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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., Team Jan-Willem van Wingerden, Wind Energy

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%., Team Jan-Willem van Wingerden, Wind Energy

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., Team Jan-Willem van Wingerden, Ship Hydromechanics and Structures

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., Team Jan-Willem van Wingerden

Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set., Team Mulders, Team Riccardo Ferrari, Team Jan-Willem van Wingerden

In this work, we find a reduced-order model for the wake of a wind turbine controlled with dynamic induction control. We use a physics-informed dynamic mode decomposition algorithm to reduce the model complexity in a way such that the physics of the wake mixing can be investigated and that the model itself can be easily embedded into control-oriented frameworks. After discussing the advantage of forcing the linear system resulting from the algorithm to be conservative (as a consequence of the periodicity of the pitch excitation) and the choice of observables, we describe a procedure for calculating the energy associated with individual modes. The considered data-set is composed of large eddy simulation (LES) results for a single DTU 10 MW wind turbine in uniform flow. Simulations were performed first with baseline control (for reference) and then with the Pulse and the Helix approaches with constant excitation amplitude and different excitation frequencies. The frequencies and energies associated with the resulting modes are discussed., Team Jan-Willem van Wingerden, Wind Energy

In most current offshore wind farms, the turbines are controlled greedily, neglecting any coupling by wake effects with other turbines. By neglecting these effects of aerodynamic interactions, the power production performance is substantially reduced. Besides the well-known wake steering and dynamic induction control wake control strategies, a novel wind farm flow control strategy called the Helix approach has been recently proposed to mitigate the impacts of wake effects and optimize wind farm performance. The Helix approach adopts the individual pitch control (IPC) technique to dynamically deform the wake into the helical shape, which induces wake instability and thereby stimulates wake recovery. The first results employing a single-harmonic signal have demonstrated promising enhancement in wake recovery effects. However, more complex signals to potentially improve the effectiveness of the Helix approach have never been studied. This paper explores the potential of using higher-harmonic signals in the Helix approach to further enhance wake mixing. The aeroelastic simulator, OpenFAST, with its recently developed free vortex wake codes is adopted to simulate the dynamic wake evolution. A Fourier stability analysis is used to quantitatively identify the wake breakdown position. Results show that in the baseline case where no Helix signals are implemented, the wake breaks down at 3.25 rotor diameter (D) from the rotor. When using the designed multi-sine Helix signals, the wake breaks down at 1.75 D from the rotor, which is a significant improvement over the breakdown distance at 2.50 D using the conventional single-sine Helix. The earlier wake breakdown indicates faster wake recovery and is to be validated by future higher-fidelity simulation studies., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden, Team Mulders

The Helix approach is a control technology that reduces the wake effect in wind farms by accelerating wake mixing through individual pitch control, resulting in significant AEP gain. However, this study found that depending on its settings, the controller may increase pitch bearing damage and loads on some turbine components. Using a modified version of NREL’s Reference OpenSource Controller in OpenFAST, this study analysed the sensitivity of loads and pitch bearing damage to different Helix controller settings on the IEA-15MW reference offshore wind turbine. Results showed that loads increased with the excitation signal amplitude but were less affected by its frequency. Additionally, more pitch bearing damage was observed in the counterclockwise Helix direction, while slightly higher loads were observed in the clockwise direction when using the same excitation signal amplitude and frequency for both directions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden

Near-wake effects of wind turbine models using the free-vortex wake have been studied extensively, but there is a lack of validation for such predictions in the mid to far wake. This paper presents a novel validation study using three free-vortex wake models of increasing complexity: an actuator disc, an actuator disc with rotation, and a lifting-line model. We emphasise the application for dynamic wind farm flow control optimisation with a focus on wake redirection using yaw misalignment. For this purpose, wake models should provide sufficiently accurate power predictions at a low computational expense to enable real-time control optimisation. Three sets of wind tunnel data are used for validation: flow measurements under steady yaw misalignment, time-resolved flow measurements for a step change in yaw, and turbine output measurements with yaw control and simulated wind direction variation. Results indicate that the actuator-disc model provides the best balance between computational cost and accuracy in power predictions for the mid to far wake, which is not significantly improved upon by the addition of rotation. In the near wake, the added complexity of the lifting-line model may provide value as it models blade loading and individual tip vortices. Altogether, this study provides important validation for further studies into optimisation of wake steering under time-varying conditions and suggests that the actuator-disc model is a suitable candidate for use in a model-predictive wind farm flow control framework., Team Jan-Willem van Wingerden, Wind Energy

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., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Mulders, Wind Energy, Team Jan-Willem van Wingerden, Team Riccardo Ferrari

Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade-off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real-time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC-IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid-fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made., Team Jan-Willem van Wingerden, Team Riccardo Ferrari

The performance of wind farms can substantially increase when their individual turbines deviate from their own greedy control strategy and instead also take into account downstream turbines operating in the wake. The helix approach is a recently introduced dynamic wind farm control strategy that tackles this issue by leveraging individual pitch control to accelerate wake recovery. Its effective implementation requires detailed knowledge about the scaling between control input and the resulting power gain and turbine loading across the farm. In the present work this scaling is explored by means of large-eddy simulation of a two-turbine farm in the conventionally neutral atmospheric boundary layer. A parameter sweep for the amplitude of the helix is performed showing monotonous increase of the farm's power output with increasing pitch amplitude within the considered range of zero to six degrees. The scaling of the power gain suggests that a threshold amplitude should be exceeded for effective speed-up of the wake recovery, whereas the damage equivalent loads computed for the turbines indicate an upper limit for the amplitude despite increasing power gains., Team Jan-Willem van Wingerden, Energie and Industrie

