Floating offshore wind energy is a technology that has gained significant interest over the past few years due to its potential to unlock vast, high-quality wind resources far from shore and in deeper waters, areas that fixed-bottom turbines cannot reach. However, harnessing this resource presents unique challenges, among them are maximising annual energy production (AEP) while minimising blade fatigue damage. These objectives inherently conflict. Aggressive control settings that boost energy capture tend to increase loads, accelerating material fatigue. Crucially, both AEP and fatigue life depend on the turbine controller. By systematically varying a set of key features of a proportional–integral (PI) controller, this thesis sets up a workflow to investigate how controller parameters shape the trade-off between AEP and blade fatigue. To navigate this multi-objective landscape, Pareto optimisation is employed, generating a front of controller configurations that balance energy yield against blade durability. Economic viability is assessed through levelised cost of energy (LCOE) calculations, linking extended fatigue life to deferred maintenance costs and potential improved lifetime. Within the existing literature, where past studies have treated AEP, fatigue, and control in isolation, this work offers a unified framework, demonstrating how slight adjustments in controller settings can unlock significant performance gains. The core contribution of this thesis lies in the framework itself. An end-to-end roadmap is presented, combining metocean data analysis from Utsira Nord, aero-servo-hydrodynamic simulations using SIMA, AEP analysis and fatigue life estimation, Pareto front construction, and LCOE impact assessment. Key results show that blade life can be extended by over 15% with less than a 1% drop in AEP, translating to a meaningful reduction in LCOE of at least 3% under typical economic assumptions. These findings highlight the opportunity to explore the controller parameter space further with this system, paving the way for more finely tuned strategies that optimise the balance between the two objectives.

To achieve net-zero greenhouse gas emissions by 2050, the Global Wind Energy Council (GWEC) emphasizes the need for a significant expansion in global wind power capacity. A key factor of this growth is the upscaling of wind turbines, which increases the swept area and exposes the blades to higher wind speeds at higher altitudes, thereby increasing energy yield. However, this upscaling introduces significant control challenges. Larger wind turbines increase aeroelastic complexity, with stronger nonlinear dynamics arising from increased blade flexibility and more significant wind shear across the expanded rotor diameter. Additionally, the increased rotor inertia delays the system response, making rotor speed regulation more difficult under varying wind conditions.

This thesis proposes an adaptive closed-loop Subspace Predictive Control (SPC) framework designed in an attempt to handle the complexity of larger, nonlinear wind turbines. Closed-loop SPC is a direct Data-Driven Predictive Control (DDPC) method that does not rely on explicit state-space modeling, but instead uses measured input-output data to predict future outputs and compute optimal control action. For the optimal control action, it sets up a receding horizon optimization that regulates above-rated rotor speed. This thesis focuses on the above-rated region, where aeroelastic complexity becomes more pronounced due to higher wind speeds and greater wind speed variations, posing significant control challenges.

To capture time-varying and nonlinear behavior more effectively, the closed-loop SPC incorporates Recursive Least Squares (RLS). The controller adapts to time-varying conditions through online parameter estimation using RLS, which updates a locally linear model in real time. Three RLS variants are examined: standard RLS without forgetting, RLS with exponential forgetting, and RLS with directional forgetting. Standard RLS weighs all past data equally, which may be effective when the system dynamics remain stationary but limits adaptability to changing conditions. Exponential forgetting addresses this by placing more weight on recent data, improving adaptiveness, but at the potential cost of losing parameter estimation accuracy in less-excited directions. Directional forgetting refines this further by applying forgetting selectively along the directions of incoming data, preserving excitation in recently unexcited directions and enhancing estimation robustness.

To reduce the phase lag introduced by increased rotor inertia, wind preview information is incorporated into the closed-loop SPC as a feedforward signal. This wind preview is included in the receding horizon optimization problem, enabling the controller to anticipate and proactively respond to upcoming wind changes. Additionally, the wind preview is demonstrated using more realistic measurements obtained through a LIDAR simulator.

