In this thesis, the effect of a surging and pitching motion of a floating wind turbine on its wake as well as the effect of a surging, pitching and yawing motion on a stationary downstream turbine are analysed. Two experiments are conducted in order to address both points. The turbine models used are scale models of the DTU 10 MW turbine, and the upstream turbine is placed onto a kinematic robot which can move in all 6 degrees of freedom. The first objective is studied using Particle Tracking Velocimetry and the second objective using load cell measurements. It is found that pitch has a larger effect on the flow field in the wake than surge and that the effects of low frequency motion are more visible than those of high frequency motion. Moreover, it is found that the tracing particles are concentrated in specific areas, affecting the accuracy of the results. In future research, it should be examined why this is the case and how it can be improved. Additionally, low frequency pitch and surge had the greatest effect on the loads. A clear sinusoidal motion is visible and the mean thrust, torque, and power is increased. The effect of the high frequency motions is not visible in the results. In future studies, the wake behind a downstream turbine can also be examined using Particle Tracking Velocimetry. Moreover, the downstream turbine can also be moving rather than standing still and more movements of the turbine can be examined.

A key hurdle to meet the ambitious renewable energy targets is the high levelized cost of offshore wind energy. With Operations and Maintenance (O&M) actions making up more than a quarter of the total costs, substantial research efforts have been undertaken to optimize these actions. In line with this overarching goal, this project focuses on O&M actions, specifically optimizing time-based maintenance strategies to minimize costs. Literature reveals a variety of methods developed, such as Remaining Useful Life (RUL) models, degradation models, reliability models and maintenance models to simulate Wind Turbine (WT) lifetimes, O&M actions and calculate costs. A majority of these studies only look at
single failure modes (FM) or multiple independent FMs. In the field of wind energy, there is a significant lack of research on failure interactions and degradation dependence between different components or FMs. This project aims to build a model that accounts for dependencies in degradation between different FMs. This model is further combined with a maintenance model to simulate maintenance actions and estimate corresponding O&M costs.

The proposed degradation-based maintenance model builds on a simplified in-house model developed by the Wind Energy group at TNO. A Markov model, built in Python, is used to represent the degradation process. This is further combined with copula functions to represent degradation dependencies, using available copula packages in Python. Inspection and maintenance functions are then integrated into the degradation model to simulate O&M actions within the WT lifetime. Each action incurs either one or a combination of costs due to inspection, maintenance or operation, from WT downtime. An extensive number of time-based strategies are modelled, with varying inspection intervals for each. Finally, the accumulated lifetime costs are calculated and compared for the various maintenance strategies. The inspection interval with the minimum lifetime costs is selected as the optimum maintenance
strategy. In order to verify the validity of the proposed model, the results of an existing research paper on degradation modelling for WT blades are first replicated.

After successful verification, case studies are carried out to apply the model to the degradation processes of offshore wind turbine (OWT) hydraulic pitch systems. This is an extremely critical system, crucial for both WT operation and safety with the highest failure rates among all WT systems. The case studies selected in this work cover two pitch FMs: hydraulic fluid leakage and hydraulic valve wear. The model is applied to the case of single FMs and the case of multiple FMs, both independent and dependent. For both the single FM models, different optimum inspection intervals and lifetime costs are obtained. A higher failure rate for one of the FMs is the basis for the increased optimum lifetime cost and the decreased optimum inspection interval. Comparing the direct summation of the results of two single FM models with the multiple FM independent model outputs dissimilar results. The independent model leads to a reduced inspection interval and slightly higher lifetime costs. For the dependent
case, a 39% reduction in the lifetime costs and an increase in the optimal inspection interval is
observed compared to the independent case. This results in cheaper and less frequent O&M actions for the dependent case.

This work shows that, compared to the other case studies, modelling dependency between FMs allows for improved O&M actions, resulting in lower costs. This methodology has the potential to be used in future multi-component or multi-FM studies. The inclusion of degradation dependencies between components would better model the inter-dependencies between FMs and hence allow for improved optimization of O&M strategies.

