The transition to renewable energy sources is essential to mitigate climate change, and floating wind turbines (FOWTs) present a promising solution to harness offshore wind resources. Light Detection and Ranging (LiDAR) systems mounted on nacelles provide a cost-effective and efficient means to measure wind fields, critical for turbine performance, control and load simulation. However, FOWT's motion introduces complexities in LiDAR measurements due to velocity and positional changes. This thesis focusses on developing a correction method for LiDAR measurements on FOWTs, addressing the influence of motion on wind velocity, position, and direction. The accuracy and uncertainty of these corrected measurements are quantified.

Simulated six degrees of freedom (6DOF) motion and a power law wind field are inserted in a numerical LiDAR model, in which corrected and uncorrected measurement position, direction and line of sight velocity are constructed. The corrected outputs are validated through reconstructed wind fields and the uncertainty of the correction is quantified. In this study, significant motion-induced bias is identified in the reconstructed wind fields. The dominant motion affecting measurement accuracy was identified as pitch motion, especially when it exhibits a non-zero mean. The relative error of the reconstructed power law wind field parameters is reduced by 3 orders of magnitude. Despite an increase in uncertainties associated with the correction method applied, the correction remains effective in reducing the error in LiDAR measurements induced by FOWT motions. The findings highlight the necessity and feasibility of motion correction for LiDAR measurements, offering substantial improvements in the accuracy and reliability of reconstructed wind fields for floating wind turbine applications.

Measure Correlate Predict (MCP) is a method used to characterize the future wind resource at potential new wind farm locations. It finds a relationship between the wind data obtained at the target site and a nearby reference site over a short time period. This relationship is applied to long-term historical data from the reference site in order to predict the long-term wind resource at the potential wind farm location. Over the last few decades, new MCP techniques have been proposed, and modelled meteorological data in the form of reanalysis products have emerged. However, little research has been done regarding how the application of modelled data and different regression techniques in MCP affect the achievable wind resource prediction accuracy.

This project set out to investigate the accuracy of the MCP procedure under different configurations. Three comparative studies have been done in this project. Firstly, the attainable accuracy with either nearby MET-station data or ERA5 reanalysis data as a long-term reference source is assessed. Secondly, the use of different regression types for forming the relationship between target and reference data in the MCP procedure is assessed. Lastly, the accuracy achieved with standard MCP is compared to that achieved with a new wind resource estimation method, the method of analogs. The accuracy of the different configurations is assessed through the ability to accurately predict a period of wind speed values measured at 35 sites located in different terrain types. The predictions are evaluated using metrics such as the coefficient of determination, the root mean square error, the mean absolute error and the mean bias error.

This study found that ERA5 reanalysis data can serve as a reliable alternative to observed MET-station data. Generally, using ERA5 reanalysis data as a reference source always led to more accurate predictions then a MET-station reference source if the Pearson correlation between target and MET-station is lower than 0.8, and for offshore targets. If the Pearson correlation between target and MET-station reference is higher than 0.9, the achieved accuracy with either the MET-station or ERA5 data as a long-term reference is similar and depends on specific site conditions. In terms of regression methods it was found that the Matrix method, using the target site sectors for determining the regression parameters, generally outperforms other regression methods in terms of accuracy when determining the mean wind speed. Lastly, the method of analogs, a recently developed wind speed estimation method, yielded a similar prediction accuracy to standard MCP.

It should be noted that the different regression methods in employed in MCP all exhibited very similar prediction outcomes, with an average absolute difference in the predicted mean wind speed between the best and worst performing regression methods of only 0.046 m/s. Furthermore, the performance of the method of analogs in terms of accuracy improves with a longer concurrent period during which the relationships are formed. This project employed relatively short concurrent periods for certain targets, which may have contributed to sub-optimal performances of the method of analogs.

Offshore wind is a rapidly maturing renewable energy technology and is expected to play a central role in future energy systems. It has the potential to generate more than 420,000 TWh per year worldwide, an amount approximately equal to eighteen times the global electricity demand today. While many of the most abundantly resourceful sites are at water depths too extreme for fixed-base solutions, current floating turbine technologies suffer with high-costs and inefficiencies. Seawind Ocean Technology B.V., a Netherlands based company, is a manufacturer of fully integrated floating wind turbine systems. They have designed an innovative set of low-cost, low-weight, upwind two-bladed wind turbines, with teetering hinge and yawed power control. This teetering hinge decouples the shaft from rotor, by adding an extra degree of motion, and protects the turbine from harmful aerodynamic and gyroscopic loads, as well as the rotor from hydrodynamic loads. The innovative active yaw control eliminates the need for complex pitching systems to regulate power output, in turn reducing turbine head weight and turbine costs.

