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.

One important environmental concern in operating wind farms is the bat risk of collision with wind turbines’ rotating blade. To avoid this, the bat curtailment practise is implemented, in which the turbines are shutdown during bat activity periods. This, however, leads to a decrease in the annual energy production (AEP). A proper assessment of the energy loss due to bats is key to correctly estimate the wind farm AEP. Following the manufacturer strategy, the industry standard provides a model to estimate the annual energy efficiency due to bat curtailment in wind farms. This is a time domain efficiency model that works with setting the power generation time stamps to zero into the reconstructed hub height power time series used to alculate the AEP, according to temperature, wind speed and time limits that are recognised to be sensitive to the bat activity. The exact values of these limits depend on the turbine control system design choice of the specific turbine model.
However, after analysing the post-construction operational data of wind farms, the annual bat energy efficiency evaluated according to the industry standards is observed to underestimate the real efficiency seen in operation.
In this work, the aim is to explain and evaluate the discrepancies seen between the bat curtailment losses in operation and the bat efficiencies modelled using the industry standard. To achieve this, the operational data of different wind farms are analysed and the resulting differences in losses are broken
down into multiple categories including yearly windiness, error in the control strategy implementation, inaccuracy of nacelle instrument readings of the ambient parameters (including the wind speed), inertia in the control of the turbine.
The content of this work is organised as follows: in Chapter 4 the theoretical content and tools supporting the research are given, in Chapter 5 the research question is explained in detail, in Chapter 6 the methodology applied to analyse the operational data is described, then, the behavior of three wind farms is investigated during bat curtailment, in Chapter 7 the three wind farms are analysed in pre-construction and the bat curtailment energy losses are evaluated according to the industry methodology, in Chapter 8 the post and pre-construction results are compared and the gaps between the two are investigated, in Chapter 9 a sensitivity analysis over the parameters seen as sensitive to bat curtailment is carried out to understand the influence these have in the bat losses, in Chapter 10, an alternative methodology to correct for the nacelle anemometer reading is proposed and, finally, in Chapter 11 the conclusion is drawn.

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.

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.

With an ever increasing demand for sustainable energy, limitations of current sustainable technologies are studied widely. In wind farms, the so-called wake effect provides the biggest limitation on wind farm total power output. Using wind from the unaffected boundary layer to re-energize the wind flow in the wake provides a method of limiting this wake effect. In this study, kites are introduced to steer the wind flow of the unaffected boundary layer into the wake through a downwash velocity. RANS (Reynolds-averaged Navier-Stokes) simulations are performed in Computational Fluid Dynamics (CFD) software OpenFOAM of the atmospheric boundary layer (1), a small four-turbine wind farm (2) and a wind farm with static kites between the turbines (3). The turbines are modelled through the actuator disc approach, and kites are introduced through the more complex actuator line method. Results of the athmospheric boundary layer (ABL) and wind farm simulations correspond well with literature. Through extensive kite parameter studies, an optimal layout of kites in the wind farm is presented yielding a wind farm efficiency increase of 2.3 %, which increases over 5% for even larger kites. Kite size and the kite’s downstream location show to impact the re-energising levels of the wake flow the most. The kites generate a downwash wake instead of a single downwash velocity, a finding that should further be studied in future research.

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.

The installed offshore wind energy capacity in Europe has been increasing rapidly over the past years, this trend is expected to continue thanks to the European green deal. In order to meet the future energy demand, new wind energy solutions such as increasing wind turbine size but also floating structures are being developed. This means that stability phenomena and their effects on wind turbine fatigue and energy yield are becoming increasingly important.
The aim of this thesis project is to define and quantify the applications and limitations of the Dutch Offshore Wind Atlas for wind energy purposes by using it to calculate the energy yield and fatigue damage of two reference wind turbines at multiple locations in the North Sea, with special attention for atmospheric stability. Atmospheric stability is quantified using the shear exponent, Obukhov length and potential temperature profile. For the energy yield and fatigue calculations, a comparison is made between DOWA hourly data, 10-minute LES runs and site measurements as well as a comparison between locations across the North Sea. Additionally, some relevant parameters for wind farm design such as weather windows and dominant wind direction are analysed.
The results show that for certain wind energy applications, the DOWA data performs relatively well, but several limitations were found as well.

