As the world shifts toward sustainable energy, offshore wind power is becoming increasingly important because of its ability to capture stronger and more consistent winds. However, offshore wind turbines operate in harsh environmental conditions, exposing wind turbine blades to loads. These conditions accelerate blade degradation through mechanisms such as leading-edge erosion, which disrupt airflow, increase drag, and decrease lift, ultimately reducing the aerodynamic efficiency and energy output of the turbine. Over time, this degradation alters the aerodynamic characteristics of the turbine, posing
significant challenges to control strategies.

This work addresses this challenge by proposing a physics-informed, learning-based framework for modeling blade degradation in wind turbines. Unlike earlier studies that model degradation as a scalar reduction in performance (typically as a uniform downscaling of the power coefficient CP ), the method developed here decouples the impact on lift and drag coefficients. This approach enables the reconstruction of complex, shape-altering changes in the CP (λ) curve. The result is a generalized degradation framework, parameterized through sensitivity coefficients k1 and k2, that represents the degradation
trajectory in a degradation severity space. These sensitivity coefficients are not static but evolve over time, enabling a dynamic and interpretable representation of aerodynamic degradation.

The framework is implemented into the Wind Speed Estimation - Tip-Speed Ratio (WSE-TSR) control scheme, a closed-loop scheme that is capable of learning. Within this control scheme, the degraded
turbine dynamics are compared with an internal reference model to derive estimation errors, which in turn are used to iteratively update the internal degradation parameters. The result is a correcting controller that learns degradation severity and tracks the evolution of the CP (λ) curve over time.

Initial simulation results, based on modeled degradation paths and a realistic leading-edge erosion case,
revealed limitations in the learning process caused by systematic errors in the control scheme. These errors led to discrepancies between the estimated and actual degraded aerodynamic profiles. However, after identifying and analytically correcting for this model error, the learning algorithm demonstrated significantly improved performance. It was then able to reconstruct accurate degraded CP (λ) curves for moderate and severe degradation scenarios. A discussion is provided on the origin of the learning
inconsistencies and how they could be further investigated. The framework underscores the potential of the framework to support integration into digital twin systems for adaptive control in a degraded state.

By combining physics-based simulation, parameter learning, and a control scheme, this thesis presents a framework that enhances our ability to monitor and interpret degradation in offshore wind turbines. In particular, it provides physics-based insight into how degradation mechanisms, such as leading-edge
erosion, affect aerodynamic performance through changes in lift and drag. This approach contributes to the development of more accurate and adaptive digital twin models that can account for suboptimal aerodynamic performance due to blade degradation.

Wind turbine controllers are often designed based on simplified dynamical models. Over time, the turbine properties change because of structural degradation and wear and tear of actuation systems. This leads to an increasing discrepancy between the modeled and actual system properties, resulting in the controllers obtained by model-based techniques not yielding optimal performance for the actual system. Model-free data-driven controller tuning techniques can provide a solution by controller recalibration, and one such promising algorithm is Iterative Feedback Tuning IFT. The IFT algorithm is an automated, online, model-free tuning algorithm for tuning fixed-structure controllers. Previous work with IFT for optimizing wind turbine controllers has been conducted in high-fidelity simulation and has shown that the controller performance can be increased significantly. The IFT algorithm has been successfully implemented on many physical systems, including industrial applications; however, for wind turbine controllers, the algorithm has only been implemented in simulation. This work shows that with IFT, the control performance of a lab-sized experimental wind turbine can be increased, and it analyzes to what extent IFT can fine-tune the parameters of these controllers. This thesis subjects the collective pitch controller and the fore-aft tower damping controller of the experimental turbine to tuning with IFT. It is concluded that for the collective pitch controller, IFT can reduce a predefined cost function and that the algorithm tunes the controller parameters to the proximity of their optimal value, resulting in tighter reference tracking.