There are a number of aims and objectives for this thesis project. The first, and most fundamental, was to investigate two-bladed and floating offshore turbine technology and share these findings with the hope that they make their way into the hands of change-makers who can promote the technology and consequently accelerate the green transition. The second was to optimise the loading analysis models of the Seawind 6 turbine using DNV GL’s wind turbine design software, Bladed. This is a 6 MW two-bladed floating offshore turbine and is intended for commercialisation by 2024. Seawind have extensive operational data from the Gamma 60’s deployment (the world’s first variable speed, two-bladed, wind turbine with a teetering hinge) in the 1990s. The Gamma 60 was modelled and compared to this operational data, with the calibrations in turn applied to the Seawind 6 models to improve its accuracy. Once achieved, the third goal was to carry out a thorough ultimate and fatigue load analysis of the optimised Seawind 6 model for various design load cases at extreme water depths. This investigation proved that the various investigated turbine components have been adequately sized and that suitable construction materials have been selected considering the loads they are expected to withstand. Following this large body of work, the fourth objective was to verify the theory that teeter motion is aerodynamically damped when the blades of a two-bladed, teetered turbine, are vertically oriented due to differences in angles of attack of the oncoming and retreating blades when yawed out of the wind. The fifth and final goal of this thesis work was to pull everything together and compare the floating offshore two-bladed Seawind 6 to a conventional floating three-bladed state-of-the-art turbine competitor. This was in terms of ultimate and fatigue loads experienced, capital and operational expenses (CAPEX and OPEX), lifetime carbon abatement, levelised cost of energy (LCOE), ease of manufacture and deployment, and operational performance.

The development of offshore wind technology has progressed rapidly since the first offshore wind farm came to birth in 1991. During the last three decades, offshore wind has become an economically attractive option of renewable energy source for many countries to fulfill their commitment to combat the climate crisis. However, an open challenge remains in offshore wind, namely the aerodynamic interactions between multiple turbines, referred to as wake effects. When the upstream turbines extract energy from wind, they generate the wake with low velocity and high turbulence level on the downstream turbines, which substantially reduces the overall wind farm performance. The annual power production loss due to wake effects is generally 10 - 40%, which limits the reduction of the levelized cost of electricity for offshore wind energy.

Therefore, developing effective wind farm control strategies which could mitigate the wake effect has become an emerging research area in the past decade. Recently, a novel wind farm control strategy called the Helix approach is proposed. The Helix approach adopts the individual pitch control technique to dynamically deform the wake into the helical shape, which induces wake instability and thereby stimulates wake recovery. This control strategy has demonstrated promising potential to mitigate the wake effect and to enhance the wind farm performance. To date, the Helix approach employs single harmonic pitch signals. However, more complex and higher-harmonic signals to potentially improve the effectiveness of the Helix approach have never been explored. Therefore, the purpose of this master thesis is to explore the potential of using multi-sine pitch profiles to further enhance the wake mixing.

The high-fidelity aeroelastic simulator OpenFAST with its recent free vortex wake code is adopted to simulate the dynamic wake evolution. A Fourrier stability analysis is used to quantitatively identify the wake breakdown position. According to the simulation results for the multi-sine pitch profiles, the wake breaks down at 1.75 D, which is earlier than the one at 2.50 D for the original single-sine Helix strategy. The earlier wake breakdown indicates the potential of faster wake recovery, which is required to be validated by the higher-fidelity model in the future studies.

A structural firm will not invest a large sum of money in early design fase on a wind tunnel aeroelastic model to determine the displacements, forces, and moments when the dimensions of a building have only been estimated because the outcomes are only valid for that specific model. It is well-known that the outcomes are hard to determine precisely in early design fase.

This study aims to make a modelling tool in Matlab to predict the bending and torsional oscillations of a high-rise building due to wind load fluctuations in time and space.

To test the research question, which is, does the designed Juffertoren building in Rotterdam and the structurally strengthened student building Voorhof in Delft comply in the serviceability limit state when looking at bending and torsion accelerations in the along wind direction.

After strengthening the occupants of the student building Voorhof still complained about motion sickness during storms.

The results show that the Juffertoren and the student building Voorhof do not meet the serviceability limit state requirement.