As the demand for renewable energy rises, the impact of wake effects on wind farm performance and efficiency has become a primary focus for both industry professionals and academic researchers. However, to effectively mitigate these wake effects and improve wind farm efficiency, there is a critical need for an accurate wake representation. This requires well-established medium-fidelity wake models, which are currently lacking. To achieve this goal, it is essential to develop a wake model capable of accurately generating the axial wind profile within the wake. This model is crucial for precisely assessing the loads on downstream wind turbines and evaluating their energy production potential. Moreover, it provides valuable insights into optimising methods to mitigate wake effects and improve overall wind farm efficiency. For the model to be truly beneficial to the research effort and industry applications, it must demonstrate an increased accuracy with a limited increase in computational cost.

This thesis delves into medium-fidelity wake models to enhance the precision of calculations and predictions concerning wind turbine fatigue and aeroelastic loading. It specifically examines the use of a finite element method with Hermite interpolation basis functions as a numerical approach to solve the Ainslie wake model and achieve these objectives. Additionally, the study employs a downstream marching scheme to solve the partial differential equations and introduces the Newton-Raphson method to address non-linearities within the model. The potential benefit of using a finite element method lies in its potential for improved stability compared to the finite volume method, which shows satisfactory performance but limited stability, and superior performance compared to the spectral method, which has shown to exhibit poor conservative properties and instabilities for higher mode numbers. Furthermore, this thesis aims to bridge the gap between modelling and reality by implementing a pressure Poisson equation. Specifically, the focus has been on implementing the pressure component resulting from the forcing in the pressure Poisson equation.

Implementing Hermite interpolation basis functions posed several challenges. Increasing their order caused significant ill-conditioning and greater sensitivity to mesh quality, reducing their effectiveness for this application. Additionally, higher-order Hermite functions proved ineffective for solving the continuity equation due to the presence of odd basis functions. In contrast, piecewise linear basis functions have shown to be compatible with the continuity equation and were effective in solving the diffusion term. Despite this, stability issues emerged, likely due to the Ladyzhenskaya-Babuka-Brezzi condition not being met.

The implementation of forcing in the finite volume Ainslie wake model demonstrated mass and momentum conservation, and the axial velocity has been shown to align with literature. However, the dependency of the vortex strength on the domain size complicates the determination of a correct converged value for $\Gamma$. Furthermore, comparisons of the velocity field with the IEC 61400 standard implementation without the inclusion of a forcing term revealed that the forcing term implementation more accurately reflects the real flow dynamics, capturing both the blockage effect and the edge force effect.

Overall, this thesis demonstrates that the investigated method does not provide a stable solver for the Ainslie wake model. However, this does not rule out the potential of the finite element method for solving the Ainslie wake model. With adjustments to the basis functions and the application of Taylor-Hood elements, this method is still anticipated to deliver satisfactory results compared to existing methods. Furthermore, this thesis has laid the foundation for the inclusion of a Poisson solver. Specifically, it outlines the integration of pressure gradient resulting from actuator disk forcing and presents various formulations for the wind turbine forcing. However, further research is needed to address the calculation of the pressure induced by the velocity field.

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.

Rotor imbalances—such as mass imbalance, pitch misalignment, and yaw misalignment—are critical faults in wind turbine systems. These imbalances cause uneven load distribution on components, leading to excessive wear, failures, increased operational costs due to unplanned downtime, and reduced energy output. Despite advancements in monitoring technologies, current maintenance strategies in wind turbines still rely on time-based manual inspections, as they lack reliable automated detection systems. This thesis addresses the need for a more efficient fault detection framework by integrating already available drivetrain Condition Monitoring System (CMS) vibration signals— commonly used to detect drivetrain component failures, like in gears and bearings— with traditional SCADA data. The aim is to extract signal features that capture the system's dynamic behavior and effectively detect and diagnose rotor imbalances. Notably, this approach overcomes the limitation of current systems not having direct measurements from the blades by leveraging operational data already collected from wind turbines.

Building on prior research, the proposed approach combines frequency and time-domain analyses and focuses on two key data sources: drivetrain vibration measurements and rotor speed data from the SCADA system. A decoupled simulation framework integrates aeroelastic simulations from OpenFAST with a multi-body drivetrain model in SIMPACK, specifically for the 10 MW DTU reference wind turbine. The results show that drivetrain velocity signals, particularly in the side-to-side direction, are highly sensitive to rotor imbalances, enabling accurate trend analysis. Features such as peak amplitudes at 1P and 3P frequencies form the basis of the fault detection and diagnosis criteria proposed in this thesis. By using the median values of their distributions, imbalances can be effectively detected and diagnosed. This approach also supports the implementation of a decision tree framework for real-time fault classification across various operating conditions.