As the world shifts towards sustainable energy, wind power stands out as a compelling solution to combat climate change. Advances in wind technology, particularly larger turbines, have made it more cost-competitive. Taller towers and bigger rotors allow modern turbines to access better wind resources at greater heights, capturing more power and reducing costs.

Yet, achieving economically viable designs for taller towers requires reducing material usage. While this reduction decreases weight and costs, it also makes towers more prone to fatigue loading and material damage due to their inherent flexibility. This flexibility can lead to resonant behaviour, particularly in variable-speed wind turbines, exacerbated by factors like rotor mass and aerodynamic imbalances. Such resonances can cause material degradation, structural failure, and thus higher maintenance costs. The challenge of minimizing tower motion in wind turbines has been addressed with both active and passive conventional control strategies, largely for fore-aft tower movement. The current advanced control methods for managing tower side-side periodic loads rely on passive frequency-skipping techniques. However, there is a need for more advanced control methods capable of actively cancelling these loads. Therefore, this thesis aims to develop an efficient convex model predictive control method known as the velocity-based quasi-Linear Model Predictive Control (qLMPC) scheme, specifically designed for actively cancelling side-side periodic loads. This approach omits the need for extensive equilibrium input and state vectors, reducing memory usage while effectively capturing the system’s nonlinear behaviour.

This study uses a Model Demodulation Transformation (MDT) technique to derive wind turbine
tower dynamics, focusing on the real and imaginary parts of the tower top displacement and velocity signals. The goal is to minimize these outputs, reducing tower motions caused by periodic side-side forces. The model includes a wind turbine aerodynamic model, linearized through velocity-based linearization and controlled using a velocity-based qLMPC controller.

Optimization weights are carefully tuned to balance minimizing power output disruptions with mitigating disturbances from side-side periodic loads. Five simulation cases, with varying wind speeds and load profiles, were conducted. The results show the algorithm’s simplicity, efficiency, and suitability for online applications. In all cases, the optimization objective is achieved, with the tower top position and velocity converging to zero, demonstrating the control algorithm’s effectiveness in mitigating side-side periodic loads.

As wind turbines keep increasing in size, there is a growing emphasis on minimizing the fatigue-inducing dynamic loads on their rotating and non-rotating components. Individual Pitch Control (IPC) is one such technique aimed at alleviating these loads, wherein each blade receives an additive, periodic, zero-mean pitch command to compensate for the periodic blade loads that, in turn, arise due to asymmetries in the wind field. Over the course of the last two decades, IPC has undergone extensive research, both from simulation-based studies to practical validations on operational wind turbines. While these studies have pointed to an increased actuator duty cycle and, consequently, a higher risk of fatigue failure of the actuator subsystem, research on constraining IPC pitch action is still nascent. Most of the constrained IPC techniques proposed in the literature are input-constrained and not output-constrained, in that they impose limiting constraints on the individual pitch inputs but not the blade moment outputs. On the other hand, the output-constrained IPC techniques proposed in the literature have employed the use of advanced control strategies, such as MPC and data-driven control, which still await widespread industrial adoption due to a preference for classical control strategies in the industry. This study proposes a classical control-based strategy for constraining single-blade control (SBC) action while compensating for the phase loss in the system. To develop this strategy, several amplitude demodulation techniques are reviewed, and an unconstrained SBC strategy with phase loss compensation is established. High-fidelity simulations demonstrate that the constrained SBC strategy allows for a trade-off between the actuator duty cycle and fatigue loading on the blades, and the inclusion of phase lag compensation improves load mitigation at the targeted frequencies.

In urban areas, vertical-axis wind turbines (VAWTs) show promise due to their omnidirectional design, addressing challenges faced by traditional horizontal-axis turbines (HAWTs). Despite significant progress in urban VAWTs, extensive multidisciplinary research is needed to optimise their efficiency and use in such environments.

