Abstract
Scanning Doppler lidars are the best tools for acquiring 3D velocity fields of full scale wind turbine wakes, whether the objective is a better understanding of some features of the wake or the validation of wake models. Since these lidars are based on the Doppler effect, a single scanning lidar normally relies on certain assumptions when estimating some components of the wind velocity vector. Furthermore, in order to reconstruct volumetric information, one needs to aggregate data, perform statistics on it and, most likely, interpolate to a convenient coordinate system, all of which introduce uncertainty in the measurements. This study simulates the performance of a virtual lidar performing stacked step-and-stare plan position indicator (PPI) scans on large-eddy simulation (LES) data, reconstructs the wake in terms of the average and the standard deviation of the longitudinal velocity component, and quantifies the errors. The variables included in the study are as follows: the location of the lidar (ground-based and nacelle-mounted), different atmospheric conditions, and varying scan speeds, which in turn determine the angular resolution of the measurements. Testing different angular resolutions allows one to find an optimum that balances the different error sources and minimizes the total error. An optimum angular resolution of 3 ∘ has been found to provide the best results. The errors found when reconstructing the average velocity are low (less than 2% of the freestream velocity at hub height), which indicates the possibility of high quality field measurements with an optimal angular resolution. The errors made when calculating the standard deviation are similar in magnitude, although higher in relative terms than for the mean, thus leading to a poorer quality estimation of the standard deviation. This holds true for the different inflow cases studied and for both ground-based and nacelle-mounted lidars.
Highlights
Wind power growth worldwide is a result of an ever increasing demand for renewable energy
A careful evaluation of wind resources is needed for an accurate estimation of produced power from a future wind farm [2,3], and the power losses and increased fatigue loads associated with the wind turbine wakes [4]
From the results presented it can be concluded that the best option to minimize only the error associated with the assumption of unidimensional average flow is to use a nacelle-mounted lidar, it does not give a very significant advantage since both lidar positions provide acceptably low errors for most analysis purposes
Summary
Wind power growth worldwide is a result of an ever increasing demand for renewable energy. With upper limits of rated power for single turbines reaching the order of 10 MW, the common solution to keep increasing the generated power is to install a larger number of wind turbines. Most of the wind turbines are affected by the wake flow of others, resulting in a reduction of incoming wind speed and an increase of turbulence [1]. A careful evaluation of wind resources is needed for an accurate estimation of produced power from a future wind farm [2,3], and the power losses and increased fatigue loads associated with the wind turbine wakes [4]. Accurate wake models are needed to that end, and the location of each of the wind turbines inside the farm needs to be optimized in order to minimize the losses [5,6]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