These results suggest that more research should be done when looking at serviceability limit state requirements by light weight and thin buildings.

The growth of renewable energy technologies is leading to energy systems that are more reliant than ever on renewables such as Wind and Photovoltaic (PV) power. Despite their benefits in terms of sustainability, their ubiquity poses challenges in maintaining grid stability given their intermittency, emphasising the prediction of power fluctuations. Physical models and statistical approaches, especially for nowcasting (forecasting for 0-6 hours in the future), have been superseded by Machine Learning (ML) methods in terms of forecast accuracy (below 3% Root Mean Squared Error (RMSE)). Within ML, Artificial Neural Network (ANN) methods seem to perform particularly well for nowcasting. This project focuses on predicting solar and wind meteorology with that level of accuracy, and on how to best use the prediction to minimize the cost of maintaining a balanced energy system, i.e. one where power consumption matches production at any moment. Producing accurate power predictions based on Multi-Modal (MM) data and the extent to which prediction accuracy reduces system cost are challenges to be addressed in this thesis. MM and End-to-End (E2E) training (with the system cost as the task of an ANN based algorithm) are investigated to this end. MM learning involves handling information from multiple types of input (audio and visual, for example) for performing a ML task such as regression or classification. It is of interest for this project because it has been shown to outperform other NN approaches in predicting sudden changes in solar irradiance. E2E learning entails an algorithm design which predicts the end goal of a ML process directly from the inputs. This is pursued because it addresses the true task (cost minimization) of system operators as the focus of the ML algorithm. The proposed method consists of a NN architecture that learns to fuse features from MM data (sky imagery and meteorological sensor data) at intermediate layers of the network in order to predict PV or Wind generation. This prediction is then used as an input to an Optimal Power Flow (OPF) problem (which seeks to minimize generation costs in a power system, considering power balance and transmission network constraints to ensure the twin goals of economic and secure system operation). The proposed model is trained E2E, therefore it is informed by the minimized cost solved by the optimization, rather than the intermediate power prediction (as conventional approaches would involve). In an IEEE 6-bus system with PV generation, a sequential training baseline results in costs 10% higher than a perfect forecast, while our proposed MM4-E2E approach achieves costs only 7% higher, a significant improvement. The intermediate prediction of PV power by MM4-E2E is also improved, with 18% lower RMSE by the proposed model compared to the baseline, explained by the enhancement of one modality by the other through MM learning. In a power system with two renewable sources, costs are reduced through the proposed model compared to a conventional approach (4% excess cost compared to 7%, measured against a perfect forecast), but power prediction accuracy is worse, sue to convergence to local minima.

The Ekman spiral is described by a coupled system of differential equations originally discussed by Walfrid Ekman (Ekman, 1905). This system is a simplified version of the Navier­Stokes equations. The differential equations, as discussed in Ekman’s paper, concern the currents of the ocean. However, it is also possible to interpret these equations so as to describe and predict the flow of wind. The research as presented is not only inspired by Walfrid Ekman’s original paper, but also by the master thesis from de Jong (2021). The main contribution of this thesis is to include the influence of a constant vertical wind speed on the classical Ekman spiral. After stuyding the classical Ekman spiral, the inclusion of a constant vertical wind speed is done step­wise. First, the vertical wind speed is discussed without having any vertical Coriolis forces. The classical Ekman spiral and the Ekman spiral with vertical wind, but no vertical Coriolis force, were solved exactly. Then, the vertical wind speed is included fully, giving rise to a non­linear coupled system of differential equations. For the non­linear system, an algorithm for solving it analytically using a general perturbation method is proposed. Next, the hodograph of the non­linear equations of motion including a constant vertical wind speed, is made using Euler’s Explicit numerical method and a shooting problem is solved.