This dissertation addresses this gap in four aspects. First, a low-fidelity noise model based on state-of-the-art literature is developed, allowing fast, acceptable, and accurate predictions for preliminary design stages of the primary noise sources on an urban VAWT. Then, a wind speed estimator and tip-speed ratio (WSE-TSR) tracking controller is designed to maximise the power production of an urban VAWT in turbulent wind conditions. This WSE-TSR tracking controller turned out to be an ill-posed problem, impacting the turbine and controller performance in the presence of model uncertainty. Follows the presentation of an approach that combines frequency-domain analysis and multi-objective optimisation, demonstrating its effectiveness in assessing and calibrating torque control strategies, thereby contradicting earlier assumptions and establishing new perspectives on performance optimisation for real-world wind turbines. Based on these collective findings, a decision-making framework is derived, capable of striking a balance between VAWT performance and noise acceptance, allowing for the first time to consider psychoacoustic annoyance as a metric.

In summary, this thesis contributes significantly to advancing the understanding of the complex dynamics of VAWTs, specifically focusing on human acoustic perception nearby, laying the groundwork for the successful integration of VAWTs into urban landscapes.

With the increasing water depths of the new offshore wind farms, the challenging soil conditions, the availability of assets, and other factors, jack-up installation vessels may no longer be suitable to complete the installation scope of work for the new wind farms. Therefore, installation methods and techniques using floating vessels must be further developed to allow safe and efficient installation of wind turbines.

Due to the response of floating installation vessels, excessive motions can be transmitted to the lifted object, making the installation operation very weather sensitive. To increase workability, Heerema Marine Contractors (HMC) has developed the RNA method for wind turbine installation using a semi-submersible crane vessel. This method uses a temporary support structure on the vessel deck (installation tower) where the RNA is fully assembled with the assistance of the GREP (Guided Root End Positioning) tool to constrain the blade root to the top of the installation tower and consequently the hub. Finally, the RNA is installed in a single lift on the WTG permanent support structure. However, the GREP tool is compatible only with a specific range of wind turbine blade dimensions; therefore, for a different-size wind turbine blade a new GREP tool must be designed, fabricated, and mobilised.

This project proposes an improvement to the HMC's RNA method to eliminate the necessity of the GREP tool. That is, a motion-compensated Stewart platform attached to the crane boom, where the blade is fixed to be installed in the hub on top of the installation tower. The project is developed by investigating the initial assumptions; those are crane boom stiffness, blade deflection, installation tower motions and their influence on the vessel's response, and aerodynamic loads acting on the blade during installation. The kinematics (inverse and forward) and dynamics of a Stewart platform are formulated, as well as the mechanical concept of the proposed system and the blade installation process using a Stewart platform attached to the crane boom.

Furthermore, to eliminate the requirement of the GREP tool, a control system is developed to compensate for the blade root motions relative to the hub. The motion control system uses sensors to measure the hub's motions and generate the Stewart platform actuators' set points. Different possible sensor set-ups are evaluated, and a filter is designed to reduce the influence of the sensors' noise. The control system is developed on the basis of feedback PI (Proportional and Integral) and adaptive feedforward control to the actuators (hydraulic cylinders).

It is concluded that it is technically feasible to use a motion- compensated Stewart platform for blade installation in the RNA method. However, the economic aspects of the proposed solution must be investigated.

Drifting, a specialized form of sideslip control, involves intentionally inducing and maintaining a state of oversteer for lateral sliding of the vehicle. While previous research has primarily focused on autonomous drift control, the integration of the driver in the control loop remains largely unexplored. This thesis aims to explore how a vehicle sideslip control system can be designed and evaluated for driver-involved drifting scenarios. To this end, a seven degree-of-freedom vehicle model and a tire model calibrated on data from a test vehicle were used to develop a model-based optimization scheme, leveraging the capabilities of all-wheel torque vectoring to achieve the desired drifting behavior. Phase portrait and real data analyses during drifting maneuvers were conducted to understand drift dynamics and develop the reference generator, which calculates targets for the controller. The reference generator and controller were validated through simulations using IPG CarMaker, drifting along a constant radius circle and a section of a track. The results demonstrate that the developed control system enables stable and controllable drift behavior with a driver-in-the-loop and allows drivers to fully exploit their vehicle’s capabilities.