The methodology was tested under both above and below-rated wind speeds, first in steady-state conditions and then in turbulent inflow scenarios. Additionally, health state indicators are proposed to recognize fault severity levels by clustering median value features within predefined ranges for low, medium, and high severity. This comprehensive monitoring approach effectively tracks fault progression across the imbalance scenarios under study. As a result, the proposed method lays the foundation for a future data-driven system that can reduce reliance on manual inspections and provide a scalable solution for predictive maintenance in wind turbine operations.

FOWTs pose several control challenges. This work addresses reduced order modelling of FOWT to develop an MPC. They key objective is tackle the platform pitch instability while improving the performance in other KPIs. An existing reduced order model, which considers platform pitch and surge, and rotor speed DOFs is adapted and extended, with tower flexibility. The models are validated with open and closed-loop simulations in HAWC2, using the IEA-15MW RWT and the WindCrete spar-buoy floater. Based on these models, an ADKF is designed to estimate several unmeasured states and the wind speed. The MPC aims at reducing the platform pitch motion and tracking the reference rotor speed utilising collective blade pitch angle. The proposed holistic weight tuning procedure conveys the use of Pareto fronts and metrics from several relevant KPIs to guarantee an effective trade-off. Results show a major effects of the simulated wind conditions on the optimal tuning, therefore the process should account for realistic met-ocean conditions. The final tuning is compared to the baseline PI controller with torque compensation. Significant improvements are observed in terms of platform motion reduction in surge, pitch and roll, improved power quality, and greatly reduced fatigue and extreme loads on the tower base and shaft, under all simulated wind conditions. Limitations on rotor speed tracking near rated wind speed show that further development could lead to increase performance.

The wind turbine wake is a downstream region of kinetic energy and velocity deficit, higher turbulence and has a complex helical vortex structure. The wind turbine wake’s stability depends mainly on the ambient turbulence and the wind turbine tip- speed ratio. Researchers have developed several wake control techniques to mitigate this issue, such as static axial induction control by torque or pitch control, dynamic axial induction control by pitch control and actuating flaps or other actively controlled add- on devices. Some gaps are present in these methods, in terms of applicability of these techniques in terms of full scale validation and practicality to have the devices as add on to existing blades.

To address these research gaps, this study focuses on designing and evaluating segmented Gurney flaps (in line with the ECN (now TNO Wind Energy) patent by Edwin Bot and Arne Van Garrel) for wind turbine blades to enhance the wake recovery by inducing turbulence in the wake to excite the tip vortices. 4 Gurney flaps were attached in the tip region of each blade of a GE 3.8MW research wind turbine. Field tests were conducted in this study for the wind turbine wake (using a scanning LiDAR to scan a sector up to 5.5D downstream at different altitudes; with a scan time of roughly 3 minutes) and performance analysis (using 10 minute averaged measurement data). Free vortex wake simulations were conducted to validate the faster wake breakdown by the change in lift distribution upon addition of segmented Gurney flaps. Simulations using dynamic blade element momentum theory with IEC NTM inflow conditions from cut in to cut out wind speed were conducted to assess the structural impacts on the retrofitted wind turbine.

The field tests’ wake analysis was quantified with different wind speed, turbulence intensity, wind shear, wind direction conditions. Post processing of LiDAR data involved filtering, creating bin averaged data set, using Gaussian process regression and retrieval of the required wind component. The results show a consistent increase in wake recovery, generally at all downstream distances. The retrofitted configuration results are associated with a higher standard error because of a shorter testing period (due to increased noise levels) than the baseline configuration. The wake simulations indicate an earlier wake break down position, by roughly 2D.

The simulations indicate AEP increase for the retrofitted wind turbine to be roughly +0.2% (+50MWh), at the expense of increased blade and tower structural loads. The wind speed weighted damage equivalent blade flapwise bending moment was found to increase by roughly +2% to +4%; with peaks observed in partial load region of the wind turbine, associated with the operating angle of attack in this region.

This research is carried out in collaboration with Vattenfall, to investigate the performance of a 76-turbine onshore wind farm. Since the start of operation, the wind farm has not been reaching P50 production estimates. Over the first five years of operation, there has been a big average gap between Annual Energy Production (AEP) and long-term AEP P50 estimates. This thesis gives insights into the physical causes of this apparent underperformance. Conventional 10 minutes averaged data as well as data with a higher resolution, named high-frequency data, has been available for this purpose. Therefore, the research question to be answered is: “Can the underlying physical causes for the underperformance of the Pen y Cymoedd wind farm be found by the use of 10 minutes averaged and/or high-frequency data?”.