The adaptive closed-loop SPC is validated on the DTU 10 MW reference turbine using QBlade, a high-fidelity wind turbine simulator. Various wind scenarios, including gusts, ramps, and turbulent inflow, are evaluated with and without wind preview feedforward. Results demonstrate that the inclusion of wind preview significantly improves rotor speed tracking performance and reduces pitch activity. This improvement is also observed when more realistic LIDAR wind measurements are used in simulations with a turbulent wind field. In the conducted wind cases, among the RLS-based adaptive closed-loop SPC strategies, exponential forgetting combined with wind preview consistently outperformed the other RLS approaches across all scenarios evaluated in this thesis. These findings demonstrate that introducing adaptiveness through forgetting, together with feedforward wind information, can enhance closed-loop SPC performance in rated rotor speed tracking.

Data-Driven Predictive Control (DDPC) has emerged as a promising alternative to Model Based Control (MBC), enabling direct control using Input-Output (I/O) data without requiring explicit model identification. This thesis advances DDPC by bridging the gap between theoretical developments and real-world implementation. The study focuses on four algorithmic variants: Subspace Predictive Control (SPC), Closed Loop Subspace Predictive Control (CL SPC), Recursive Closed Loop Subspace Predictive Control (R-CL SPC), and Constrained Recursive Closed Loop Subspace Predictive Control (CR-CL SPC), to enhance adaptability and ensure real-time feasibility.

A key insight in computational efficiency is the R-CL SPC algorithm, which updates system parameters online using recursive least squares estimation combined with Givens rotations. This reduces computational complexity by avoiding large-scale matrix inversions at each time step. Additionally, the CR-CL SPC variant introduces constraint handling via a Quadratic Programming (QP) solver, enabling input and output constraints.

These algorithms’ performances were evaluated through simulation studies and real-time experiments on a piezo-actuated beam setup, where the control objective was to suppress the first two natural vibration modes. The R-CL SPC algorithm demonstrated a strong balance between computational efficiency and control performance, achieving execution times below 0.2 ms while maintaining effective vibration damping. Meanwhile, though at a higher computational cost, CR-CL SPC validated constraint enforcement capabilities.

This thesis demonstrates that adaptive SPC algorithms can be successfully implemented on real-world hardware. The results contribute to the growing knowledge on direct DDPC strategies and provide a foundation for their broader application in real-time, constrained control systems.

Wind turbines have been deployed worldwide to address the growing energy demand while also targeting ambitious plans for the energy transition to renewable sources. Although wind energy is a key renewable energy source, it still faces unsolved challenges and offers ample room for innovation. One major concern is its variability, with the maximum power available fluctuating over time. Importantly, wakes from upstream turbines also contribute to reducing the power availability for downstream turbines. These wake effects are magnified by the construction of large wind farms with densely spaced wind turbines, aiming to efficiently use the allocated space.

Wind farm control strategies can be implemented to mitigate these wake effects and optimize wind farm power generation. In scenarios requiring on-demand response, such as those explored in this thesis, wind turbines are leveraged to provide flexibility, constrained by their maximum power availability. The power delivery of wind power plants upon request is facilitated by a closed-loop wind farm controller, providing active power control at fast timescales. Active power control involves adjusting the resource's active power to assist power grid operators in balancing energy supply and demand, thereby improving energy security.
Our proposed closed-loop control solution provides superior response capabilities by
compensating for reduced power availability, ultimately enhancing the reliability of on-demand power generation.

The wind variability across turbines, intensified by wake effects, contributes not only to attaining fluctuations in power generation but also to fluctuations in structural loads on the turbines. Amplified by wake-induced turbulence, this structural load variability across turbines leads to uneven degradation of turbine components over the long term. In offshore scenarios, where accessibility is limited and maintenance operations must be minimized due to higher costs compared to onshore counterparts, controlling turbines to prolong their lifetime is of significant interest. In this thesis, this aspect is addressed at both the wind farm and wind turbine levels.

At the farm level, we propose that farms fulfilling grid energy demands must also balance the aerodynamic forces of their turbines to evenly distribute structural degradation among them.
This can be achieved without compromising the power generation when the turbines operate below their maximum energy extraction capacity. We have demonstrated that by implementing a real-time feedback loop, it is feasible to balance aerodynamic loads while meeting wind farm energy demands, albeit limited by wind availability. Moreover, we have demonstrated that balancing aerodynamic forces is advantageous for active power control in a wind farm affected by wake effects, compared to simply distributing power requests uniformly.