At the mercy of strong winds, high wind shear, unstable boundary layer and anomalous atmospheric conditions, stands a wind turbine designed to produce sustainable power under harsh conditions. The field of wind energy is a promising prospect for a sustainable future. Diverse research towards the improvement of a wind turbine’s capability and cost is currently the focus of the wind energy industry. With higher wind turbines being designed every day, various challenges and limitations of the current state-of-the-art surface; anomalous atmospheric conditions, structural integrity and cost.
The goal of this thesis is to extend the approach to design a site-specific wind turbine considering an anomalous atmospheric condition. By coupling a mesoscale model with a stochastic turbulence function, a wind field capable of depicting a particular atmospheric condition is created. Using an aeroelastic solver the resulting loads on a wind turbine can be simulated. The methodology uses Weather, Research and Forecasting (WRF) model to re-create an event of low-level jet identified at the met mast of FINO-1, off coast Germany. The wind profile is coupled with a stochastic turbulence function designed at FINO-1 to be used as wind field for the aeroelastic solver, FAST.
A literature survey identified a multitude of approaches used for simulating a low-level jet and analyse the loads on a wind turbine, the majority of which indicate high computational costs and contrived re-creations of the event. Thus, the challenge was to identify a near-realistic event creation with low computational costs. Therefore, coupling a low-resolution mesoscale model to create the event with a site-specific stochastic turbulence function is used to analyse loads on a wind turbine.
Meteorological data analysis at FINO-1 led to the identification of three case studies of low-level jets under varied stability conditions of the atmosphere. The case studies are compared with the International Electrotechnical Commission (IEC) standard’s, IEC – 61400 – Ed3; IEC Kaimal and IEC Great Planes Low Level Jet (GPLLJ) spectrum. For cases with high stability, on an average proposed model predicts 21% higher stress on blade root and 27% higher at the tower top and base in comparison to IEC GPLLJ and 15% and 30% lower in comparison to IEC Kaimal, respectively. Similarly, under unstable conditions, proposed model predicts similar loads on the blade root, 7% lower loads at the tower top and base in comparison to IEC GPLLJ and 30% higher loads for blade root and tower top and base in comparison to IEC Kaimal. Comparing these results with literature on high stability loading higher loads are expected under these conditions.
Concluding, this project developed a model framework to analyse anomalous atmospheric phenomena on a wind turbine specific to a site with low computational costs. While the capabilities of the model have been successfully showcased, only a partial validation on a benchmark case has been carried out. Therefore, going forward a full physical validation of the model for its accuracy for its target applications is recommended.

The height of wind turbines continues to increase, making the need for more and higher wind measurements for wind turbine model calibrations also increase. The Energy-research Centre of the Netherlands (ECN) and the Danish Technical University (DTU) have conducted the ScanFlow campaign in the winter of 2016/2017 to study the inflow wind field of one of ECN's research turbines by deploying multiple lidar instruments. One of the instruments used in this campaign was a SpinnerLidar. A SpinnerLidar is a forward looking, nacelle-mounted, continuous wave wind lidar system. It measures Line-of-Sight components of the wind in a plane 60 meters in front of the turbine, which need to be transformed into 3D wind vectors. To do so necessary assumptions were made about the free inflow periods, namely that a vertical shear is present and that a wind direction misalignment is more likely than horizontal deviations in wind speed. With these applied assumptions the 3D wind components were determined and used in a validation study with a pulsed lidar instrument, the WindCube V2. The proposed method seems robust as a high correlation in wind speeds at hub-height between two distinctly different lidar systems was found. Using the validated SpinnerLidar measurement to find the turbulent characteristics of the free inflow wind field resulted in turbulence intensity plots showing a higher turbulent component in the lower regions of the measurement plane. Also indications for a induction zone are visible in the SpinnerLidar measurements when compared to the WindCube measurements.

As one of the carbon-free emission transportation method, fuel cell electric vehicles (FCEV) have become a very popular research topic for the recent years. However, as the fuel of FCEV, the hydrogen is usually produced by traditional steam reforming method, which is still not an environmentally friendly process.

This report focuses on the study of infrastructure for hydrogen producing and refueling with zero carbon emission. An on-site water electrolysis hydrogen producing and refueling system powered by wind energy is designed and simulated in this study.

The hydrogen is produced by on-site PEM electrolyzer powered by distributed wind turbine. First of all, the suitable petrol stations for such hydrogen refueling station modification are selected by GIS data analysis in Germany. By applying the constraints for safety and noise consideration, about 500 stations are selected from over 10000 petrol stations in Germany.

Furthermore, the hydrogen producing and refueling system is designed and simulated by MATLAB modelling. The system is composed of five main components: wind turbine, PEM electrolyzer, compressors, storage tank and hydrogen dispenser. The technical and economic details for each of these devices are defined by a series of literature review. Besides, some parameters are from the real commercial products to make the system model more practical.

A case study is built to validate the designed model for a 330kg/day H2 refueling station in Germany based on both current and future scenarios. The results show that more than 170 tons hydrogen can be produced annually. It can cover most of the hydrogen demand for the refueling throughout the year, which eliminates most of the hydrogen delivery cost from the other producer to the refueling station.

