Vertical-axis wind turbines (VAWTs) are considered promising solutions for urban wind energy generation due to their design, low maintenance costs, and reduced noise and visual impact compared to horizontal-axis wind turbines (HAWTs). However, deploying these turbines close to densely populated urban areas often triggers considerable local opposition to wind energy projects. Among the primary concerns raised by communities is the issue of noise emissions. Noise annoyance should be considered in the design and decision-making process to foster the social acceptance of VAWTs in urban environments. At the same time, maximising the operational efficiency of VAWTs in terms of power generation and actuation effort is equally important. This paper balances noise and aero-servo-elastic performance by formulating and solving a multi-objective optimisation problem from a controller calibration perspective. Psychoacoustic annoyance is taken as a novel indicator for the noise objective by providing a more reliable estimate of the human perception of wind turbine noise than conventional sound metrics. The computation of the psychoacoustic annoyance metric is made feasible by integrating it with an accurate and computationally efficient low-fidelity noise prediction model. For optimisation, an advanced partial-load control scheme – often used in industrial turbines – is considered, with the Kω2 controller as a baseline for comparison. Optimal solutions balancing the defined objectives are identified using a multi-criteria decision-making method (MCDM) and are subsequently assessed using a frequency-domain controller analysis framework and mid-fidelity time-domain aero-servo-elastic simulations. The MCDM results indicate the potential application of this controller in small-scale urban VAWTs to attain power gains of up to 39 % on one side and to trade off a reduction in actuation effort of up to 25 % at the cost of only a 2 % power decrease and a 6 % increase in psychoacoustic annoyance on the other side compared to the baseline. These findings confirm the flexible structure of the optimally calibrated wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller, effectively balancing aero-servo-elastic performance with noise emissions and marking the first instance of integrating residential concerns into the decision-making process.@en

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.@en

This study investigates the impact of struts and a central tower on the aerodynamics and aeroacoustics of Darrieus Vertical Axis Wind Turbines (VAWTs) at chord-based Reynolds numbers of 8.12 × 104. A 2-bladed H-Darrieus VAWT is used, featuring a 1.5m diameter, a solidity of 0.1 and a blade cross-section of symmetrical NACA 0021. The turbine design is kept simple and straight-bladed which is essential for isolating and analyzing the specific effects of struts and a tower. The high-fidelity Lattice Boltzmann Method (LBM) in PowerFLOW 6-2020 and the mid-fidelity Lifting Line Free Vortex Wake (LLFVW) method in QBlade 2.0 are employed, with the mid-fidelity method providing a faster analytical tool for insights into the turbine performance. Firstly, both the LLFVW (mid-fidelity) and LBM (high-fidelity) methods effectively capture the general trends observed in VAWT power performance. However, the former predicts mean thrust values that are approximately 10% higher, and mean torque values that are approximately 19% higher, in comparison to the latter. Subsequently, the former predicts lower streamwise wake velocities relative to those predicted by the latter. These differences increase in configurations that include struts and a tower (to 30% - 31%). Secondly, the presence of struts and a tower leads to a reduction in both mean power (by 15% to 55%) and thrust (by 3% to 3.6%), with a further small decrease observed when doubling the tower diameter (power and thrust both by 0.5% to 3%). The struts predominantly affect the spanwise distribution of blade loading, while the tower impacts the azimuthal variation of blade loading. Additionally, the addition of struts and a tower reduces low-frequency noise (50-200 Hz) while increasing high-frequency noise (> 300 Hz). The observed decrease in mean blade loading results in reduced low-frequency noise, while the increase in high-frequency noise is ascribed to the increased intensity of BWI/BVI leading to higher unsteady loading fluctuations on blades.

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Wind turbine partial-load controllers have evolved from simple static nonlinear function implementations to more advanced dynamic controller structures. Such dynamic control schemes have the potential to improve power production performance in realistic environmental conditions and allow for a more granular trade-off between loads and energy capture. The control structure generally consists of a wind speed estimator (WSE) combined with a controller aiming to track the commanded tip-speed ratio (TSR) reference. The performance and resulting closed-loop system stability are however highly dependent on the accuracy of the internal model in the WSE-TSR tracking scheme. Therefore, developing learning algorithms to calibrate the internal model is of particular interest. Previous works have proposed such algorithms; however, they all rely on the availability of (rotor-effective) wind speed measurements. For the first time, this paper proposes an excitation-based learning algorithm that exploits the closed-loop dynamic structure of the WSE-TSR tracking scheme. This algorithm calibrates the internal model without the need for wind speed measurements. Analysis and simulations show that the proposed algorithm corrects for model uncertainties in the form of magnitude scaling errors under ideal constant and realistic turbulent wind conditions.@en

On-shore horizontal-axis wind turbines (HAWTs) provide a cost-effective solution for low carbon electricity generation. However, public acceptance is still a problem. A possible alternative to a HAWT is a vertical-axis wind turbine (VAWT), which is more visually appealing and less noisy. Furthermore, the inherent omni-directionality of VAWTs makes them suitable for installation in urban environments where the turbulence levels are high, and the wind direction variations are significant. However, the variation with the azimuth angle of the blade-effective wind speed and the angle of attack makes VAWT performance difficult to predict. This study proposes a wind speed estimator for a VAWT to address this challenge and to exploit knowledge of the blade-effective wind speed for load reduction control strategies. An Unscented Kalman Filter is used to solve the blade-effective wind speed estimation problem and is applied to a realistic 1.5 m two-bladed H-Darrieus VAWT model, for which the aerodynamic characteristics are determined using an actuator cylinder model. The system performance is evaluated using different wind speed variation scenarios. Overall, good agreement between the reference and estimated blade-effective wind speed is found both in terms of trend and absolute values.