At the wind turbine level, we introduced two wind turbine controllers designed to individually restrict real-time aerodynamic loads as a surrogate of structural loads in turbine components. These controllers are referred to as load-limiting controllers. The first load-limiting controller employs an optimal control approach. The operator can impose structural load constraints, using a convex model predictive control for power tracking. The second controller, which is more practical, utilizes a switching mechanism with integral control that allows the operator to prioritize a structural load setpoint over a power demand setpoint. This prioritization aims to reinforce structural safety in situations where turbines are compromised from their design conditions. This could be a consequence of numerous factors, such as unpredictable degradation, installation issues, vessel collisions, and others.

As wind turbines prove to be a viable, reliable, and eco-friendly energy source, new wind farm projects are becoming more ambitious, incorporating a larger number of turbines than ever before. Additionally, there is a substantial growth in wind turbine installations within existing wind farms. This growth in the number of turbines poses an implementation challenge for wind farm control systems. Similar challenges have been encountered in controlling other large-scale systems with collective goals, where agents must instead make decisions based on partial information due to communication limitations in processing or transmission.

Anticipating this implementation challenge, we transition from a centralized to a distributed wind farm control solution. Taking advantage of the time scale inherent in typical wind farm controller implementations, we exchange information with neighboring turbines rather than a central workstation. Our aim, in particular, is not to gather partial information but to achieve consensus across the entire farm. However, our control methodology has a negative implication - the addition of delays - which is carefully examined by the derived stability condition for the design and is assessed through simulations. Notwithstanding these delays, the proposed solution is fully distributed and has been demonstrated to be both simple and effective, facilitating the application of our control solutions in large-scale wind farms.

Lastly, we validate our wind farm control solutions through experiments conducted with scaled wind turbines in full-wake conditions. In this way, we verify the benefits of our control solutions not only through high-fidelity simulations but also through real-world experimentation.

The work presented in this thesis emphasizes the importance of wind turbine controllers capable of offering demanded power to the grid while enhancing reliability in power delivery and addressing structural and maintenance concerns. We introduce closed-loop wind farm controllers designed to handle these challenges. Furthermore, we expand the implementation through a distributed approach on one front, while on the other front, we validate the solutions by means of experiments. The findings from this research contribute to the efficient operation of future wind farms by employing feedback control strategies across clusters of wind turbines.

Wind energy has the potential to accelerate the transition from a carbon-based to a carbonfree energy supply system. This transition is essential in the ongoing global effort to combat the growing impacts of climate change. Due to the availability, as well as the increasingly competitive cost, the wind energy industry has enjoyed rapid growth in terms of installed wind capacity. Where the first onshore wind farm was composed of nearly 5000 turbines in 1980, with a capacity of 576 MW the current largest offshore wind farm in development has a design capacity of 1400 MW with only 100 turbines. Almost all offshore wind farms currently in operation, under construction, or in the planning phase are designed with bottom-fixed turbines and are in relatively shallow water.

The total available wind energy capacity increases significantly when deeper waters can also be accessed by wind turbines and wind farms. For these areas, floating wind turbine technology will play an essential role. When they are deployed in similarly sized wind farms as bottom-fixed wind farms they will also encounter challenges currently faced by these bottom-fixed farms. One of these challenges is the wake interaction between turbines, a cause of significant efficiency losses for a wind farm. The field of wind farm flow control aims to develop a control solution that can alleviate the negative effects of the wake interaction between turbines...

Our planet is warming up with potentially disastrous consequences. The main cause of this climate change is the increase of greenhouse gases in the atmosphere, which are mainly emitted by burning fossil fuels to generate energy. Therefore, fossil fuels need to be substituted to reduce emissions from the energy sector. Renewable energies offer an alternative with reduced emissions. Among these, wind and solar energy are growing the fastest. This thesis investigates how the wind energy supply can be increased by improving its operational efficiency.

There are several reasons why a wind turbine may not generate its maximum capacity, one of them being its placement. Turbines are often placed in farms, which allows the collective use of infrastructure and minimizes land usage. The downside is that the turbines influence one another: As a turbine extracts energy from the wind, an area with lowered wind speed develops downstream. This area is called wake, and other turbines affected by it will generate less energy.

The ways to address this problem can be split into pre- and post-construction measures. Pre-construction the wind farm layout can be optimized, and post-construction control strategies are needed to operate the wind farm optimally. These strategies fall under the term wind farm flow control and aim to manipulate the flow between the turbines to optimize the farm performance. A turbine’s wake can be altered by changing the turbine’s resistance to the flow or by misaligning the turbine with the wind direction. The former leads to a faster wake recovery, and the latter results in a redirection of the wake, also called wake-steering.