Due to the ever increasing demand of energy and the urgency in which this has to be produced using renewable energy resources the request for offshore wind farms is larger than ever. As a result of the competitive nature of the energy sector it is important to keep the cost of energy low in order to keep an edge over the competition. Therefore, to keep wind energy economically viable in the future the cost of producing energy should be lowered. One way this can be done is by lowering the initial costs of installation, as this is relatively high. In this thesis an installation method of monopile foundations will be introduced, which has potential to reduce costs of the installation of wind farms. More specifically, this thesis will focus on lowering of monopiles to the seabed. Problems arise when the monopile hanging on the crane wire is excited by waves hitting the submerged part of the monopile. This can cause excessivemotion of the monopile. Particularly if resonance occurs, which is the case when the wave frequencies and the natural frequencies of the monopile coincide. This could lead to catastrophic failure if no mitigating measures are taken. The most common method to counteract this phenomenon is to wait until weather conditions allow for further continuation of operations, which can consume lots of valuable time and thus increasing costs. Therefore, this thesis will focus on optimizing the workability of offshore installations of monopiles by using gyroscopicmotion actuators. These actuators, called controlmoment gyroscopes (CMG), will reducemotion of the monopile by applying a moment counteracting the moment created by the waves. This system is modelled in MATLAB. The model consists of the monopile suspended on the crane wire, a gripper and the CMG. The equations of motion are determined using the Lagrangian method and linearized around their equilibrium position. The equations of motion are then written in state space representation in order to study the behaviour of the system and to tune the PD controller. The PD controller was tuned using the loop shaping method. The MATLAB model is used to tune the controller. However, the results will be examined using OrcaFlex. Therefore, the same system is also modelled in OrcaFlex, a commercial time domain simulations package, in which an external function is built using Python to represent the CMG and the corresponding PD controller of the CMG moment. The whole system is then subjected to a sea state with a significant wave height of 2 meters and a wave period range from 5-8 seconds. The 3 hour simulations are run for each period step of 0.5 seconds from 5 to 8 seconds. From each simulation the most probable maximum is calculated. This is done for four variants of the system…

A new wind shear relation is derived based on the atmospheric equations of motion, which are, in turn, based on the Navier-Stokes equations. In order to make the obtained system of equations solvable, it is assumed that the air density and pressure follow the International Standard Atmosphere relations and that the wind speed is constant over time. Additionally, it is assumed that the gravity force is constant, that the air is incompressible and that there is no wind speed variation in x- and y-direction.
If the additional assumption of no vertical wind speed is made, then the relations simplify to the Ekman equations of motion, which result in the Ekman spiral. Not making this additional assumption results in a system of equations that can be solved exactly or using asymptotic matching. As expected, the solution that was derived using asymptotic matching approaches the exact solution.
The derived relation is compared to wind speed relations from literature, such as the log law, the profile by Gryning, the profile by Peña and the profile by Kelly. It appears that the log law performs best in terms of simplicity and accuracy, but a final conclusion cannot yet be drawn. Additional research is recommended into what types of locations can be accurately represented by the derived relation and into what input data is required and obtainable in order to increase the accuracy of the wind speed predictions by the theoretical profiles.

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.

Our present-day society is utterly dependant on electricity. This dependence will only grow as electrification of sectors such as manufacturing, transportation and building heating takes off. Most of the electricity these days comes from conventional plants running on coal and natural gas. Despite that these are reliable and cheap, the disadvantage of emitting greenhouse gases is no longer acceptable. Sustainable alternatives such as solar PV and wind have the highest potential. However, the replacement of conventional power plants by sustainable alternatives is subject to understanding the intermittent and unpredictable behaviour of both wind and solar PV and thereby ensuring that generation equals demand at all times. Energy storage technologies allow for separation between generation and supply to the grid. The aforementioned makes the large-scale integration of wind and solar PV more difficult. This study lays the foundation for an interconnected system model where solar PV, wind, and battery storage is combined. This study is therefore deliberately different than existing studies focussing on small, already severely constrained systems, such as island systems. The problems experienced and possible solutions are first identified by conducting a literature review and simultaneity analysis of wind and solar PV power. It turns out that it is possible while being self-reliant, using all the potential renewable energy and shifting the generation by arbitrage on the APX market to design a system with an increasing rate of income. The simulations showed that when the combined generation (of both wind and solar PV) during peak availability is higher than the grid connection capacity, the computed battery size [MWh] increases rapidly, causing a swift decrease in rate of return. This research also shows that with current imbalance settlement prices and battery installation cost minimizing imbalance is less viable than arbitrage on the APX market. The presented results are consistent with how the electricity system currently operates. At last, the results indicate that curtailing ’cheap’ solar PV energy with significant overplanting on the existing limiting grid connection is beneficial. In this research some important steps have been taken towards the design for a grid-connected optimal system. The method proposed should be tested with more wind and solar PV generation data. Further research should consider longer periods with real generation data making the results presented more accurate.