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The combined wind speed estimator and tip-speed ratio (WSE–TSR) tracking wind turbine control scheme has seen recent and increased traction from the wind industry. The modern control scheme provides a flexible trade-off between power and load objectives. On the other hand, the Kω2 controller is often used based on its simplicity and steady-state optimality and is taken as a baseline here. This paper investigates the potential benefits of the WSE–TSR tracking controller compared to the baseline by analysis through a frequency-domain framework and by optimal calibration through a systematic procedure. A multi-objective optimisation problem is formulated for calibration with the conflicting objectives of power maximisation and torque fluctuation minimisation. The optimisation problem is solved by approximating the Pareto front based on the set of optimal solutions found by an explorative search. The Pareto fronts were obtained by mid-fidelity simulations with the National Renewable Energy Laboratory (NREL) 5 MW turbine under turbulent wind conditions for calibration of the baseline and for increasing fidelities of the WSE–TSR tracking controller. Optimisation results show that the WSE–TSR tracking controller does not provide further benefits in energy capture compared to the baseline Kω2 controller. There is, however, a trade-off in torque control variance and power capture with control bandwidth. By lowering the bandwidth at the expense of generated power of 2 %, the torque actuation effort reduces by 80 % with respect to the optimal calibration corresponding to the highest control bandwidth.@en

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

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In recent years, industrial controllers for modern wind turbines have been designed as a combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking control scheme. In contrast to the conventional and widely used Kω 2 torque control strategy, the WSE-TSR scheme provides flexibility in terms of controller responsiveness and potentially improves power extraction performance. However, both control schemes heavily rely on prior information about the aerodynamic properties of the turbine rotor. Using a control-oriented linear analysis framework, this paper shows that the WSE-TSR scheme is inherently ill-conditioned. The ill-conditioning is defined as the inability of the scheme to uniquely determine the wind speed from the product with other model parameters in the power balance equation. Uncertainty of the power coefficient contribution in the latter mentioned product inevitably leads to a biased effective wind speed estimate. As a consequence, in the presence of uncertainty, the real-world wind turbine deviates from the intended optimal operating point, while the controller believes that the turbine operates at the desired set-point. Simulation results confirm that inaccurate model parameters lead to biased estimates of the actual turbine operating point, causing sub-optimal power extraction efficiency.

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Vertical-axis wind turbines have the potential to be installed nearby urban areas, where noise regulations are a constraint. Accurate modelling of the far-field noise with low-order fidelity methods is essential to account for noise early in the design phase. The challenge for the vertical-axis wind turbine is the unsteady azimuthal variation of the flow over the blades, which makes the prediction of the far-field noise complex with low-fidelity methods. In this paper, the state-of-the-art of low-fidelity methods are assessed against scale-resolving high-fidelity numerical simulations of a realistic vertical-axis wind turbine carried out with the lattice-Boltzmann very large eddy simulations method. High-fidelity numerical data are validated against experimental aerodynamics data of the same vertical-axis wind turbine. The low-fidelity method is based on the actuator cylinder model coupled with semi-empirical models for airfoil-self noise and turbulence-interaction noise. Results show a good agreement between the high-fidelity simulations and the low-fidelity model at low frequencies (i.e. between 2 × 10 ¹ Hz and 1 × 10 ² Hz), where turbulence-interaction noise is the dominant noise source. At higher frequencies, the airfoil-self noise dominates and existing methods, based on steady airfoils, do not correctly predict noise. This paper shows that the presented low-fidelity model predicts the aerodynamics and the aeroacoustics of the turbine with an acceptable accuracy for a design stage. However, improvements are needed to better predict the far-field noise for blades in an unsteady field.

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Simulation methods ensuring a level of fidelity higher than that of the ubiquitous Blade Element Momentum theory are increasingly applied to VAWTs, ranging from Lifting-Line methods, to Actuator Line or Computational Fluid Dynamics (CFD). The inherent complexity of these machines, characterised by a continuous variation of the angle of attack during the cycloidal motion of the airfoils and the onset of many related unsteady phenomena, makes nonetheless a correct estimation of the actual aerodynamics extremely difficult. In particular, a better understanding of the actual angle of attack during the motion of a VAWT is pivotal to select the correct airfoil and functioning design conditions. Moving from this background, a high-fidelity unsteady CFD model of a 2-blade H-Darrieus rotor was developed and validated against unique experimental data collected using Particle Image Velocimetry (PIV). In order to reconstruct the AoA variation during one rotor revolution, three different methods-detailed in the study-were then applied to the computed CFD flow fields. The resulting AoA trends were combined with available blade forces data to assess the corresponding lift and drag coefficients over one rotor revolution and correlate them with the most evident flow macro-structures and with the onset of dynamic stall.

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The paper presents an experimental study of applying variable loads on a vertical-axis wind turbine (VAWT). The experiment is conducted in an open-jet wind tunnel on a two-bladed Darrieus VAWT equipped with active individual blade pitch control. Variable loads are achieved by dynamically changing the pitch angle of the individual blades and by keeping the wind speed of the tunnel constant. The blade loads are measured using strain gages and the flow velocity is measured upwind and downwind of the rotor using a hotwire. Dynamic inflow phenomena are clearly visible both in the turbine loads and in the velocity field. A time delay based upon the flow convection in the wake is identified. It results that the induction of the turbine can be controlled by changing the pitch of the blades. The experimental database allows to validate a new dynamic inflow model for VAWT and will be made publicly available for research purposes.

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