The current state-of-the-art of wind farm flow control is to utilize wake-steering in an open-loop control configuration. To this end, steady-state engineering models of the wake are used to optimize the farm set points offline. This is done for a selection of atmospheric conditions and the set points are stored in a lookup table (LuT). During operation, the flow conditions are used to look up the precomputed turbine set points. A problem with this approach arises as open-loop control assumes a perfect match between the model and the actual conditions in the field. There are reasons why this might not be the case: (i) There is an inevitable modeling error, which creates a mismatch between the model and the reality; (ii) conditions can arise that are offline not accounted for, e.g., time-varying atmospheric conditions or layout changes due to turbine downtime.

These problems can be addressed by closing the loop. In closed-loop control, measurements are used to continuously correct the model and to adapt it to the current state of the true wind farm. Optimal set points are then found based on the current model state. The control strategy can, therefore, react to new conditions. A challenge is that the optimization needs to happen online and requires a way to incorporate sensor data into the model. Previous work has designed closed-loop approaches using the same computationally cheap steady-state models that were previously used for open-loop control. This was achieved by adapting the parameters of the model based on the mismatch between the observed and predicted measurements, like power generated. A core assumption these models make is that the flow is in a never changing steady state. However, flow conditions do change, and the large spacing between turbines leads to minutes of delay between the control action the upstream turbine takes and the effect that the downstream turbine experiences. The question arises: What could be achieved using dynamic wake models instead of steady-state ones? These can incorporate wake dynamics, which could lead to better decision-making.

This thesis designs a closed-loop model-predictive wind farm flow control strategy based on a dynamic wake model to maximize the energy generated by a wind farm under time-varying conditions. The thesis is comprised of three building blocks: (i) The development of a dynamic wake model, (ii) the derivation of a sensor fusion strategy to identify the state of the flow field, (iii) the composition of a control strategy that uses the model to optimize the control set points. The building blocks are then connected to form the closed-loop control strategy.

The model building is based on the further development of an existing model, which utilizes a steady-state wake model and reintroduces flow dynamics. In the first step, the underlying wake model is substituted by a three-dimensional one, and the formulation is adapted to heterogeneous flow conditions. In the second step, the model is reformulated as a framework that links to an arbitrary wake model. This is done to profit from advancements in the steady-state model development and to significantly decrease the computational cost of the model. In the third step, the dynamic model is compared to a steady-state one in a set of high-fidelity wind farm simulations under time-varying conditions based on field measurements. The results show that the dynamic model does provide a better match with a simulated wind farm.

In the second part of the thesis, a state estimation methodology is introduced. To this end, an ensemble approach is adopted, where the multiple versions of the model are simulated in parallel. The correlation between the ensembles is then used to correct them based on the predicted and measured wind direction and power measurements of the turbines. A byproduct of the ensemble approach is that each estimated state also has an uncertainty based on how much the ensembles agree on its value.

The third part of this thesis investigates the control and optimization problem. This part focuses on the cost function formulation and the behavior it leads to. In a steady-state frame, the delays do not have to be taken into account, but in a dynamic formulation, they become a challenge. We, therefore, propose a cost-function formulation that synchronizes the control actions with their effect at the downstream turbines. This leads to a series of smaller optimization problems instead of one larger one.

The three building blocks of this thesis are then tested in a case study: The closed-loop controller is employed to maximize the energy of a ten-turbine wind farm under time-varying conditions. Both the farm layout and wind direction time series are based on field conditions. The controller generates an overall energy gain of up to 4% over the baseline using noise-free wind direction measurements.
This is on par with the steady-state approach. However, the closed-loop approach is found to be more robust to disturbed wind direction measurements - Where the performance of the steady-state approach decreases to 1.7% due to the sensor noise; the closed-loop approach still achieves a 2.5% gain.

The conclusion of the work presented in this thesis is thereby: Closed-loop wind farm flow control based on a dynamic engineering surrogate model leads to a more accurate and robust state estimation of the wind farm flow field but, given no preview, does not necessarily lead to a higher energy generation than what can be achieved with steady-state models.