Answers to this research question were found by following a set-up methodology, partially based on International Electrotechnical Commission (IEC) standards and with an added purpose for high-frequency data. This analysis was carried out for 14 turbines divided over 3 turbine clusters, with each turbine cluster accompanied by a MET mast, performing independent site measurements. The analysis only considers unwaked turbine sectors. After filtering and correlating the data sets, a cloud analysis and overall & directional performance analysis were carried out to understand the influence of the site as well as the influence of turbine controls to underperformance. The cloud analysis investigated deviating turbine behaviour, where the overall & directional performance analysis focused on the influence of the site on turbine performance.

The set-up methodology and analysis led to the following findings: on average, after data filtering, wind turbines perform 95% compared to the performance numbers the manufacturer delivers. There is a big difference in performance between the two turbine types at the site. Overall, the turbulence intensity (TI) class A turbines are performing better than the TI class B turbines. From the overall & directional performance analysis, bad performance (>-5% compared to the warranted performance) was seen for sectors with a complex orography, as well as for sectors with few elevation deviation or forestry. The cloud analysis revealed performance improvement potential for multiple turbine control regions. The high-frequency data gave additional unique insights in turbine performance, only visible in higher resolution. Moreover, it clarified turbine behaviour visible in 10 minutes averaged data.

The electricity generation from wind has seen significant growth over the past two decades with a compound annual growth rate of over 21%, and this trend is projected to continue, aligning with the goals of the European Green Deal. In the context of offshore wind turbines, operational and maintenance costs can account for up to 30% of the total costs throughout the project's lifecycle. This highlights the need for the development of efficient condition monitoring methods aimed at mitigating maintenance expenses.
The gearbox is one of the most failure-prone components in wind turbines, leading to extended downtimes and substantial financial outlays. When the components of the gearbox degrade, the heat generated within the gearbox increases. This increase leads to higher oil temperatures, making the temperature signal a suitable indicator of the gearbox health condition. The main objective of this thesis is to design a physics-based normal behaviour model (NBM) for the estimation of the wind turbine gearbox oil temperature and to use field data to validate its effectiveness. The energy conservation principle is applied to the gearbox to formulate an equation for the calculation of the gearbox oil temperature. To account for some unidentified design specifications of the gearbox, the equation is fitted to available historical data from the Supervisory Control and Data Acquisition (SCADA) system to determine the unknown parameters. The model uses as input an array of operational measured signals available in the SCADA system, including rotor speed, power output, nacelle temperature and inlet oil temperature, which is the temperature of the oil after running through the cooling system.
A case study demonstrates the model's effectiveness in accurately predicting the gearbox oil temperature with a mean absolute percentage error of 0.75%. In order to investigate how well the physical characteristics of the gearbox are represented by the model, the coefficients of the energy balance equation derived from fitting it to the field SCADA data are compared to the parameters calculated using known characteristics of a reference gearbox. The comparison results indicate that the model's parameters are generally within the same order of magnitude as the reference values. To provide a benchmark, the performance of the physics-based model is also compared to that of two data-driven models using artificial neural networks (ANNs) for temperature prediction proposed in the literature. The results show that the proposed physics-based model outperforms both ANN models, achieving 14% and 59% lower root mean squared error (RMSE) and a reduction in time required for training of 70% and 95% when compared to the two ANN NBMs respectively.

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.

Wind farm interactions with the surrounding airflow leads to a reduction in velocity greater than the linear sum of single turbine inductions and is known as global or upstream blockage. The mechanisms and magnitude of global blockage effect are not yet fully understood. Models to simulate upstream blockage to improve efficiency estimates and better understand global blockage have thus far not been refined. The aim of this research is to investigate the sensitivity of upstream blockage to the numerical configuration of CFD simulations to improve model accuracy and understanding of global blockage.

RANS simulations are executed under steady state conditions with a k − ϵ turbulence model. A neutral stability, pressure-driven atmospheric boundary layer is modelled with fully developed uni-directional flow. Wind farms are modelled as actuator discs with 5 rows of turbines on flat terrain. Streamwise and spanwise spacing is set to 7 turbine diameters (D) and 5D respectively. Global blockage is measured against the induction of a single turbine at 2.5D upstream. Variables investigated include the domain height, lateral extent, inlet and outlet distances from the wind farm.

