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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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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To justify the use of two single-input single-output (SISO) control loops instead of more complex multi-input multi-output (MIMO) control, the axes in a wind turbine's pitch control system should be fully decoupled using the multi-blade coordinate transform. To achieve that, usually, an azimuth offset is required, correcting for phase lags originating from, e.g., actuator delays and blade flexibility. In wind turbine simulations, this parameter is commonly obtained by analysis of the linearized turbine models. This work, however, demonstrates that analyzing linearized turbine models is not sufficient for correcting the full phase lag when coupling wind turbine simulation tools to large-eddy simulators (LES), since additional phase lags may arise. Instead, this work proposes deriving the azimuth offset using data-driven modelling directly in coupled LES, where data is generated by exciting the structure with pseudo-random binary noise. Using this approach it was found that the optimal azimuth offset is three degrees higher than when using the linearized model, which demonstrates that deriving the optimal azimuth offset from linearized models is not suitable for coupled simulations.

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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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Long Short-Term Memory Recurrent Neural Networks (LSTM) are used to build surrogate models to forecast time-series blade loads for both fixed and floating offshore wind turbines. In this paper, we train surrogate models on datasets generated with OpenFAST on the IEA-15MW-RWT under a range of metocean conditions. The aim of the surrogate models is to generate load forecasts inexpensively and accurately such that they can be used in a model predictive controller. Two cases are investigated with different model inputs: one with only measurements available to typical PI controllers and another one with additional wave elevation and deflection measurements (alongside the endogenous variable). The model performances are evaluated and compared. It was found that for the fixed turbine, the models predicted all three blade loads to a high degree of accuracy. The floating turbine surrogate models performed relatively worse, but edgewise and pitching moments are still reasonably accurate. The surrogate model forecasts the flapwise moment to a satisfactory accuracy only in 58% out of 400 test cases. The addition of wave elevation and blade deflection features did not significantly improve the prediction performance of the surrogate, demonstrating that just the information used by current PI controllers may be sufficient for forecasting blade loads.

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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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In the context of wind turbine pitch control for load alleviation or active wake mixing, it is relevant to provide the time- and space-varying wind conditions as an input to the controller. Apart from classical wind measurement techniques, blade-load-based estimators can also be used to sense the incoming wind. These consider blades to be sensors of the flow and rely on having access to the operating parameters and measuring the blade loads. In this paper, we wish to verify how robust such estimators are to the control strategy active on the turbine, as it impacts both operating parameters and loads. We use an extended Kalman filter (EKF) to estimate the incoming wind conditions based on the blade bending moments. The internal model in the EKF relies on the blade element momentum (BEM) theory in which we propose accounting for delays between pitch action and blade loads by including dynamic effects. Using large-eddy simulations (LESs) to test the estimator, we show that accounting for the dynamic effects in the BEM formulation is needed to maintain the estimator accuracy when dynamic wake mixing control is active.

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The dynamic induction control wake mixing strategy has the potential to increase the energy yield of floating wind farms. These floating turbines will be subjected to surface waves, caused by the wind, and swell. When dynamic induction control is applied in open-loop, the effect of second-order wave forces and dynamic induction control on the thrust force can be out-of-phase and have destructive interference. In this work, we propose a method to synchronize the dynamic induction control input to the effect of the second-order wave forces. This is achieved by formulating the synchronization problem within an H_∞ optimization framework and designing a controller that minimizes the difference between the effect of wave-induced thrust variation and thrust variation. Time domain simulations show that synchronization at a desired frequency can be achieved and that the overall performance of the dynamic induction control method can be enhanced.