In addition, by using the optimal pre-allocation control strategy, the system can become partially stand-alone with the grid. Only the high-pressure compressor system and cooling system for dispenser need energy supply from the grid, which is less than 1% of the system energy consumption. It means no extra grid reinforcement is needed. The wind energy can be used in a very efficient way. More than 95% of wind energy can be used for hydrogen producing while the other 5% supplies for the compressors as the electricity. The sensitivity research is also performed based on the climate data in a different year, which shows the stable operational behavior for the system.

Last but not least, the economic analysis is carried out based on the case study. For the current scenario, the hydrogen production cost of the system is €6.1/kg and the overall dispensing price is €10.9/kg. It is expensive because the distributed wind turbine and on-site PEM electrolyzer are still costly technologies for now.

However, with the R&D progress of these technologies, the production cost and the dispensed hydrogen fuel cost price for the future scenario will reduce to €2.6/kg and €5.1/kg respectively, which makes the hydrogen a very competitive fuel for the vehicles in the future.

As building insulation and airtightness are constantly improving, ventilation plays a dominant role in fresh air supply, thermal comfort and managing heat losses and cooling needs. By designing an integrated ventilation system, it is possible to improve air quality and thermal comfort, at the same time enhancing energy efficiency. This thesis focuses on the development of such a ventilation system. It is done by employing the best features of existing systems and including several new features.
Throughout the thesis, ventilation, night cooling, solar shading, heat balance, heat recovery unit and renewable energy sources are studied. In this study, the demanded yearly ventilation air flow rates are found, the optimal heat exchanger model is selected and optimized, yearly energy consumption is calculated, while at the end, a wind cowl is selected and saved yearly energy using the cowl is calculated. The Excel Macro tool is developed and used to establish the needed yearly air flow rates, applying night cooling and solar shading. The Matlab tool
is used for the selection and optimization of the heat exchanger as well as yearly energy consumption, with and without the wind cowl. The main specifications of the new system are found. Since a resize of the heat exchanger passage is recommended, an experiment is carried out in order to verify the heat transfer coefficient of the air traveling in the heat exchanger.
The developed ventilation system provides the dwelling with a ‘good’ indoor air quality at any time of the year, using minimum required air flow rates per room. Thus, combined with the heat recovery device the heating demand for ventilation air is minimized. The system also provides thermal comfort at most times of the year using natural cooling and optimal shading. Thus, the cooling need of the dwelling is minimized. However, due to the heat recovery unit and high night cooling flows, the newly developed ventilation system has an enhanced electricity use. Therefore it is recommended to also redesign the air distribution ductwork system within the dwelling for low pressure use The wind cowl contributes to electricity savings even more if the ventilation system has a well-designed, low pressure air distribution system.

Low level jets (LLJs) are situations where wind speed maximums occurs close to the surface. Unfortunately, the influence of LLJs on wind turbine power production is still difficult and costly to quantify due to that the existing LLJ models are all time-based. The work that is done in this report addresses the research question should low level jets be included in offshore wind turbine design.
In this report, data from an offshore meteorology site IJmuiden is studied to analyze the occurrences and properties of LLJs. It is found that LLJs are frequent phenomena which occur at 1/3 of the days in a year. Most of the LLJs are observed when the atmosphere is very stable, which means stability is a crucial factor to LLJs. Based on the diabatic wind profile model, an assumption is made that the LLJ wind speed profile is related to friction velocity, Obukhov length and roughness length. The observed data is divided into groups by Obukhove length to find the relationship between these factors and the wind speed prole of a LLJ. It is found that the LLJ model can be fully dened by introducing an intersection height. The newly developed model is applied to wind turbine power analysis. The theoretical results agree with the simulations on Bladed, which show that power production variation due to the occurrence of LLJs depends on multiple factors, these are: Obukhov length, friction velocity, wind turbine hub height
and wind turbine rotor radius. The power production of 5 MW reference wind turbine due to the occurrence of LLJs diers from -0.27 to +0.31 MW compared to the expected power production considering constant wind speed across the rotor disc when Obukhov length varied from 60 to 180 m. The results also imply the applicability of the new LLJ model. It is recommended that wind turbine engineers should include LLJs in wind resource assessment. This LLJ model can be further improved by future research with data obtained from onshore meteorology sites. In addition, this research can be extended by applying this model to fatigue and load studies.