Domain heights ranging from 5D to 25D are investigated for change in magnitude and scale of upstream blockage for a laterally infinite wind farm. A clear trend of increasing blockage with domain height is observed. At domain heights of less than 15D, upstream velocity is increased by a maximum of 0.18% (5D). Larger domain heights produce a maximum velocity reduction of 0.23% (25D). The shape of upstream blockage is independent of domain height.

A finite wind farm of 5 columns and lateral extents ranging from 2.5D to 20D on each side are utilized to investigate the impact on blockage. Wider domains of 5D to 20D display increasing blockage with width, while a domain of 2.5D exhibits behaviour similar to a laterally infinite wind farm. Blockage ranging from 0.13% (10D) to 0.31% (20D) reduction in velocity is shown to be highest at the center column of turbines and decreases toward the outer columns.

Inlet and outlet distances ranging from 15D to 100D are modelled. Upstream blockage for inlet distances of 50D to 100D produce consistent upstream blockage magnitude and extent of 0.22% and 30D respectively. Shorter inlet distances result in decreased upstream blockage with a minimum of 0.12% (15D). The shape of blockage remains consistent through all inlet ranges. Outlet distance have no identifiable impact on upstream blockage magnitude and extent.

Changes to the numerical configuration show a clear correlation of increased blockage with cross sectional area of the domain. Constraining the domain in the vertical and lateral directions constricts flow resulting in reduced blockage. Blockage becomes independent of inlet distances at values of 50D and higher. Outlet distance has no identifiable impact on upstream blockage. Choosing a numerical configuration with adequately sized domain boundaries is pertinent in producing realistic upstream blockage.

This thesis sets out to improve the physical grounding and predictive accuracy of cumulative wake effect modelling within wind farms with yawed turbines. It derives an analytical solution for the lateral velocity field within a wind farm and compares its predictions to those of computational fluid dynamics.

A parametric study is performed using a Reynolds-averaged Navier-Stokes (RANS) solver with the k-ε-fP turbulence model, Joukowsky rotor-based actuator disc, and neutral log-law inflow within the PyWakeEllipSys framework to determine the effects of yaw angle, thrust coefficient, and turbulence intensity on the lateral wake.

The results of this parametric study are used to solve an approximate form of conservation of mass and momentum in the lateral direction for a turbine within a wind farm. The solution is an explicit equation predicting the lateral velocity distribution and lateral wake deflection within a wind farm of arbitrary layout and with arbitrarily yawed turbines. It also provides a first mathematical proof of secondary wake steering.

The solution is implemented in Python and used to predict the velocity distributions in several wind farm cases, including for a single turbine, a two-turbine arrangement, and two wind farm cases with aligned and staggered layouts. These predictions are then compared against those of the RANS setup. The model significantly overestimates wake deflections unless corrected to neglect the near wake, but the corrected version shows promise, particularly in predicting wind farm power of the staggered layout, where the prediction is 19% closer to the RANS result than the prediction that considers lateral velocities equal to zero.

With the growing offshore wind industry, ramp events, i.e., significant variation in wind speed (or power) within a short time period, have become of critical importance to the end-users. The analysis of wind power ramp events from the perspective of grid operators or energy traders is prevalent in the literature. Nonetheless, this area of research is still evolving. The criterion of a minimum of turbines and its effect on the detection of ramp events, thresholds, or smoothing of power differences have not been considered so far. Moreover, little research has been conducted on analyzing the impact of ramp events on ultimate wind turbine loads. To the best of the authors’ knowledge, the results have not yet been validated with measurements. Therefore, the primary objective of this report is to investigate the impact of wind-induced ramp events on offshore wind farm power and ultimate wind turbine loads by using offshore measurements.

At first, a wind power ramp detection algorithm is developed by considering the suitable power normalization technique and the criterion of a minimum number of turbines. The detected ramp events followed a decreasing trend in a number of events with the increasing wind speed, turbulence intensity, ramp duration, and wind direction changes. This can be because of the strong dependence of the number of ramp events on the non-linearity of the power curve. However, the available measurement data sets were found to be insufficient to capture the correlation and travel time of ramp events within two wind farms. Therefore, detailed meteorological information with the high spatial and temporal resolution is necessary for thorough analysis.