Operating in real-world conditions, modern large capacity wind turbines often experience off-design situations, enduring dynamic loads characterized by complex unsteady aerodynamics. Key among the challenges in predicting these dynamic loads is understanding the effects of wind shear and turbulence, both individually and in their complex interplay. This research aims to shed light on these phenomena, with an emphasis on their impacts on wind turbine fatigue loads and power production.

The research first provides an in-depth analysis of the influence of atmospheric stability on wind shear profile, aiming to extend the wind shear profile beyond the range of LiDAR measurements. Recognizing the limitations of existing power law and logarithmic law extrapolation methods, the study validates the use of multiple stability correction functions for accurate wind speed extrapolation. Subsequently, the research delves into the intricate effects of wind shear and turbulence on fatigue loads at the blade root of wind turbines, leveraging aeroelastic simulations. This research addresses the challenge of assessing wind turbine suitability for sites where one or several wind climate parameters surpass their design class values. It investigates the potential of the Response Surface Methodology (RSM) to estimate site-specific fatigue loads, a process that conventionally requires extensive aeroelastic simulations. This research also extends the scope to include the assessment of site-specific wind turbine power curves, validating the use of the Rotor Equivalent Wind Speed (REWS) and turbulence renormalization methods. Both methods show promise in estimating site-specific wind turbine power curves using a power curve measured under varying wind conditions.

In essence, this study emphasizes the significant impact of wind shear and turbulence on the performance and longevity of wind turbines. By shedding the light on potential improvements, this study hopes to contribute towards accurate power output and fatigue load assessments.

Due to the increased share of (offshore) wind turbines, more stringent power requirements have been established. Importantly, the low-voltage ride-through requirement states that a wind turbine must remain connected to the electrical grid after a short intermittent grid fault. In the industry and academia many solutions have been proposed, but these are limited by requirements of detailed system knowledge, lack of optimality guarantees, or no testing on high-fidelity models. Therefore, two Iterative Learning Control (ILC) algorithms are presented aimed to solve these issues. The ILC algorithms apply model-free learning based on iterations. Shown is that these ILC algorithms can yield improved performance on a low- and high-fidelity models, with fast convergence of the 2-norm of the output error. The major contributions of this work lie in the application of ILC on grid fault control for wind turbines and in the extension of the norm-optimal ILC to include input constraints using optimisation methods.

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.

In the mission to slow down global warming, the replacement of fossil fuels by renewable energy resources is key. A tipping point in the adoption of renewable energy resources is notable, as they are becoming economically more viable. Offshore wind energy is considered essential in realizing the greenhouse gas emission reduction targets. However, high construction, installation, and maintenance costs cause offshore wind to remain in competition with fossil fuel-based energy. Therefore, further reduction of offshore wind energy costs is crucial.

The most common method of wind energy cost reduction is the upscaling of nominal power ratings by increasing the size of the rotor. However, an alternative way for attaining cost reductions might be the employment of a radically different hydraulic drivetrain concept. Hydraulic-drivetrain wind turbines have the potential of lowering the construction and maintenance cost of wind farms by using a shared hydraulic network. Hydrostatic power, generated by individual wind turbines, is transmitted to a central location where electrical power is collectively generated. However, challenges arise in the control of hydraulic-drivetrain wind farms, e.g., limited pump torque controllability and increasingly complex coupled dynamics for a rising amount of employed wind turbines.

