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Multi WTG performance offshore, using a single scanning doppler lidar

  • Synsam Group Norway AS


In this paper we describe the setup and results of a scanning LiDAR measurement campaign at an offshore park, aiming at performing multiple power curves and characterizing the wind flow variation in the vicinity of the wind farm. The Scanning LiDAR is located on an offshore substation, 1.8 km at the West of the Anholt wind farm. The lidar has a nominal range of 3.5km, and scans towards the first row of wind turbines, measuring the inflow and wakes of three wind turbines. A vertical profiling lidar, also located on the substation, provides additional wind speed and direction measurements, and a wave buoy provides information about the water temperature and sea state. The data are processed in order to reconstruct velocity measurements at 2.5 rotor diameters in front of the turbines, allowing for multiple and concurrent power curve analysis. At the same time, the dataset also provide some novel insight into wake effects and turbulence outside and within the wake of a wind turbine.
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With further development of LiDAR technology wake measurements by use of LiDAR became of common interest in the wind energy community. To study new measurement strategies of scanning and nacelle LiDARs, in combination with already existing measurement principles of static LiDARs, Norcowe conducted in collaboration with the Energy research Centre of the Netherlands (ECN) the Wind Turbine Wake Experiment Wieringermeer (WINTWEX-W). In this study we use data from the static Windcubes V1 to illustrate a proof of concept of wake effects at 1.75 and 3.25 rotor diameter downstream distance. After validating Windcube data against sonic anemometers from the met mast, we compare downstream velocity deficits and turbulence intensities between measurements of static and scanning WindCubes. To further characterize single wind turbine wakes and their frequencies of occurrence we analysed the results in terms of atmospheric stability. Wake measurements are of great importance to further developing tools for optimising wind farm layouts and operations.
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Using scanning lidar wind turbine wakes can be probed in three dimensions to produce a wealth of temporally and spatially irregular data that can be used to characterize the wakes. Unlike data from a meteorological mast or upward pointing lidar, the spatial coordinates of the measurements are not fixed and the location of the wake also varies in three dimensions. Therefore the challenge is to provide automated detection algorithms to identify wakes and quantify wake characteristics from this type of dataset. Here an algorithm is developed and evaluated on data from a large wind farm in the Midwest. A scanning coherent Doppler wind lidar was configured to measure wind speed in the wake of a continuously yawing wind turbine for two days during the experiment and wake profiles were retrieved with input of wind direction information from the nearby meteorological mast. Additional challenges to the analysis include incomplete coverage of the entire wake due to the limited scanning domain, and large wind shear that can contaminate the wake estimate because of the height variation along the line-of-sight. However, the algorithm developed in this paper is able to automatically capture wakes in lidar data from Plan Position Indicator (PPI) scans and the resultant wake statistics are consistent with previous experiment's results.
In most of the present yield estimation models the inflow to offshore wind farm clusters is predicted to be uniform and exactly the same as the free, undisturbed wind far upstream from turbine locations (for winds coming from the sea). However, it has been observed that for certain wind directions the average wind speeds measured at turbine locations vary significantly along the outer edges of the farm. This phenomenon is attributed to flow field distortion caused by the wind farm itself. The existence of these phenomena, referred to as "wall effects", is confirmed and quantified in this work. The order of magnitude of power output variations along the outer edges of the cluster is estimated for the Horns Rev 1 offshore wind farm. The influence of wind direction, wind speed, turbulence intensity and atmospheric stability on wall effects is studied. The work also provides an explanation of the underlying physical mechanisms responsible for the observed variations in turbine power outputs. It has been discovered that the Coriolis force is one of the main driving factors of wall effects. Also, the impact of wall effects on farm's AEP is addressed. Since the analysis is based on wind farm SCADA data, methods for data treatment and ambient wind signal derivation as well as their limitations are briefly discussed.
Real world offshore power curve using nacelle mounted and scanning Doppler lidars
  • Wagner
(Wagner et al. 2015) Real world offshore power curve using nacelle mounted and scanning Doppler lidars. Wagner, Rozenn; Vignaroli, Andrea ; Courtney, Michael ; McKeown, Stephen ; Cussons, Robert ; Murthy, Raghu Krishna ; Boquet, Matthieu. 2015. European Wind Energy Association (EWEA).EWEA Offshore 2015 Conference, Copenhagen, Denmark, 10/03/2015.
Wind turbine wake detection with a single Doppler wind lidar. H Wang and R J Barthelmie
(Wang and Barthelmie 2015) Wind turbine wake detection with a single Doppler wind lidar. H Wang and R J Barthelmie 2015 J. Phys.: Conf. Ser. 625 012017
Poster session presented at European Wind Energy Conference & Exhibition
  • Matthieu Boquet
Boquet, Matthieu. 2014. Poster session presented at European Wind Energy Conference & Exhibition 2014, Barcelona, Spain.
Cost effective offshore wind measurement Couts et al., EWEA Resource Assessment
  • Couts
(Couts et al. 2015) Cost effective offshore wind measurement Couts et al., EWEA Resource Assessment 2015, Helsinki, Finland, 2-3 June 2015