Wind energy has witnessed a staggering development race, resulting in higher torque density demands for the drivetrain in general and the gearbox in particular. Accurate knowledge of the input torque and suitable models are essential to ensure reliability, but neither of them is currently available in commercial wind turbines. The present study explores how a subspace identification algorithm can be applied to fiber-optic strain sensors on a four-stage gearbox to obtain operational deflection shapes. An innovative measurement setup with 129 fiber-optic strain sensors has been installed on the outer surface of the ring gears to research the deformations caused by planet gear passage events. Operational deflection shapes have been identified by applying the multivariable output-error state space (MOESP) subspace identification method to strain signals measured on a serial production end-of-line test bench. These operational deflection shapes, driven by periodic excitations, account for almost all the energy in the measured strain signals. Their contribution is controlled by the torque applied to the gearbox. From this contribution, a torque estimate for dynamic operating conditions has been derived. Accurate knowledge of the input torque throughout the entire service life allows for future improvements in assessing the remaining useful life of wind turbine gearboxes.@en

Periodic wakes are created on upstream wind turbines by pitching strategies, such as the Helix approach, to enhance wake mixing and thereby increase power production for wind turbines directly in their wake. Consequently, a cyclic load is not only generated on the actuating turbine’s blades but also on the waked wind turbine. While the upstream load is the result of the pitching required for wake mixing, the downstream load originates from interaction with the periodic wake and only causes fatigue damage. This study proposes two novel individual pitch control schemes in which such a periodic load on the downstream turbine can be treated: by attenuation or amplification. The former method improves the fatigue life of the downstream turbine, whereas the latter enhances wake mixing further downstream by exploiting the already-present periodic content in the wake; both were validated on a three-turbine wind farm in high-fidelity large-eddy simulations. Fatigue damage reductions of around 10% were found in the load mitigation case, while an additional power enhancement of 6% was generated on the third turbine when implementing the amplification strategy. Both objectives can easily be toggled depending on a wind farm operator’s demands and the desired loads/energy capture tradeoff.

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Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential in modern, highly loaded gearbox designs, as maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Unfortunately, the traditional method of measuring dynamic mechanical torque using strain gauges placed on the outer surface of a rotating shaft and transmitting the resulting signal is unsuitable for serial deployment due to technical and economic constraints. An alternative method based on fiber-optic strain sensors placed on the stationary outer surface of the gearbox ring gear has been proposed. Like shaft torsion, the radial deformation of the ring gear is proportionate to the rotor torque. Placing the sensors on a stationary component is a cost-effective alternative for serial implementation because the need for complex and expensive data transfer via wireless transmission or a slip ring is eliminated. In this paper, we present the results of an extensive field experiment conducted to evaluate the torque measurement accuracy of this novel sensing solution installed on the gearbox of a Gamesa G97 2-MW wind turbine at the National Renewable Energy Laboratory’s Flatirons Campus. Torque measurements derived from fiber-optic strain sensors placed on the ring gear of the planetary stage are compared to conventional torque measurements from strain gauges placed on the main shaft. Two different torque estimation data processing methods were evaluated, with the method based on operational deflection shapes providing the most accurate results with an average normalized root mean square error below 0.7% for a load revolution distribution analysis. The effect of operating conditions on the torque estimate was also investigated, and the third planet-passing operational deflection shape was found to be the least sensitive to nontorque load-related effects. The fiber-optic strain sensors’ successful operation during the complete test campaign has demonstrated a robust and accurate solution for fleet-wide enhanced gearbox remaining useful life estimation.

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Wind farm flow control aims at mitigating wake effects in order to maximize power production in wind farms. This work mostly focuses on the Helix strategy, which relies on individual pitch control to radially offset the application point of the thrust force from the rotor center and to dynamically change its azimuthal position. Previous studies have shown that power gains for a downstream turbine are higher for a counter-clockwise (CCW) rotation of the application point than for a clockwise (CW) one. In the CCW case, the wake develops as a right-handed helix, while in the CW case, a left-handed helix is observed. Using Large Eddy Simulations, this paper shows that the helix handedness in the wake matters due to its interaction with the wake swirl. Results of the CCW and CW helix first highlight the formation of streamwise vorticity in the near wake, which is transformed into strong coherent vortices in the far wake. Those vortex structures, to some extent similar to the counter-rotating vortex pair in the wake of yawed wind turbines, are responsible for (i) displacing the wake thanks to their induced velocities and (ii) deforming the shape of the wake.