Current control strategies for hydraulic-drivetrain wind farms are developed based on classical control methods. These methods become less suitable for maximizing the collective power of larger wind farms. Therefore, this thesis presents a modification of the existing convex economic model predictive control (CEMPC) framework for conventional wind turbines, such that it becomes compliant to the domain of single-turbine and multi-turbine hydraulic-drivetrain wind farms. The CEMPC method provides computational tractability by circumventing the nonlinear nature of the dynamics. A novelty in this work is that the CEMPC framework is scalable for the control of multiple wind turbines. Moreover, an additional algorithm is proposed to extend the applicability of this framework to control wind turbines containing digital displacement pumps.

In a simulation study, the performances of the developed CEMPC framework applied to a single-turbine and two-turbine hydraulic-drivetrain wind farm are compared for different wind speed scenarios. In all scenarios, the proposed CEMPC framework shows its ability to adequately control the employed wind turbines. When comparing the obtained power production efficiencies with a conventional wind farm employing NREL 5-MW reference turbines, the efficiency of the hydraulic-drivetrain wind farm is 10%-17% lower compared to the reference wind farm. To reduce the levelized costs of offshore wind energy, the hydraulic-drivetrain wind farm concept has to provide at least an equivalent cost reduction over the lifetime of the wind farm. The scalability in the number of controlled turbines makes the proposed CEMPC framework for hydraulic-drivetrain wind farms a promising candidate for realizing the necessary cost reduction.

Climate change is an inevitable fact and governments worldwide have started to recognize this by expanding the contribution of renewable energy sources to our global energy production. Wind energy is currently one of the most attractive solutions for the transition to a more sustainable energy future. Since wind energy is not the only competitor in this journey towards a greener earth, reduction of the Levelized Cost of Energy (LCoE) is an important indicator to stay competitive. The wind industry pursues this goal primarily by increasing the size of wind turbines. As a result, the increased rotor size also comes with a significant increase in static and fatigue loading on the wind turbine’s rotating and fixed structure. To address this problem, the industry continues to investigate the application of alternative and economically more viable drivetrain technologies. An interesting development highlights that the implementation of robust compact hydraulic wind turbine drivetrains presents such an alternative. Hydraulic transmission systems are typically employed in high-load systems and form an excellent opportunity for application in multi-megawatt turbines. The Delft Offshore Turbine (DOT) is a hydraulic wind turbine concept which replaces conventional drivetrain components with more robust hydraulic components. It substitutes the conventional power-train inside the nacelle with a single seawater pump. Pressurized seawater is directed through the high-pressure discharge line to a combined Pelton turbine, connected to an electrical generator on a central multi-megawatt electricity generation platform. Moreover, the low-pressure feed line, directing seawater to the seawater pump, also has to maintain a certain pressure to prevent cavitation in the pump cylinders. This requires energy, which forms an issue since the availability of electrical energy is not self-evident on offshore locations. The goal of the DOT concept is to be completely self-sustainable and independent of external power sources. One solution for this problem is the extraction of hydraulic energy from the pressurized seawater inside the high-pressure discharge line before it is harvested by the Pelton turbine. This mechanism is referred to as the turbine’s
Regenerative Power System (RPS), and adds up to the challenge of hydraulic turbine control. This thesis presents the modeling, control design and control evaluation for this intermediate version of the DOT concept. Besides rotor speed control, also the feed line pressure and the regenerative power system have to be regulated. Decentralized gain scheduled SISO controllers are employed and evaluated in below-rated conditions. Using off-the-shelf components, it is shown that the overall drivetrain efficiency and controllability are increased for operation at maximum rotor torque in the below-rated (partial load) operational region.

Wind energy currently is one of the most attractive solutions to help in the goal of switching to a more sustainable way of energy production. To stay competitive with other forms of energy production, the reduction of the Levelized Cost of Energy (LCoE) is an important indicator. One way of achieving this goal is by increasing the size of the wind turbine. As a result, the increased blade length also comes with a significant increase in fatigue loads present on the wind turbine’s rotating and fixed structure.