The devastating effects of climate change are becoming more evident each day. We are running out of time, and our best collective effort is needed to revert the situation and ensure a sustainable and healthy future. Renewable energies are a key enabler for such a future. Wind energy has attained remarkable progress in the last decades, and the growth required to meet future net-zero scenarios is extraordinary. Wind turbine technology is rapidly evolving towards larger rotors and power ratings, resulting in much higher torque density demands for the drivetrain in general and the gearbox in particular. Maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential for modern highly loaded gearbox designs with significant dynamic interactions. The contribution of this dissertation can be summarized by its goal: to develop a method to measure dynamic mechanical torque in geared wind turbines. A key requirement was set to enable fleet-wide implementation to monitor torque throughout the complete service life of the wind turbines. This dissertation proposes a method based on strain measurements on the outer surface of the static first-stage ring gear that overcomes the main drawback of traditional methods. A series of experiments are presented, ranging from proof-of-concept tests conducted using gearbox test benches to an extensive field validation campaign. These experiments have advanced the technology readiness level and demonstrated the accuracy and robustness of the proposed method, which is now deemed ready for commercial implementation. Future research should explore improving drivetrain loading using dynamic mechanical torque measurements with novel data-driven control strategies. Additionally, recursively tracking operational deflection shapes over time is recommended for fault detection.

The global demand for renewable energy is driving the rapid growth of the wind energy sector, with wind farms increasingly preferred over stand-alone turbines due to their operational and economic benefits, such as reduced deployment costs, operational efficiencies, and minimized land use. Effective wind farm operation depends on optimizing energy extraction, necessitating accurate energy yield predictions. Traditional wind turbine design focuses on maximizing individual performance, often overlooking interactions within wind farms that can lead to suboptimal overall performance due to wake effects. Wakes are characterized by reduced wind speeds and increased turbulence downstream of turbines, negatively impacting the performance of downstream turbines. Managing these wakes is crucial for improving overall wind farm efficiency.

Wind Farm Flow Control (WFFC) aims to enhance wind farm performance by managing these wake effects through control strategies. This thesis addresses the need for integrating advanced control techniques, such as Individual Pitch Control (Helix Approach), into engineering wake models to improve wind farm simulations and real-time control applications.

To achieve this, the research identifies the most suitable wake model by evaluating various models implemented in PyWake, selecting the Super Gaussian model for its superior accuracy in representing the wake behavior in the whole region. A robust calibration framework is developed, balancing precision and computation time. The thesis investigates the stability of the calibration process, addressing common optimization challenges such as local minima, and identifies strategies to ensure the global optimum configuration.

The sensitivity of model parameters to different inflow conditions is analyzed, allowing the calibration process to be generalized across a range of wind speeds within the partial load region of the power curve. This generalization reduces the need for frequent recalibration, streamlining the process. The research further explores the responsiveness of different control configurations, with a focus on the helix approach, to establish correlations between control signal amplitudes and model parameters.

The newly extended model incorporates helix approach information through parameter calibration for varying pitch angle amplitudes, demonstrating a polynomial relationship between model parameters and control signal amplitudes. This integration reduces the calibration requirement to a single procedure, enhancing model adaptability.

Finally, the extended model is tested for optimizing online wind farm performance across different configurations. The helix approach shows significant improvements in reducing wake losses and increasing overall wind farm efficiency, particularly in turbine clusters. Comparisons with traditional "greedy control" highlight the helix approach's advantage, with power output increasing by up to 25% in clusters of five turbines when upstream turbines utilize helix control.

This thesis contributes to the field by providing a comprehensive approach to wind farm modeling and control, facilitating the practical application of advanced control strategies. The integration of control data into engineering wake models not only enhances calibration processes but also broadens their practical applications, paving the way for more efficient and sustainable wind farm operations.

This thesis investigates the concept of turbine repositioning to enhance energy production in floating wind farms. Due to the dense deployment of floating turbines, downstream units could potentially experience reduced wind speeds caused by the wakes of upstream turbines, leading to decreased power output—an effect known as the wake effect. To address this, methods such as power de-rating and yaw-based wake redirection have been extensively studied. Notably, for floating wind farms, the ability of turbine bases to move within a certain range has prompted the proposal of turbine repositioning as a novel wake mitigation strategy.