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A variety of wind farm control strategies exist in order to reduce unfavorable wake effects in large wind farms. While strategies like wake steering already reached a high maturity level, it is interesting to compare them to more recently proposed strategies. Such a comparison can form the basis for the development of a symbiotic wind farm control toolbox, from which a control strategy is chosen and activated depending on the operating conditions. The present study compares wake steering with helix control across a wide range of turbine spacings and wind directions using large-eddy simulation (LES). The size of the search space is made computationally tractable for LES by adopting a setup based on one physical upstream turbine and a distribution of virtual downstream turbines which do not exert any thrust force. It is found that helix control is beneficial for full wake overlap and turbine spacing of less than six rotor diameters whereas wake steering proves to be optimal further downstream and for partial wake overlap. Furthermore, the results show that the helix control setpoint in the proximity of full wake overlap scenarios is less susceptible to wind direction variations. This finding indicates that the combination of wake steering and helix control has potential for the design of a wind farm controller which is more robust in full wake overlap scenarios and can reduce the need for large yaw offset adjustments.

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Dynamic wind farm flow control is the art and science to maximize the energy yield of large wind farms. In this paper we will address the problem of large time delays between control actions of the different turbines in the farm and the delayed impact on the downstream turbines. We propose and show how a time-shifted cost function approach can render the receding horizon optimization problem more efficient and can mitigate the unavoidable turn-pike effect. We further show how the resulting setup can be used to break the optimization problem apart into several smaller optimization tasks to reduce the computational load. We demonstrate that the proposed changes do allow an economic model predictive control strategy to engage into collaborative wind farm control for long term gains, while a more traditional cost function approach leads to greedy turbine behavior. As a result, we take a crucial step towards a mature implementation of dynamic model based wind farm flow control.

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Induction control methods offer a potential solution to minimizing wake effects that occur in large wind farms. This paper presents an experimental study on multiple induction control methods for wind farm power maximization. Wind tunnel experiments were conducted on two aligned scaled wind turbines. The upstream turbine was operated with static induction control, periodic dynamic induction control with collective pitch actuation, and dynamic individual pitch control (the helix approach). All wind farm control implementations were compared to a baseline case, which optimized the individual power extraction of both turbines. Tomographic particle image velocimetry was used to measure the wake of the upstream turbine. Based on turbine measurements, grid searches were employed to discover the optimal frequency and amplitude of the pitch actuation in the dynamic induction control cases. While static induction control showed increased wake velocities in the near wake, it did not provide an overall increase in power production of the two-turbine array. Dynamic induction control methods, especially the helix approach in the counterclockwise direction, were seen to significantly increase the total power output compared to the baseline control case. However, this improvement came with a larger amount of pitch actuation and increased fatigue loading of structural components in the fore-aft direction.

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Floating wind energy has attracted substantial interest since it enables the deployment of renewable wind energy in deeper waters. Compared to the bottom-fixed turbines, floating wind turbines are subjected to more disturbances, predominantly from waves acting on the platform. Wave disturbances cause undesired oscillations in rotor speed and increase structural loading. This paper focuses on investigating the potential of using wave preview measurement in the control system labeled as wave feedforward to mitigate the effects of the wave disturbances. Two wave feedforward controllers were designed: one to reduce generator power oscillations and the other one to minimize the platform pitch motion. In this study, a software-in-the-loop wave tank experiment is presented for the purpose of investigating the potential of these wave feedforward controllers. In the experiment, a 1:40 scaled model of the DTU 10 MW reference wind turbine is used on top of a spar platform, with the baseline feedback control functionalities. Different environmental conditions, including wind speed, significant wave height, turbulence intensity, and wave spreading, were applied during the experiments to test the feedforward control performance and their effect on the turbine dynamics in general. It was found that the feedforward controller for the generator power reduces the power fluctuations properly with a fair control effort, while the one for platform pitch motion requires almost double the actuation duty for the same percentage reduction. Furthermore, the feedforward controller was able to counteract the wave disturbance at different wave heights and directions. However, it could not do much with increasing turbulence intensity as wind turbulence was found to have more dominance on the global dynamic response than waves.@en