The offshore wind energy market is highly dynamic and one of the major challenges is to become more cost competitive against other sources of energy. The support structure of an offshore wind turbine shows a high potential for further cost reduction. Jules Dock is investigating in the replacement of a steel tower on a monopile foundation by a Glass Fiber Reinforced Plastic (GFRP) tower, which has a higher specific strength and better corrosion resistance compared to steel. This would enable lower transportation and installation costs and a potentially longer lifetime. Currently Jules Dock assesses the feasibility of a GFRP tower versus a steel tower using an aero-elastic simulation tool. However, such a tool is computationally demanding and requires detailed turbine data, which are often not available in a preliminary design phase. For this reason, a research project has been set-up to develop a parametric design model which can effectively be used without detailed turbine data and aero-elastic simulations, to identify the design driving constraints for preliminary designs and mass optimization purposes. The parametric design model analyses the natural frequency constraint and Ultimate Limit State (ULS). The natural frequency of the support structure is intended to be in the soft-soft region, which will result in the lowest mass possible as has been found in previous research. The natural frequency of the support structure is analyzed with a finite beam element model, in which the Rotor Nacelle Assembly (RNA) is modelled as a point mass. The interaction with soil is also included using a three spring model. For the ULS, the yield and buckling capacity of the support structure are analyzed. For the steel monopile and transition piece, dedicated design standards have been used. For the composite tower, however, these design standards are not existing and therefore analytical solutions from composite tube applications have been used and verified for this purpose. FEM analysis showed that the yield capacity can be determined exactly. The buckling capacity approximation in the parametric design model showed conservative results compared to a dedicated buckling analysis tool. In the parametric design model the maximum loading for the ULS constraints has been analyzed using a quasi-static load analysis method which includes turbulence and dynamic wave loading effects. This method has been compared with the time-domain simulation tool Phatas, which is integrated in Focus6. This comparison showed that also the tower top bending moment should have been included. If this is taken into account, the integrated load analysis method appears to approach the maximum loading for a very stiff tower quite accurately compared with the time-domain simulations. For flexible towers, however, the loads are less accurately approached, since dynamic interactions between wind and wave loading have appeared to be relevant, especially for load cases with a wave excitation frequency close to the natural frequency of the support structure. In the quasi-static load analysis method these dynamic effects are only taken into account for the submerged part of the support structure, but the time-domain simulations have shown that these effects will increase the maximum loading on the support structure above water level as well. For mass optimization and identification of the design driving constraints of this support structure, a genetic algorithm has been developed. Several optimization runs have been made, resulting in multiple feasible solutions with only small differences in support structure mass. While the buckling constraint drives the design of the monopile, transition piece and top of the tower, the lower part of the tower is design driven by the yield constraint. It has been concluded that the loading analysis method in the developed parametric design model should include the tower top bending moment, since this leads to a mass increase of up to 20% of the support structure mass. Next to that, it has been observed that the optimization algorithm tends to converge to solutions with a natural frequency to be as low as possible, such that the dynamic amplification of the wave loading is minimized. For these solutions, the loading analysis method in the parametric design model may have underestimated the maximum loading on the flexible support structure. This needs to be further investigated.

Flows over complex terrains present uncertainties in wind farm power production and lifespan. Core to these uncertainties are wake dynamics, which lack adequate models and need further validation. In lieu of conducting field experiments, this study integrates scanning lidar measurements and large eddy simulation (LES) to characterize wakes in complex terrain. LES data of flow over complex terrain near Perdigao, Portugal has been generated prior to this study. In the current study a `synthetic’ lidar is placed inside the LES to sample the wind field in space and time as a commercial scanning lidar would. From the synthetic scan fields, the wake is located and characterized using metrics of wake center location and maximum velocity deficit. These metrics are evaluated against the same metrics as determined by LES fields. By treating the LES metrics as the ``target”, the relative accuracy of the metrics can be determined. This accuracy is a function of the scan geometry, allowing for the geometry to be optimized for wake characterization. Since a lidar scan represents neither an instantaneous field nor a mean field, it is not clear whether the target should be the LES instantaneous or mean fields, therefore the metric accuracy is assessed using different LES fields in the form of instantaneous snapshots of the wake, fields averaged over the time of a scan cycle, and long-term averaged fields.