This study delves into optimal control strategies for turbine repositioning, with a particular emphasis on manipulating rotor yaw angles. It introduces two primary repositioning strategies: static repositioning, suitable for farms with relatively slack mooring lines, and dynamic repositioning, for those with tighter lines. Alongside, the research proposes optimization methods to identify the optimal control sequences for each repositioning strategy. Lastly, by analyzing rotor yaw angle control sequences in the frequency domain, this study distinguishes the frequency component crucial for repositioning turbines from that steering the wakes. The findings provide significant insights into enhancing the cost-effectiveness of power production in floating wind farms through effective wake interaction management.

The offshore wind energy sector has experienced remarkable growth in recent years, driven by increasing demand for renewable energy sources and technological advancements. As wind farms expand and turbine sizes increase, challenges associated with wake effects and turbine interactions become more pronounced. Therefore, wind farm control strategies have been developed to mitigate the negative effects of wakes, consequently enhancing overall power production or reducing fatigue loads. The helix approach has emerged as a promising method for controlling wake dynamics, shaping the wake into a helical pattern to increase wake mixing further downstream.
In this context, phase synchronisation has emerged as an extension of the helix approach, simultaneously applying the helix on up- and downstream turbines. By leveraging the existing helical periodic components induced by the upstream turbine, phase synchronisation can initiate the helix at the downstream turbine with less pitch action. % while improving overall power performance.
However, power is still compromised at the upstream turbine due to the execution of the helix. Consequently, the potential for improvement arises with the integration of phase synchronisation onto the meandering effect—a phenomenon often already present at the downstream turbine. This effect yields benefits similar to those of the helical wake concerning wake recovery, offering the potential to improve overall power performance without power loss at the upstream turbine.
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This thesis has developed a phase synchronisation method to track the time-varying excitation frequency inherent to the meandering effect. Enhancements were made to a SOGI-PLL, enabling the extraction of phase, amplitude, and bias from periodic loads, which forms the basis of our closed-loop control strategy for the downstream wind turbine. The improvements led to the development of the ESOGI- and MSTOGI-HTan-QT2PLL methodologies, which address internal disturbances caused by offsets and enhance system stability, while accurately tracking of the phase during phase jumps and frequency steps and ramps.
The methodologies were tested at different levels of fidelity. First, a low-order study validated the effectiveness of the proposed method during uncertain conditions, which showed better performance of the MSTOGI-HTan-QT2PLL methodology. Then, the results of the higher-fidelity experiments demonstrated that our method could successfully amplify the meandering effect without loss in power, while attenuation led to reduced fatigue loads with minimal power loss. These results show the potential of integrating phase synchronisation on the meandering effect.

Floating Offshore Wind Turbines (FOWTs) pave the way to accessing deep water regions with abundant wind resources that are unreachable to bottom-fixed turbines. This technology is not widely deployed due to the increased cost of producing energy. A suitable control strategy can improve the power quality and extend the lifespan of the FOWT, resulting in a reduction of the final cost of energy. However, FOWTs face a specific set of control challenges in the above-rated operational region, such as the negative damping issue, rough environmental conditions, and increased model complexity due to both the floating structure and the increase in wind turbine size. Advanced control strategies are the most suited to resolve the negative damping problem, but obtaining a control model that balances low complexity with good accuracy is an increasingly difficult task. To mitigate the effect of environmental disturbances, feedforward control using wind preview is most commonly employed. Although waves have been shown to increase rotor speed oscillations and turbine loads, wave preview-based methods remain scarce.

This thesis implements a model-free, feedforward controller based on wave preview for the above-rated region of a FOWT. The controller uses a preview of wave forces acting on the floating platform and aims for simultaneous rotor speed regulation and platform motion reduction using collective blade pitch control. As a model-free approach, a modified Data-enabled Predictive Control formulation that considers past and future information about measurable disturbances is proposed. The controller is implemented with a linear model of the NREL 5-MW wind turbine installed on the OC3-Hywind spar-buoy platform and tested in several cases. The effectiveness of the wave feedforward data-driven controller is evaluated in a high-fidelity environment using the QBlade simulator. A decrease in rotor speed variance of 67% and platform pitching motions of 71% is obtained, at the cost of a 7-fold increase in blade pitching effort compared to the baseline controller. In turbulent wind conditions, the wind proved to be the dominant disturbance, and including both wind and wave previews in the controller is recommended. This work demonstrated the feasibility of a model-free feedforward control strategy for wave effect mitigation in FOWTs. Further efforts are required to adapt this strategy to closed-loop operation and to validate its effectiveness across the entirety of the above-rated region.

