Normalized-wake characterization by long-range lidars

Slide presentation on Priscila Orozco's PhD project entitled "Normalized-wake characterization by long-range lidars" given on the EAWE Phd Seminar 2020. This PhD project is inserted in the LIdar Knowledge Europe (LIKE) project H2020-MSCA-ITN-20 (https://www.msca-like.eu) - funded by the European Union (grant no. 858358) - and developed at UL International GmbH under the supervision of Dr. Juan Trujillo. It is co-supervised by Prof. Dr. Martin Kühn from the ForWind Center for Wind Energy Research at the Carl von Ossietzky University of Oldenburg. Submitted abstract for the EAWE Phd Seminar 2020: Remote sensing techniques such as lidars have proven to be the most versatile tool for measuring wind turbine wakes due to their good resolution and flexibility (Wildmann et al., 2018). Indeed, they have aided in revealing that wakes exhibit large-scale dynamics and that the measured field depends on which frame of reference is used to analyze them (e.g. Trujillo et al., 2011; Beck et al., 2015). These studies have shown that there is the potential of filtering out the large-scale effects to obtain a wake field unaffected by external atmospheric conditions. This PhD project strives to evaluate this potential and its possible applications. The main objective is to prove to which extent external large-scale effects of the boundary layer can be separated from the far wake from full-field measurements. More specifically, we intend to reach a site-independent “normalized wake”, analogous to the wake in an environment influenced only by turbulence. In the first phase of this PhD, different analysis techniques will be tested on single-wake lidar measurements to obtain the normalized wake under different operational conditions. The PARKCAST project data from the Alpha Ventus wind farm, acquired by two nacelle-mounted long-range lidars (one looking upwind and the other downwind), will be used in this stage. By applying different wake tracking techniques, we first intend to estimate a local quasi-steady wake from the downwind-looking lidar measurements and represent it in a meandering frame of reference. Secondly, the aim is to spot the optimal analysis method to reach a normalized wake and perform a proof of concept of its application, i.e., turning it back to a local wake by considering the atmospheric conditions of the measurement site.LIdar Knowledge Europe (LIKE) project H2020-MSCA-ITN-20 (https://www.msca-like.eu) - funded by the European Union (grant no. 858358