As wind turbine power capacities continue to rise, taller and more flexible tower designs are needed for support. These designs often have the tower's natural frequency in the turbine's operating regime, increasing the risk of resonance excitation and fatigue damage. Advanced load-reducing control methods are needed to enable flexible tower designs that consider the complex dynamics of flexible turbine towers during partial-load operation. This article proposes a novel modulation-demodulation control (MDC) strategy for side-side tower load reduction driven by the varying speed of the turbine. The MDC method demodulates the periodic content at the once-per-revolution (1P) frequency in the tower motion measurements into two orthogonal channels. The proposed scheme extends the conventional tower controller by augmentation of the MDC contribution to the generator torque signal. A linear analysis framework into the multivariable system in the demodulated domain reveals varying degrees of coupling at different rotational speeds and a gain sign flip. As a solution, a decoupling strategy has been developed, which simplifies the controller design process and allows for a straightforward (but highly effective) diagonal linear time-invariant (LTI) controller design. The high-fidelity OpenFAST wind turbine software evaluates the proposed controller scheme, demonstrating effective reduction of the 1P periodic loading and the tower's natural frequency excitation in the side-side tower motion.

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A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.@en

Wake mixing techniques like the Helix have shown to be effective at reducing the wake interaction between turbines, which improves wind farm power production. When these techniques are applied to a floating turbine it will excite movement. The type and magnitude of movement are dependent on floater dynamics. This work investigates four different floating turbines. Of these four turbines, two are optimised variants of the TripleSpar and Softwind platforms with enhanced yaw motion. The other two are the unaltered versions of these platforms. When the Helix is applied to all four floating turbines, the increased yaw motion of the optimised TripleSpar results in a reduction in windspeed whereas the optimised Softwind sees an increase in windspeed with increased yaw motion. From simulations using prescribed yaw motion at different phase offsets between blade pitch and yaw motion, we can conclude that this is the driving factor for this difference.

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Actuator line modeling of wind turbines requires the definition of a free-stream velocity in a computational mesh and a regularization kernel to project the computed body forces onto the domain. Both choices strongly influence the results. In this work, a novel velocity sampling method—the so-called effective velocity model (EVM)—is implemented in the CFD software SOWFA, validated, and compared to pre-existing approaches. Results show superior method robustness with respect to the regularization kernel width ((Formula presented.)) choice while preserving acceptable accuracy. In particular, the power predicted by the EVM is nearly independent of the (Formula presented.) value.

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Individual pitch control (IPC) is a method to mitigate periodic blade loads in wind turbines, and it is typically implemented using the multi-blade coordinate (MBC) transform, which converts the blade load measurements from a rotating frame into the non-rotating tilt axis and yaw axis. Previous studies have shown that by including an additional tuning parameter in the MBC, the azimuth offset reduces the coupling between non-rotating axes, allowing for higher performance levels for diagonal controller structures. In these studies, the decentralized control of IPC was composed of two identical integral controllers. This work analyzes and compares the improvement that the azimuth offset can provide in different adaptive gain scheduling IPCs where the diagonal controllers can have integral or proportional action with different gains. They are applied to a 15 MW wind turbine simulated with OpenFAST v3.5 software. The controller parameter tuning is addressed as an optimization that reduces blade fatigue load based on the damage equivalent load (DEL) and is resolved through genetic algorithms. Simulations show that only using different controller gains in IPC does not provide significant improvements; however, including azimuth offset in the optimal IPC schemes with integral controllers allows for the greatest DEL reduction with a lower actuator effort.

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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

As a result of more stable wind conditions and the depletion of near-shore locations, wind farms are moving farther offshore into deeper waters, challenging the current limits of offshore heavy-lift operations. This paper presents and verifies a novel frequency-domain framework to perform extensive site-specific analysis, of floating installations of wind-turbine towers, subjected to wind and wave loads. The versatility and potential of this framework is demonstrated with a case-study of a wind farm near the coast of Portugal. The results lead to the following conclusions: (1) Only considering beam-seas the yearly workability is 39 %; (2) Workability is mostly limited by wave loads; (3) Tower motions tend to decrease with tower size and are not significantly affected by hook-tower distance (sling length); and finally, (4) In this case-study the most contributing frequencies for tower motions are 0.3 and 0.4 rad/s, corresponding mainly to the first pendulation mode.