Individual Pitch Control (IPC) forms an interesting opportunity in attenuating these fatigue loads. IPC is generally applied with the help of the Multi-Blade Coordinate (MBC-) transformation. The IPC control strategy uses the Out-of-Plane (OoP) bending moments measured on each blade. The MBC-transformation transforms the measured OoP bending moments towards the non-rotating reference frame. As a result the OoP bending moments are transformed into non-rotating yaw- and tilt-moments. The minimisation of these signals is then used as a control objective. Subsequently, the provided non-rotating control signals are then transformed back to the rotating domain to obtain the implementable individual pitch signals.

In the literature this controller synthesis is often employed by two separately operating Single-Input Single-Output (SISO) control loops. Whereby implicitly (or sometimes explicitly) assuming that the yaw- and tilt-moments are sufficiently decoupled to make this type of control viable. In the literature, a recent frequency domain analysis has shown that the coupling is non-negligible. Literature suggests that the introduction of an offset in the inverse MBC-transformation can help decouple these yaw- and tilt-moments, although this offset is usually found in a heuristic manner and its real effects are unknown.

In this study a thorough analysis on the effects of the azimuth offset is given on simplified and high-fidelity models. It is shown that the choice of blade-dynamic model structures has a significant effect on the analysis for maximum decoupling. It is also shown that a first-order model approximation is able to locate the ideal offset of a complex high-fidelity non-linear wind turbine model, which is subsequently verified by simulations and a sensitivity function analysis.

Oxy-fuel gas cutting is the most widely applied industrial thermal cutting process due to its various benefits such as low cost equipment, versatility and flexibility in terms of manual or mechanized operation. It is employed in cutting mild steel plates with thicknesses ranging from 0.5mm to 250mm, to manufacture heavy equipment such as windmills, offshore structures
and structures in high-rise buildings. In the cutting process, a mixture of oxygen and the fuel gas (here acetylene) is used to preheat the metal to its ignition temperature which, for steel, is 700C - 900C (bright red heat) but well below its melting point (1400C). A jet of pure oxygen from the cutting torch is then directed into the preheated area instigating a vigorous exothermic chemical reaction between the oxygen and the metal to form iron oxide (slag in general term). The high pressure oxygen
jet from cutting torch blows away the slag enabling the jet to pierce through the material and continue to cut through the material as the torch progresses.
Now, in order to optimize and automate this cutting process of steel, we will be studying various factors such as speed control of the cutting torch to maintain an accurate angled cut, need of preheater to improve quality of cut, the oxygen pressure required to enable the exothermic process sustained and to pierce deeper. Hence to study and implement the above factors, we need to have the knowledge of temperature distributions in the process at any stage.
The objective of the project is to obtain a dynamic model of the oxy-fuel gas cutting processby employing system identification techniques. The integral part is the study of heat transfer dynamics of the process and investigation of the parameters important in oxy-fuel cutting.
Furthermore, we choose to model the heat sources in oxy-acetylene flame cutting, and the heat conduction in the steel using a numerical simulation based on the Cell Method. The cell method aims at numerical formulation of physical theories from the very outset, i.e. without discretizing the differential equations [1] and hence it is suitable for direct computer implementation. It requires the examination of parameters that are important when cutting different tubes and to develop a control system to automate the entire process with the aim
of achieving improved cutting speed and high quality cut.

Wind turbine technology has become one of the most attractive solutions to generate electricity as well as to cope with the global carbon emissions. Having a prospective future in terms of growing worldwide demands, it becomes more important to reduce the Levelized Cost of Energy (LCoE) of the wind turbine which is generally done by manufacturing the structure, such as the rotor blades and tower, larger. Consequently, this so-called upscaling strategy introduces a drawback in terms of more severe fatigue loadings at both the rotating and fixed components, for example due to wind shear effect, turbulence, and tower shadow, if not dealt with properly.