Wake losses in wind farms, caused by closely spaced turbines, reduce downstream power production and overall efficiency. These losses can be mitigated through wind farm flow control strategies, specifically wake steering, which involves yaw control to misalign turbines with incoming wind, and redirecting wakes to enhance overall power output. This thesis aims to develop a real-time, model-based controller that addresses operational and environmental challenges in large-scale wind farms. The controller adapts to off-design conditions such as changing atmospheric factors and offline turbines, making it more effective than traditional controllers based on pre-optimized lookup tables. The research modifies engineering wake models to account for heterogeneous wind conditions, improves the computational efficiency of the Serial-Refine optimization strategy for real-time use, and incorporates tuning of wake model parameters to ensure adaptability. Additionally, the impact of offline turbines on optimal yaw misalignment angles is examined. Results indicate that adapting wake models using sensor data improves power predictions in non-uniform wind environments, while the distributed optimization framework for real-time yaw adjustments performs comparably to centralized methods. Incorporating offline turbines into the control strategy further boosts power output, leading to increased revenue for the wind farm. This research advances wind farm flow control by developing control policies that are tailored to dynamic, real-world site conditions.

The clustering of wind turbines in a wind farm results in overall efficiency losses as downstream wind turbines operate in the wake of their upstream neighbours. Wind farm flow control (WFFC) strategies have emerged to reduce these wake effects with the goal of maximising overall performance. Dynamic induction control (DIC) aims to enhance the wake breakdown and restore the wake's energy content through dynamic thrust variations. The control signals are often found through the economic model predictive control (EMPC) method, which relies on an internal model to incorporate future system behaviour in the determination of the next optimal control input. These models are designed to capture the most dominant wake characteristics while remaining computationally efficient. We employ the two-dimensional free-vortex wake (FVW) model presented from [1], which models the wake through vortex element pairs released from the edges of the actuator disc. The power of the two-turbine wind farm is maximised through EMPC, improving performance by 9.64\% over greedy control simulations. However, the EMPC method inherits finite horizon effects, resulting in large control horizons to optimise. In this study, we address these limitations by employing an absolute sine parameterisation in the FVW model to limit the finite horizon effects and reduce the dimension of the optimisation problem. The significant dimension reduction allows for a grid search to find the optimal infinite horizon steady-state solution, improving the mean steady-state performance by 2.43\% over the baseline results from [1]. Additionally, we focus on converging towards this optimum through finite horizon EMPC optimising over the amplitude and offset. Grid search analyses reveal sensitivity towards initialisation due to the appearance of local minima around the infinite horizon optimum. A maximum success rate is realised for very large control horizons, maximising the probability of converging towards the infinite horizon optimum. Accounting for the inherited system delay in the objective function also realises a maximum success rate but for shorter control horizons, which significantly decreases the simulation time. The final controller design terminates simulations ten times faster through the absolute sine parameterisation compared to the baseline simulation from [1] while maximising the probability of convergence towards the infinite horizon optimum.

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.

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

Wind energy becomes more and more popular since it is environmentally friendly. Wind farm control is one of the most popular topics and it works on steering the wind farm to extract energy from wind as much as possible. Generally, the model capturing wake effects between turbines in the wind farm plays a role in wind farm control. The existing FLORIS model is considered suitable for wind farm control due to the fact that it has the ability or potential to capture wake features with reasonably computational costs. A drawback of the FLORIS model is the lack of dynamics, which is improved by developing the FLORIDyn model.

This thesis focuses on a Gaussian FLORIDyn model. The objective is to explore the possibility of improving the model accuracy by quantifying the associated uncertainty in the model parameters. Uncertainty quantification consisting of sensitivity analysis and Bayesian calibration is conducted based on a 3-Turbine case simulation using the UQLab software. Since a MCMC algorithm associated with Bayesian calibration requires to evaluate the FLORIDyn model multiple times, it can result in massive computational expenses when directly applying the computational model to the simulation. To deal with this, a surrogate model is first constructed to replace the original model. This thesis assesses two types of approaches for surrogate model construction which are the Kriging-based approach and the PCE-based approach. One approach is chosen after the comprehensive comparison in terms of accuracy and efficiency. The constructed surrogate model is then applied to the sensitivity analysis using Sobol' indices to investigate how each model parameter of interest affects the model output. Last, the high-fidelity SOWFA data are used as experimental data for Bayesian calibration. Compared to non-calibrated model outputs, calibrated model outputs are closer to the SOWFA data, which means that the accuracy of the FLORIDyn model is improved.