Following that, extreme wind speed fluctuations (ramp-like events) were found to be extreme load drivers for wind speed bins that fall under the inactive pitch or above rated wind speed region. The measured loads are further compared with the simulation results, which are obtained by following the International Electrotechnical Commission (IEC) guidelines of extreme turbulence load cases. In general, the measured loads associated with the extreme fluctuations did not outperform the simulations, except for the tower side-side moments. Besides, extreme loads around rated wind speed exceed the simulations, but the values are not related to the extreme fluctuations. Therefore, this study examined the high-frequency time series for the most interesting blade root flap-wise moments. Based on the investigation, extreme loads over the wind speed range, except near rated wind speed, were governed by extreme turbulence or ramp events involving a sudden pitch transition from the inactive to the active region. The extreme loads around rated wind speed, which exceeded the simulations, were associated with the standard deviations equivalent to the normal turbulence but with comparatively higher fluctuation frequency. In conclusion, for the ultimate load analysis, the wind speed time series should include a sudden transition of a pitch angle from the inactive to the active region.

A key challenge in the Airborne Wind Energy industry is the minimisation of the time and energy required for the transfer and return phases inherent to the power cycles of ground-generator-based kite power systems. This thesis contributes to solving this problem by exploring the use of bleed air spoilers during the return phase in terms of their potential to improve the average cycle performance. To this end, a mathematical model is first introduced to evaluate the individual phases of the characteristic power pumping cycles in a general sense. This model is further used to demonstrate that a given kite configuration with the capacity to selectively lower its glide slope and/or resultant aerodynamic loading could improve the net power output of the system by around 30%. A CFD-based simulation environment for investigations of the effects induced by bleed air spoilers is developed using Simcenter STAR-CCM+. To establish the reliability of the setup, a first study revolves around its validation with experimental data from independent wind tunnel tests on bleed air spoilers effects on ram-air parachutes. The second CFD study is based on a standard kite geometry employed by SkySails Power GmbH. Two parameter studies testing the influence of spoiler size and chordwise position are carried out to investigate how bleed air spoilers are best used to reach the desired aerodynamic effects. The results show that bleed-air spoilers should ideally be applied in the vicinity of the suction peak and that relatively small spoiler geometries can already entail a major effect on the resultant flow field. It is predicted that the evaluated configurations would improve the system's overall performance by 9 to 18.3%, mostly due to substantial reductions in the glide ratio.

Wind energy is becoming an important renewable energy source. An increased number of offshore wind farms are constructed due to the relatively higher wind speeds. Besides, compared with the land, the ocean areas offer more empty space for the installation of wind turbines. In recent years, several governments in Europe have the plan to expand their countries’ wind farms over the North Sea area. With this surge in the development of offshore wind farms, extreme weather events over the sea pose threats to the installations. A waterspout is one of such phenomenon of concern.

In this study, we simulated and characterized the atmospheric conditions associated with two waterspout events observed recently over the North Sea. These cases were selected from the European Severe Weather Database. Various types of observational data, including radiosondes, radar reflectivities, satellite imageries, lighting maps, and floating liar-based wind profiles, were utilized for detailed characterization. Atmospheric circulation patterns associated with waterspouts were deduced from surface-level and upper-air synoptic charts. A mesoscale model, called the Weather Research and Forecasting model, was used for simulations with a high spatial resolution of 1 km. We used five different parameterizations of varying complexities to quantify the sensitivity of the simulated results with respect to cloud microphysics. A number of meteorological variables and indices (e.g., thermodynamic indices, wind shear, vertical velocity, reflectivity) are extracted from the simulations and compared with the observational data. In general, our results are in agreement with the findings from previous studies. For instance, we have found that a double moment microphysics parameterization produces more realistic results in comparison with a single moment one. However, we have noticed that our simulated results fall outside the range specified by the so-called Szilagyi waterspout nomogram. This nomogram was initially proposed based on observational data from the Great Lakes region and is widely used by the operational meteorologists. Based on the results, updating this nomogram is needed with additional observational and simulated data from the North Sea region.

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.

