With the increasing number of wind farm projects, a growing interest is seen towards the extension of wind turbine durability and the optimisation of energy production. A promising technology in this direction, is the use of lidars for remote sensing of wind fields and particularly for lidar-assisted control of wind turbines. Many investigations have been carried out to study the performance of lidars for measuring global wind statistics and test out lidar-assisted control strategies. However there appears to be less research efforts towards identifying and characterising localised and specific flow structures within the wind field, which is the aspect of focus in this study.

Whilst the main objective is to detect flow structures, this study also dives into the lidar measurement process. A striking feature of this process is the extensive data processing procedure applied to reduce noise and provide a final output in the form of the line-of-sight velocity. As this consists on relatively large data reduction and condensing steps, the question therefore arises, is useful information lost during this process?

To investigate the different stages of the lidar measurement process, a continuous wave lidar emulator was developed and served as the main tool for simulating lidar operation under controlled conditions. The first part of the investigation was performed on the Lamb-Oseen vortex, and was aimed at finding traces of the vortex within the lidar outputs. Besides, the line-of-sight velocity output, the Doppler spectrum was also analysed in a statistical sense with the use of moments. Three main indicators of the presence of the vortex were identified in the velocity envelope and the variations of the Doppler spectrum standard deviation and skewness across the vortex core. Tests were performed to see the effects of varying conditions (core size, noise, line-of-sight effects, etc.) on these patterns. From these sensitivity tests, a possible approach at characterising the vortex position, core radius and circulation was formulated. Further testing was then performed by adapting the methods used to a LES simulated wind turbine tip vortices.

In all tests performed, it is clear that the most detrimental obstacle to reliable vortex detection arises from the surrounding flow field which distorts the structure of the vortex and hence the regularity of the patterns identified within the lidar measurement process. An additional barrier are measurement process noise sources which tend to damp the signal rather than distort it, thus weakening but preserving the general shape of the identified features.

Applicability of the detection method developed is possible, but challenging in real operating conditions, due to the noisy background wind field and the ignorance of the approximate vortex location in the wind inflow. However, with continued and improved future studies, the approach shown may result in a suitable way of determining wake regions from the detection of tip vortices, thus providing a valuable input for wind turbine control and wind farm power optimisation.