Fortunately, a solution from the control engineering field, namely Individual Pitch Control (IPC), emerged in response to this challenge. The idea is to rotate the pitch angle of the blades individually depending on the measured loads, normally from the corresponding blade roots. Conventional IPC incorporates the Multi-Blade Coordinate (MBC) transformation which transforms the individual blade load signals from the rotating into the non-rotating reference frame; resulting in the decoupled signals of tilt and yaw axes. This enables the design of Single-Input Single-Output (SISO) Proportional-Integral (PI) controllers that generate pitch actions at these channels; followed by the reverse coordinate transformation such that the individual pitch actions can be implemented. In reality, however, there are interactions between the tilt and yaw axes, which can be coped with by inclusion of a constant azimuth offset into the reverse MBC transformation. Although the straightforward technique is widely practiced in the literature, no validation on this offset and how it affects the cross-coupling prevail whatsoever.

Recently in the literature, an analysis of a wind turbine model with the inclusion of the MBC transformation in the frequency domain revealed the existence of tilt-yaw axes crosscoupling to some extents which invalidates the SISO control system design; confirming that the problem should be treated in the Multiple-Input Multiple-Output (MIMO) control framework. Nevertheless, little attention has been paid to enhance the decoupling by the aforementioned azimuth offset inclusion in which SISO control system design can be made possible.

In this study, an extension of the above-mentioned frequency domain analysis was done by including the azimuth offset. Having successfully shown considerable changes in terms of system’s gain, the investigation was continued by using both the linearized and nonlinear NREL 5-MW baseline wind turbine models in which the offset inclusion to the MBC transformation managed to affect the tilt-yaw interactions. The degree of decoupling assessment and the closed-loop stability analysis were done by means of Gershgorin bands. The results showed that the best decoupling effect, stability margins, and robustness could be achieved by inclusion of the azimuth offset of 20◦ for the particular wind turbine model. More importantly, this study contributes to filling the knowledge gap substantially in the literature by providing the validation of tilt-yaw decoupling by the inclusion of an azimuth offset.

The growing importance of renewable energy to meet the demands of growing population has driven much focus for research in the wind energy sector. Currently most of the power production in the wind energy sector is done using the Horizontal Axis Wind Turbine (HAWT). due to its higher efficiency and reliability as compared to Vertical Axis Wind Turbine (VAWT). However, due to limitations of HAWTs such as the complex yaw mechanism, higher positioning of the center of mass and relatively difficult up-scaling, VAWT are receiving more attention.

Attempt to access high range of power from VAWTs at offshore locations may damage the turbine blades due to increased loads. This thesis is dedicated to reduce the periodic disturbances on the turbine blades of VAWTs without affecting the total power production in a rotation, thereby ensuring the reliability and safe operation of VAWT. A control technique called Subspace Predictive Repetitive Controller (SPRC) is used for the recursive identification to estimate the parameters of wind turbine model and further providing an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the system, following which the system identification has been performed in the lifted domain.

Using the identified model, two types of control approaches have been applied. In the first approach, the objective function is to track a specific reference that indicates blade load. This reference ensures a reduction in peak loads of the VAWT by transferring the loads from their upstream to their downstream part, without comprising on the power production in one whole rotation of VAWT. In the second approach, a general control strategy is used in which the controller is given freedom to choose the pitch trajectory so as to reduce the blade loads. The controller is specified with a power reference that ensures the maintenance of total power. This strategy allows the turbine to be operated in different wind conditions, as a higher weighting is assigned to power production when wind speeds are less than rated wind speeds, and a higher weighting is assigned to the reduction of blade load when wind speeds are more than the rated wind speeds. .

These results show a real potential of the data-driven SPRC approach in wind turbines, and highlights new mechanisms to reduce the turbine loads on \ac{VAWT}s. These mechanisms offer valuable insights into enhancing the functions of VAWTs in a more reliable and damage-free manner.