A set of scan geometries was tested which vary in measurement point density, scan direction, and scan area to find that accuracy depends on a trade-off in the temporal and spatial resolution of the geometry. The results highlight not only the dependency of accuracy on the geometry parameters, but also on which type of LES field is used as the target. The long-term averaged errors were found to be unsuitable for lidars in this terrain, where the longest time scales exceed the simulation time, therefore accuracy was assessed using two types of fields, instantaneous fields taken at the start of the scan cycle, and the cycle-averaged fields. From these results two geometries were identified as performing the best.

Improvements are still needed in the wake detection algorithm, both in distinguishing points in the wake from terrain-generated turbulence and the inclusion of more wake characteristics such as size, shape, and orientation in order to develop more accurate scan geometries, hence improving predictions of wind farm production and longevity. Other scan geometry parameters also need to be explored, including scan paths and non-uniform measurement point densities.

This project is a collaboration between Goldwind, Technical University of Denmark (DTU) and Delft University of Technology (TUDelft), of which focusing in developing an optimization model that could determinate the optimal operation schedule for wind turbine under the uncertainty of the electricity price and limitation in life-time equivalent fatigue loads, for the purpose of improving and maximizing the revenue or cash flow for wind farm project.

The project comprises of two main parts, including the design and validation of up- and de-rated operation modes, and the model development and optimization of the revenue. The design of the different operation modes provide varies rated power without sacrificing the life-time structural integrity, as such offer extra flexibility in wind turbine operation schedule according to the fluctuation in electricity price. Life-time equivalent loads would be calculated for the different operation modes design and a weighting factor, that indicate the rate of fatigue consumption relatively to the normal operation, would be computed and used in the optimization process. An optimization model is developed based on the turbine structural constraints as well as the financial profile. The optimization model processes the electricity price data and other wind turbine data, for example the fatigue consumption, to reproduce the optimal operation schedule and operation price thresholds that would yield utmost financial benefits.

The optimizer has a proven performance improvement in revenue generation of 1.5% and 5% reduction in the project related interest payment under one of the case study under electricity price uncertainty. The model is effective in scheduling the turbine operation upon the varying electricity price. This optimization model is providing game changing insights and operation strategies that could be used throughout the turbine life-time under the ever changing electricity price of the energy market. And further drive down the cost of energy in the highly competitive market. By improvising continuous input to the optimizer including bending moment sensors inputs, the optimization process could reproduce a more accurate revenue improvement.

Traditionally a wind turbine’s power curve is used to model the long-term energy yield of the wind turbine and afterwards assess the performance of the turbine (power curve verification). But the current power curve is typically univariate: only dependent on the wind speed and slightly adjusted for site density, turbulence intensity and wind shear. This method limits the amount of atmospheric conditions taken into account, which (could) affect turbine performance. Therefore the Power Curve Working Group introduced
the concept of an inner power curve (for common conditions) and an outer power curve (for all other conditions). While this is an improvement, it would be desirable to have not 1, not 2, but a multitude of power curves for different conditions.

This multitude of power curves, for a range of atmospheric conditions, is the goal of this thesis. Using a non-parametric machine learning method, a neural network, the feasibility of modeling a wind turbine’s power curve from historical data is studied. This resulted in a model which provides a refinement on the warranted power curve (provided by the manufacturer) for certain conditions of 7 atmospheric parameters. These parameters are wind speed, turbulence intensity, wind shear, wind veer, temperature, pressure and humidity, but could in theory be extended to more parameters.

This model resulted in three sub-models, which: 1) assess individual parameter impact/sensitivity on turbine performance, 2) provide for a company tool to continue power curve verification after a few years and 3) provide a general model to refine long-term energy yield estimations for future wind farms. Despite
the somewhat limited model and currently available datasets to train the models from, both predictive models show improved accuracy compared to the traditional power curve. Besides validation with theory, the impact assessment model allowed for investigation of the impact of certain atmospheric conditions on
the performance of certain wind turbines.

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.

Wind turbine design is subject to load calculation standards. These determine material use which in turn affects wind turbine costs. Wind turbine cost reductions are important as they contribute to the economic feasibility of wind turbines. Therefore careful evaluation of the load standards is paramount for wind turbine design. This thesis offers an evaluation of 50-year ultimate load estimates described in the IEC 61400-1 standard and a tool for wind turbine designers to manage the estimates...