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A promising method to reduce wake effects in offshore wind farms is the Helix approach, which increases the mixing of the wake with the surrounding flow by exciting the individual blade pitch. This increases the wind speed in the wake, resulting in a higher power output at a downstream turbine. Wind tunnel testing is crucial to gather further understanding of the governing mechanisms behind the Helix and its efficiency in larger wind farm arrays. However, model turbines are expensive and complex. Porous Discs (PD) have proven to supply a less expensive and less complex alternative for wake-focused wind tunnel studies. In this study we present a novel PD model to mimic the Helix. The fundamental idea is to mimic the non-uniform, unsteady energy extraction over the rotor plane as observed at a Helix-controlled turbine. For this purpose, we derive a non-uniform porosity distribution over the PD, based on Large Eddy Simulations of a three-bladed turbine controlled with the Helix approach, and the actuator disc theory. The resulting non-uniform PD rotates at the excitation frequency described by the Strouhal number to mimic the Helix. We verified the novel experimental setup with smoke visualisation techniques and thrust measurements at a second PD in the wake and observed the typical characteristics of the Helix wake of a model turbine: First, the wake was deformed into a helical shape, and second, the wake velocity increased depending on the excitation Strouhal number.

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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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In this work, we investigate a method to derive characteristic dynamic flow field behavior from field measurements. We further explore how these changes impact the performance of a wind farm flow control strategy. For a long time, hourly to 10-min averaged data has been the predominant form to store meteorological quantities such as wind speeds and wind directions. With the decreasing cost of digital storage and improvements in measurement technology, the assimilation of higher frequent data has become more feasible. We use one of these open-source datasets provided by the KNMI to explore what characteristic flow behavior is described in the high-frequency recordings of a Wind-LiDAR located in the North-Sea. To this end we employ a K-Means algorithm to cluster 10-min time series of wind direction changes sampled at 20 s. Our study finds that the majority of wind direction changes within this time window can be described by five main clusters with clock- and counterclockwise changes of the wind direction in the range of ±4 deg. Subsequently we investigate the implications for quasi-steady wind farm flow control. We employ look-up table yaw-steering control next to baseline control in selected cases in a turbulent Large Eddy Simulation to verify the predictions made by a dynamic parametric engineering wake model. We find good agreement between both simulation environments and use the engineering model to investigate all wind directions in 2 deg resolution. The results show that the identified wind direction changes can have a significant negative impact on the power generated by a 10 turbine wind farm. The study also shows that the fixed yaw-steering set-points are still favorable over baseline operation for wind direction changes in the range of ±1.6 deg, but can act detrimental for larger changes.@en

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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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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In this paper we consider data-driven control of vibrations in a flexible structure equipped with piezoelectric transducers. The control algorithm uses elements from the fairly recent data-enabled predictive control framework. In particular, we will develop a real-time implementation of the subspace predictive control algorithm. This algorithm first solves a linear least-squares problem to recursively estimate the observer Markov parameters of the system. With those parameters a predictor is constructed which is used to solve a predictive control problem subject to constraints. The feasibility of the approach is highlighted by applying it to an experimental setup using an efficient implementation. First, this demonstrates that computations can be performed in real-time for a realistic situation. Second, we show how the scheme rapidly adapts when a sudden significant change in structural dynamics is introduced by changing one of the structural parameters.

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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

Wind energy has emerged as a prominent alternative energy source, harvesting energy through turbines to contribute sustainably to the electricity grid. Effective control of these turbines is crucial for regulating power generation, with wind farm control strategies geared toward maximizing on-demand energy generation. In this work, we propose a wind turbine regulator based on blade-pitch actuation and assess the impact of adopted turbine derating strategies on aerodynamic loading and downstream power availability in an experimental setting. By considering a derating strategy based on generator torque control law, we explore two wind farm control approaches: thrust balance and power compensation. Our findings highlight the advantages of balancing aerodynamic loads across the farm, preventing turbine saturation, and enhancing power availability by 3%-5% compared to a uniform power dispatch. Furthermore, the inclusion of power compensation results in a heightened upper limit in wind farm power tracking, indicating a 22% boost in wind farm power availability. This research underscores the potential benefits of innovative turbine regulation strategies for optimizing wind farm performance and enhancing overall energy flexibility.

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