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.

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.

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.

Wind farm flow control (WFFC) is the discipline of manipulating the flow between wind turbines to achieve a farm-wide goal, like power maximization, power tracking or load mitigation. Specifically, steady-state control approaches have shown promising results in both theory and practice for power maximization. But how are they expected to perform in a dynamically changing environment? This paper presents an open-source wake modeling framework called OFF (abbreviated from the models OnWARDS, FLORIDyn and FLORIS). It allows the approximation of the performance of WFFC strategies in response to environmental changes at a low computational cost. It is rooted in previously published dynamic parametric engineering models and offers a flexible and adaptable platform to explore these models further. The presented study tests the modeling framework by investigating the performance of different wake steering controllers in a 10-turbine wind farm case study based on a subset of the Dutch wind farm Hollandse Kust Noord (HKN). The case study uses a 24 h wind direction time series based on field data and verifies subsets of the time series in a large-eddy simulation (LES). The results highlight how dependent yaw travel is on the controller settings and suggest where users can strike a balance between power gains and actuator usage. They also show the structural differences and similarities between steady-state and dynamic engineering models. The comparison to LES shows what timescales the surrogate models cover and how accurately. While steady-state models capture turbine power signal dynamics up to  Hz, the dynamic wake description can predict dynamics up to  Hz with a better correlation and normalized root-mean-square error. Further results show that the dynamic wake description is mainly advantageous over steady-state wake models for shorter periods (< 20 min). The paper also opens up discussion about the effectiveness of wind farm flow control in a time-marching manner as opposed to a steady-state viewpoint.

With the increasing share of renewable energy, concerns regarding ensuring power system stability are ever more relevant and have been accompanied by discussions to address this yet unsolved issue. Nonetheless, enhancing sparsity and increasing generation capacity by overplanting wind turbines not only mitigates the stability problem but also accelerates the transition from fossil fuel to renewable energy sources. With the high penetration of wind energy, there will be a paradigm shift from maximizing energy extraction to generating energy on demand. In this panorama, a cooperative wind farm control may strengthen the stability of the wind power plant through compensation strategies. Still, large-scale farms raise relevant control issues regarding computation effort and information sharing, such as topology constraints and communication overhead. Here, we contribute by presenting a multi-rate distributed control strategy based on average consensus. This strategy involves estimating the power-tracking errors at a fast sampling rate and executing local control actions that collaboratively mitigate these errors over an extended sampling period. This approach achieves performance comparable to that of the resource-intensive centralized approach. The reliability is therefore enhanced by improving the power regulation while reaching modularity and sparsity inside the farm.

This book presents data-driven algorithms used in the context of wind farm modelling and exploits their relation with concepts from non-linear dynamical system theory. The algortihms include Input Output Dynamic Mode Decomposition and their combination with the Koopman Operator theory. The latter improves on modelling and analysis of the aerodynamic interaction between wind turbines in wind farms and assists in uncovering insights into the existing dynamics and improves models accuracy. The authors introduce the topic of wind farm flow control, illustrating current strategies devised to overcome power losses in wind plants due to the aerodynamic interaction between turbines. Although controlling wind farms as a whole is becoming increasingly important, the high dimensions and governing non-linear dynamics inherent of wind farm systems make the design of numerical optimal controllers computationally expensive. This book describes a possible pathway to circumvent this challenge through reduced order models that can embed the existing non-linearities. The authors make use of high fidelity open-source simulation datasets and developed algorithms to fully show the potential of this approach using visual results. The reader is motivated to use the datasets and algorithms and exploit the potential of the reduced order models.

Many systems are subject to periodic disturbances and exhibit repetitive behaviour. Model-based repetitive control employs knowledge of such periodicity to attenuate periodic disturbances and has seen a wide range of successful industrial implementations. The aim of this paper is to develop a data-driven repetitive control method. In the developed framework, linear periodically time-varying (LPTV) behaviour is lifted to linear time-invariant (LTI) behaviour. Periodic disturbance mitigation is enabled by developing an extension of Willems’ fundamental lemma for systems with exogenous disturbances. The resulting Data-enabled Predictive Repetitive Control (DeePRC) technique accounts for periodic system behaviour to perform attenuation of a periodic disturbance. Simulations demonstrate the ability of DeePRC to effectively mitigate periodic disturbances in the presence of noise.

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.

Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under the influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared with normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re-energizing the wake. Measurements indicate that the breakdown of tip vortices and the resulting destabilization of the wake are enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two-turbine setup by as much as 15%.

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.

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.

The mesh load factor, K 3, describes how loads are shared between planet gears and has become one of the key design challenges in modern wind turbine gearboxes. Planet load sharing directly impacts tooth root stresses, a critical driver of torque density and gearbox reliability. Experimental evaluation of K 3 is typically performed from sun gear tooth root strain gauge measurements, which are complex. Furthermore, such measurements can only provide an average value of load sharing. The present study describes an alternative method to evaluate the mesh load factor in wind turbine gearboxes based on fiber-optic strain sensors installed on the outer surface of the fixed ring gear. We present the results of an extensive measurement campaign to evaluate this novel sensing solution installed on the input planetary stage of a 2-MW wind turbine gearbox at the National Renewable Energy Laboratory's Flatirons Campus (Colorado, USA). The number of strain sensors on the ring gear was selected as an integer multiple of the number of planets, which has enabled an instantaneous evaluation of the mesh load factor. The effect of operating conditions on the planet load-sharing behavior of the gearbox has been investigated. The mesh load factor measured for operating conditions close to rated was below 1.05, well below IEC 61400-4 standard requirements.

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.

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.

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.

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').

In order to mitigate periodic blade loads in wind turbines, recent research has analyzed different Individual Pitch Control (IPC) approaches, which typically use the multi-blade coordinate (MBC) transformation. Some of these studies show that the introduction of an additional tuning parameter in the MBC, namely the azimuth offset, helps to decouple the nonrotating axes in the MBC transformation and enhances the IPC performance. However, these improvements have been studied without considering the increased control effort performed by the pitch signal, which is the main negative side effect of the IPC. This work addresses this trade-off between pitch signal effort and blade fatigue reduction for IPC applied to a wind turbine operating in the full load region. Here, two IPC schemes, with and without additional azimuth offset, are designed and applied to a 15 MW monopile offshore wind turbine simulated with OpenFAST software. The optimal tuning of the IPC parameters is performed by means of a multi-objective optimization solved by genetic algorithms. The optimization procedure minimizes two objective functions related to pitch signal effort and blade fatigue load. The resulting Pareto fronts show a range of optimal solutions for each IPC scheme. The selected optimal solution for IPC with azimuth offset compared to the optimal solution for IPC without offset achieves improvements of more than 10% in blade load reduction maintaining similar pitch signal effort.

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.

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.

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.