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Achieving the European Union's target of 510 GW of installed wind energy capacity by 2030 requires a significant expansion of the currently installed capacity of 255 GW [1], [2]. As a consequence of these ambitions, the power density of newly developed wind farms is rising by increasing the number of turbines within a wind farm and the size of individual turbines [3]. The larger wind farms are predominantly located offshore where wind conditions are more consistent and, on average, wind speeds are higher compared to onshore locations [4]. Furthermore, more than 80% of Europe's wind energy resources can be found in waters too deep for bottom-fixed turbines [5], [6], resulting in a sharp increase in the interest in floating wind turbines over the past decade (see 'Summary').

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Europe has set an ambitious target to increase the offshore wind power capacity to approximately 30 GW by 2026. With nearshore locations already allocated, future wind farms must be installed in deeper waters, pushing the operational limits of currently used jack-up vessels. Utilizing existing floating heavy-lift vessels presents a viable alternative. This paper disseminates data gathered during the full-scale testing campaign of a floating installation of an offshore wind turbine tower. For this purpose, novel time-synchronized motion-tracking units were developed. Analysis of the obtained data reveals that approximately 96% of the motion response of the tower is due to wave action and 3% to vortex-induced vibrations caused by the presence of a passive tugger line, which shifted one of the system's natural frequencies towards the tower's vortex-shedding frequency. Next to wind and wave-induced motion, the data reveal that the hoisting itself induces tower vibrations, accounting for less than 1% of the tower motion response. The collected data offer a distinctive perspective on this type of installation, which is unlikely to be replicated at model scale due to the scaling limitations associated with the interdependence of waves and wind. The data can be used to validate motion control strategies to enhance the efficiency, safety, and workability of floating offshore wind turbine installations.

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Wind turbines in farms face challenges such as reduced power output and increased loading when their rows align with the wind direction - a phenomenon known as the wake effect. To address this issue, dynamic induction control has been proposed, which involves dynamically adjusting the induction of upstream turbines to enhance the mixing of the wake with the free stream. As a continuation of this method, downstream turbines could potentially leverage the periodic structure in the upstream turbines' wake to improve power production further downstream by synchronizing their dynamic induction control actions. This study investigates the potential of such an approach using a three-turbine scaled setup in a wind tunnel. The findings reveal that synchronization not only improves wake mixing downstream but also results in a substantial power gain on the synchronizing turbine, suggesting potential for a synchronization controller.

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Wake steering represents a viable solution to mitigate the wake effect within a wind farm. New research that considers the effect of the control strategy within the layout optimization is emerging, adopting a co-design approach. This study estimates the potential of this technique within the layout optimization for a wide range of realistic conditions. To capture the benefits of such methods, a genetic algorithm tailored to the layout optimization problem has been developed in this work; hence this is referred to as a layout optimization genetic algorithm (LO-GA). The crossover phase is designed to recognize and exploit the differences and the similarities between parent layouts, whereas the randomness of the mutation is limited to improve the exploration of the design space. New relations have been introduced to calculate the geometric yaw angles based on the reciprocal positions between the turbines. For a base case of 16 turbines located at the Hollandse Kust Noord site, a gain in the annual energy production (AEP) between 0.3 % and 0.4 % is obtained when the co-design approach is adopted. This increases up to 0.6 % for larger farms, saturating after 25 turbines. However, the benefit of the co-design decreases in the case of low power densities or if the wind resource is highly unidirectional. On the other hand, in the case that wake steering is not applied during the operation of the farm, a decrease in the AEP up to 0.6 % is registered for a layout optimized with the co-design method. To prevent the risk related to future decisions on the control strategy, a multi-objective co-design approach is proposed. This is based on the simultaneous optimization of the layout for a scenario in which wake steering is applied versus a case where wake steering is not adopted during the operation of the farm. We have concluded that the solutions obtained with this method ensure an AEP gain higher than 0.3 % for a 16-turbine farm while limiting the loss to below 0.1 % in the case that wake steering is not applied. However, these AEP gains are affected by the size of the wind direction bins adopted in the simulations, enhancing the necessity of taking into account the wind direction errors and the yaw actuation constraints for a realistic evaluation of the co-design approach.@en