Several factors play a role in the ongoing process of the acceptance and eventual universal use of LiDAR remote sensing technology in wind site assessment. Especially turbulence parameter 푇퐼 is still giving food for thought. Three important aspects of LiDAR measured 푇퐼 that are considered key are picked out and feature in this work, which attempts to contribute to the handling of LiDAR measured turbulence. Firstly, the accuracy in measuring several wind characteristics compared to met masts,which have been the standard in wind science and industry for the past centuries, is an issue. This thesis evaluates a LiDAR met mast comparison for a Dutch onshore wind farm site to support the handling of the biases between both sources, with a focus on 푇퐼. It finds LiDAR suitable for basic wind measurements, but it still struggles with measuring turbulence accurately. Patterns in biases are exposed and explained and propositions of an alteration of the internal LiDAR correction factor 퐶, as well as the use of a new turbulence parameter called transience 휏m, are made. The second factor addressed in this thesis is an indirect one and originates from the fact that turbulence, and so 푇퐼, plays an important role in the fatigue lifetime damage of a wind turbine. It is tried to answer the question what influence small biases in 푇퐼 have on the fatigue lifetime damage of the tower and blades of a common wind turbine by means of a simulation based sensitivity study. Increments with an order of magnitude found in the first part of this work form an input for this. It is found that the fatigue damage due to stresses from certain bending moments is higher than others and that the blades and tower are affected differently by turbulence. The third factor is related to the inherent flexibility of LiDAR devices. This forms a big advantage over met masts in general and also possibly paves the way for short-term measurement campaigns by LiDAR. It is proven that 푇퐼 shows intra-annual variability, which would therefore require extrapolation techniques. The Measure Correlate Predict (MCP) methodology is chosen to conduct a case study on the application of several MCP algorithms on 푇퐼 data, containing several experiments. It was found that the linear regression method (LR) is unsuitable for making long-term turbulence predictions, but that the variance ratio method (VR) shows promising results on the used performance metrics. It seems that it does not matter whether the short-term data length is 3 or 6 months and that the picked season does not harm the final result significantly.

Wind farms are increasing in size to keep up with the increased demand for renewable energy. Larger wind farms, leads to more complex wind farm design problems for a designer. This study investigates the wind farm design process and compares computed and manual design approaches in order to find the best solution to the wind farm layout optimization problem.

In this study, the design process of a wind farm is described in detail based on the
experiences of two designers in order to get a clear view on the current state of wind farm designing. Different experiments were performed to compare a manual and a computed design approach for an offshore wind farm. It turned out that the computed designs led to a 1 % increase in energy capture compared to the manual designs. Further research is required to conclude that computed design approaches always lead to higher energy yields, however, it was found likely that the computed approach is the best method to solve the wind farm layout optimization problem. The main problem with the current available optimization software is that it cannot deal with all constraints. Therefore, optimizing a
wind farm design still requires some manual changes to fulfill all requirements for the project.

This research aims to develop a method to create a wind farm layout that is robust against the uncertainty source, the inter-year variation of Weibull parameters and wind direction sector probabilities. A wind farm layout optimisation problem under uncertainty corresponds to optimisation under uncertainty (OUU), which is computationally expensive. It is, therefore, proposed to solve this issue by applying surrogate modelling. The thesis considers and compares two different surrogate models, polynomial chaos expansion (PCE) and Kriging, on two different applications. First, the surrogate model is built for the wind farm power model to emulate the medium-fidelity model that requires to evaluate the wake effect in every wind speed and wind direction. Another application of the surrogate model is for the uncertainty quantification (UQ). As the computational expensive Monte Carlo method is traditionally used to propagate the uncertain variables, the use of surrogate models can further reduce the number of uncertain samples to obtain the statistic output response.
Once the statistic of annual energy production (AEP) of the wind farm can be computed efficiently, the optimisation under uncertainty problem is performed with the genetic algorithms. The computational time of optimisation under uncertainty is reduced by using the surrogate UQ model, and a layout that may be more robust to the inter-year variation is found as indicated by a higher P90 value of AEP.

In order to assist project developers in efficient offshore wind farm installation planning a decision-support tool has been developed. The tool combines Discrete Event Simulation and Black-box Optimization. The focus of the development was on providing the flexibility allowing a user to compare various installation strategies and impact on the total installation cost and vessel workability. Synthetic weather generation with Markov Chains was implemented in order to account for weather effects. Recomendations have been given for weather modelling (for the purpose of installation planning) and for applying different families of black-box optimization algorithms.