Wind farm control is an area of research that looks to maximise the performance of individual but most importantly the collective performance of wind farms. The performance of turbines located in a wind farm is heavily dependent on the effects of turbine wakes, and improving the efficiency of wind farms by mitigating the wake effects is an ongoing and promising field of research. Simulating the wake effects of a turbine is generally done with computationally expensive physics based modelling techniques like CFD simulations. The computational complexity of these models limits the scope of optimisation that can be performed on wake mitigating controls strategies such as dynamic induction control. A substitute engineering model like the free vortex wake model could provide better insight into dynamic induction control by virtue of its computational efficiency allowing a wider optimisation parameter space. In this thesis an optimisation framework based on a 2D implementation of the free vortex wake model and genetic algorithm optimisation is used to investigate dynamic induction control for a simple two turbine wind farm beyond the research that has been done up to this point. While yet to be confirmed with higher fidelity simulations, initial results corroborate earlier findings for simple dynamic induction signals and additionally indicate that dynamic induction control could benefit from multiple harmonics. The optimisation framework achieves its goal in allowing a wider parameter space to be searched for optimisation, even on consumer grade desktop hardware, and shows potential as a tool for further investigation of dynamic induction control.

The placement of wind turbines in wind farms is economically beneficial to the industry. Consequently, the wake generated by the upstream turbines introduces interactions with downstream turbines that significantly affect power generation. However, it is common practice to control the individual turbines to operate at their optimal settings and neglect the wake effect. Turbines in a wake experience lower wind speeds and an increase in turbulence. This results in lower power generation and increased fatigue loads on the downstream turbine. Wind farm control aims to increase overall power generation and reduce fatigue loads by considering all turbines in the wind farm.
Recently, numerous studies have been conducted to control the wake itself. The helix approach is one such strategy and seeks to initiate better mixing of the wake. For this, it uses IPC to create sinusoidal blade bending moments acting on the rotor disc. This in turn creates a helical structure in the velocity of the wind, allowing it to better mix with the surrounding energy-rich wind. As a result, the waked downstream turbine experiences an increase in power due to the reduced velocity deficit in the wake. An extensive load analysis of the helix method has shown that the rotational frequency of this helical wake can be measured in the bending moments of the waked downstream turbine. To further develop the field of dynamic wake mixing, this work investigated the initialization of the helix method in a downstream turbine by amplifying these signals through synchronization with the incoming helical wake.
This method was tested in simulation environments with increasing levels of fidelity and number of turbines simulated. The results with low fidelity showed that synchronization could be achieved without loss of performance. In addition, less pitch actuation was required to generate large bending moments due to amplification. The final experiment was conducted for a three-turbine setup and was performed in a high-fidelity model using CFD methods. Here it was found that -90 degrees out-of-phase synchronization with the incoming helical wake resulted in a 9.8% increase in power generation in a turbine further downstream. These results demonstrate the clear potential of wake synchronization in a multi-turbine setting. This study, therefore, contributes to the field of dynamic wake mixing by introducing a novel field of wake synchronization.

Maximizing the extraction of energy from wind farms with ever higher densities is becoming increasingly more important in order to achieve climate targets and simultaneously preserve nature. Improving the yield of a wind farm can be achieved by optimizing the layout, applying control, especially wake steering through yaw control has shown great results, or even combining the optimization of the layout and control into one joint optimization. In this thesis, a case study is performed on the Dutch wind farm ’IJmuiden Ver’ to investigate the real-world applicability of joint optimization. The employed method uses the genetic algorithm, capable of handling the discontinuous domain, and an improved version of the geometric yaw relationship, making coupled or nested optimization redundant. In the IJmuiden Ver case, the levelized cost of electricity (LCOE) of a joint optimized layout compared to a sequential optimized layout is around 0.3% better, even remaining around 0.2% to 0.3% better when shrinking the domain to give nature more space. This shows that joint optimization is applicable in practice and has the potential to increase the yield of a wind farm substantially without significantly increasing the computational intensity of the wind farm layout optimization problem (WFLOP).