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.

This thesis investigates terrain ruggedness characterization based on low-order terrain statistics. The statistics in question are the root-mean-square height and slope where the slopes are calculated both directly using the power spectral density and indirectly via a finite difference scheme. These statistics are, in contrast to the ruggedness index, shown to be close to independent of the input map resolution making them more suitable for future use due to the increased availability of high quality maps. Furthermore, these metric scan be bridged to the ruggedness index through strong correlations, thereby making implementation much simpler. The low-order statistics provide a strong, future-proof alternative to the current terrain ruggedness index and early investigations in this report have shown promise in the development of sector-wise correlations. Such correlations could yield directional correction factors as opposed to the current omnidirectional ∆RIX correction factor.

Modern multi-megawatt wind turbines are tall and may reach heights of 200 meter. Tall wind turbines require measurements above typical measurement mast height. As tall measurement masts are expensive and cumbersome to install, wind measurements at hub height are scarce. Currently, the use of lidars in wind measurement campaigns is increasing. Lidars provide wind measurements over the full wind turbine rotor, but lidars are not always available for an extended period. This thesis investigates the possibility of applying the measure-correlate-predict (MCP) method to short-term lidar measurements in order to extrapolate wind shear statistics.

The first step was to compare the wind shear derived from lidar measurements with wind shear derived from mast measurements because the instruments have a different measurement principle. Wind data from mast-lidar pairs at Høvsøre (DK) and Breezanddijk (NL) are used for the analyses. Subsequently, a detailed study of the seasonality of wind shear, as the seasonality becomes a concern
when the measurement period is shorter than one year. The MCP model has been implemented and validated using the commercially available software WindPRO. The data from the measurement sites have been used to test the new approach of estimating the mean wind shear exponent using short-term lidar measurements and MCP. Lastly, the uncertainty in the wind shear exponent is propagated
to uncertainty in the annual energy production.

Although the wind shear statistics are subject to seasonality, this thesis shows that the proposed method has the potential to significantly reduce the error in the estimate of the mean wind shear exponent. As the error in the mean wind shear exponent is decreased, the uncertainty in the vertical extrapolation of the wind resource can be reduced. This reduction ultimately leads to a decrease of the uncertainty in the annual energy production.