This thesis investigates the potential use of the dynamic wake model FLORIDyn for Annual Energy Production (AEP) estimations in large-scale wind farm applications. Traditional low-fidelity wake models, based on steady-state assumptions, fall short in capturing the transient nature of wake interactions under varying wind conditions. FLORIDyn addresses this by incorporating time-dependent wake effects. This study includes a review of existing models, modifications to FLORIDyn, and calibration procedure using an inverse Bayesian approach with surrogate models. On the calibrated model, a validation study against SCADA data revealed that FLORIDyn's predictions did not outperform steady-state models, mainly due to biases in flow convection and wake accumulation inaccuracies. While FLORIDyn shows promise for small wind farms with large turbine spacings, it requires significant refinement for broader industry application. The thesis concludes with recommendations to address the current deficiencies in both the FLORIDyn model and validation methodology, aiming to enhance AEP estimation accuracy and provide more reliable wind farm performance assessments.

The global energy consumption relies heavily on fossil fuels, which are projected to be depleted by 2050. This makes it necessary to optimize renewable sources such as wind energy. However, wind energy faces challenges due to the unpredictability and turbulence of wind patterns. Traditional wind turbines are limited by their reactive approach to turbulence. To address this issue, the integration of lidar technology in wind turbines provides a predictive advantage, enhancing the control of wind parks by offering insights into incoming wind
disturbances and improving feedback mechanisms. A new method for adjusting ground-based lidar turbulence intensity measurements is investigated in this master’s thesis, aiming to reduce measurement uncertainties. A novel turbulence intensity equation, developed using perturbation theory, serves as the foundation for an adjustment model that has been validated against existing standards. This adjustment method was tested in both virtual environments and on an actual wind site in the Netherlands, demonstrating its effectiveness in reducing uncertainties associated with lidar data. The study also utilizes the findings of a joint-industry project to refine data precision and compares various adjustment methods, ultimately showing that the perturbation-based adjustment improves the alignment of lidar measurements with traditional met mast data. Furthermore, the thesis explores the acceptance of lidar technology through a model based on the technology acceptance
framework, revealing that reducing measurement uncertainties positively impacts the perceived usefulness and adoption of lidar systems. This research aims to enhance the accuracy and reliability of wind energy assessments using ground-based lidar, paving the way for broader adoption in the industry. Future work should focus on expanding the dataset, incorporating advanced machine learning techniques, and extending the turbulence
intensity equation to accommodate complex terrains, further reducing uncertainties in lidar measurements.

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.