wind speed from 4 m/s to 12 m/s. The wind speed bin size is 1 m/s, giving a total of 8 sets of wake measurements. The resulting bin-averaged  datasets include the average and standard deviation of the velocity deficit at each downstream cross section as function of lateral position. Additionally, mean value of the turbine thrust coefficient CT, the stream wise turbulence intensity at hub height TI_u and the mean shear exponent ν are available.

6) Estimation of the atmospheric stability.
An estimate of the atmospheric stability is derived from the Monin-Obukhov length L and the relation with stability classes as described in Gryning et al [8].

The friction velocity is obtained as the slope of the logarithmic fitting of the wind speed profile obtained from the measurements at several heights from the met mast. The mean kinematic heat flux w 'T0' is derived from the 3D sonic anemometer.

Remarks: 

The Risø Test Site case is proposed within the IEA Task 31 with the objective to facilitate and extend the validation of wake models to several simultaneous cross sectional measurements. Such measurement characteristics have a higher spatial resolution than conventional met mast based wake campaign such as the Sexbierum case. They offer a better characterization of the near wake flow field as well as the wake downstream transportation for up to 5 rotor diameters. The characterization of the atmospheric stability may give to the participants of this benchmark an additional important information concerning the ambient conditions at the test site, and may lead to reducing the uncertainties of the benchmark.
The present test case is supplemented with the measured power and thrust curve of the Nordtank turbine, described in the report by Hansen [9].

References: 

[1] A. Rettenmeier, J. Anger, O. Bischoff, M. Hofsaß, D. Schlipf, and I. Wurth. Nacelle-Based Lidar Systems. Summer School in Remote Sensing for Wind Energy, Boulder, Colorado, 2012.

[2] A. Rettenmeier, O. Bischoff, M. Hofsaß, D. Schlipf, and J. J. Trujillo. Wind Field Analysis Using A Nacelle-Based Lidar System. In Scientific proceedings. EWEC 2010 Warsaw.

[3] D. Schlipf. Analysis of the SWE-scanner on the Risø Nordtank. Technical report, Stuttgart Wind Energy (SWE), 2011.

[4] E. Machefaux, N. Troldborg, G. C. Larsen, J. Mann, and H. Aa. Madsen. Experimental and Numerical Analysis of Wake to Wake Interaction in Wind Farms. In Scientific proceedings, pages 100–104. EWEC 2012 Copenhagen.

[5] A. Rettenmeier, O. Bischoff, D. Schlipf, J. Anger, M. Hofsaß, P.W. Cheng, R. Wagner, M. Courtney, and J. Mann. Turbulence and wind speed investigations using a nacelle-based lidar scanner and a met mast. Presentation Proceedings of EWEA 2012 conference, Copenhagen.

[6] F. Bingol, G. C. Larsen, and J. Mann. Lidar Measurements of Wake Dynamics, Part 1: One-dimensional scanning. Wind energy 13, 51-61, 2010.

[7] E. Machefaux, G. C. Larsen, K.S. Hansen, P-E. Réthoré, D. Schlipf, and M.P. van der Laan. Nacelle-based wake lidar measurements for wake model performance assessment. In submission to Journal of Wind Energy, 2013-2014.

[8] S.E Gryning, H. Jørgensen, S. Larsen, and E. Batchvarova. The wind profile up to 300 meters over flat terrain. In Proceedings of The Science of Making Torque from Wind, 2007.

[9] K. S. Hansen. Analysis of nordtank 500kw measurements. Technical report, DTU Wind Energy - Wind turbine measurement technique, 2010.

NDA: 

NDA for accessing the test case data. Files can be added in the files section of the test case.
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