In recent years, control techniques such as dynamic induction control (often referred to as “the pulse”) have shown great potential in increasing wake mixing, with the goal of minimising turbine-to-turbine interaction within a wind farm. Dynamic induction control disturbs the wake by varying the thrust of the turbine over time, which results in a time-varying induction zone. If applied to a floating wind turbine, this time-varying thrust force will, besides changing the wake, change the motion of the platform. In light of the expected movement, this work investigates if applying the pulse to a floating wind turbine yields similar results to that of the pulse applied to bottom-fixed turbines. This is done by considering first the magnitude of motions of the floating wind turbine due to the application of a time-varying thrust force and secondly the effect of these motions on the wake mixing. A frequency response experiment shows that the movement of the floating turbine is heavily frequency dependent, as is the thrust force. Time domain simulations, using a free-wake vortex method with uniform inflow, show that the expected gain in average wind speed at a distance of 5 rotor diameters downstream is more sensitive to the excitation frequency compared to a bottom-fixed turbine with the same pulse applied. This is due to the fact that, at certain frequencies, platform motion decreases the thrust force variation and thus reduces the onset of wake mixing., Team Jan-Willem van Wingerden, Wind Energy

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions., Team Jan-Willem van Wingerden, Wind Energy, Energie and Industrie

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches., Team Jan-Willem van Wingerden

Dynamic flow control strategies are raising interest for wake mitigation purposes. Among the different strategies, the so-called helix one relies on individual pitch control (IPC). The numerical simulation of the helix is thus readily performed by means of discrete-blade capturing methods. Yet, if this control strategy is considered at the scale of wind farms, the resolution required by such methods becomes prohibitive and actuator disk (AD) models should be envisioned. It is however not trivial to translate IPC strategies to an AD framework which by definition considers rotor-averaged effects. This work assesses the ability of an AD method to simulate the helix strategy by comparing it to a higher fidelity approach relying on a discrete-blade capturing model. Results show that the disk-type approach supplemented with a disk-adapted IPC scheme is able to capture both the forced motion of the wake at low turbulence and the faster wake recovery at moderate turbulence. From a quantitative perspective, the disk-type approach predicts bigger power gains, compared to those foreseen by the discrete-blade type approach, for a downstream turbine in the wake of a helix-operated one., Team Jan-Willem van Wingerden

Wind turbines may experience local weather perturbation, which is not taken into account by the commonly-used wind turbine simulation packages. Without this information, it is extremely challenging to evaluate the controller performance with regard to the effect of the variation of local atmospheric conditions. On the other side, it is too late and costly to wait until field test time. To fill this gap, in this paper, we develop a control-oriented turbine dynamic simulation framework to evaluate the controller performance considering the perturbation of local atmospheric conditions. This goal is achieved by integrating an internal wind turbine (IWT) model in the Weather Research and Forecasting (WRF) simulation tool. The proposed framework is implemented on a 5MW reference wind turbine, where the effects of the local atmospheric conditions are illustrated. The proposed WRF-IWT model are validated by comparing the results with those derived from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST)., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

As renewable energy sources such as wind farms become dominant, new challenges emerge for operating and controlling them. Traditionally, wind farm control aims to dispatch power set-points to individual turbines to maximize energy extraction and, thus, their usage as assets. Yet, grid balance and frequency support are fundamental in presence of high renewable penetration and volatility of energy prices and demand. This requires a paradigm change, moving from power maximization to revenue maximization. In this paper, three active power control strategies pushing this shift of paradigm are investigated, namely: wake-loss compensation, thrust balancing, and load-limiting control. The findings of large eddy simulations of a reference wind farm show that wake-loss compensation indeed improves the power generation on waked wind farms, but at the price of increased structural loads on certain turbines. The addition of a thrust balancing can equalize the stresses of individual turbines and their wear in the long term, while still attaining the required power output at the farm level. Furthermore, load-limiting controllers could potentially aid by allowing maintenance to be scheduled in a single time window, thus reducing operation and maintenance costs., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

The knowledge of the Effective wind speed (EWS) allows the designing of wind turbine controllers that regulate power production and reduce loads on turbine components. Traditional single-point measurements are known to suffer from high noise and poor correlation with the EWS. As an alternative to overcome these problems, EWS estimators can be designed. The main challenge is the high non-linearity of the wind speed influence on the drive-train dynamics. Therefore, an estimator based on the unscented Kalman filter (UKF) is proposed and compared against an extended Kalman filter (EKF) and the immersion and invariance (I&I) technique. Simulation results are provided and show the superior performances attained by the UKF. Furthermore, the usefulness of the estimated EWS is demonstrated by designing a sliding mode controller (SMC) that can track a desired power reference. In addition, the controller allows operating in sub-optimal conditions, where load reduction is attained at the expense of power maximization. The proposed estimator's and controller's performances are evaluated under wind farm wake conditions via high-fidelity simulations. The findings show that UKF can outperform the EKF and the controller can reduce loads, except under highly waked conditions., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

The reduction of fatigue loadings in wind turbines to increase their lifetime has become of special interest from a control viewpoint. Individual Pitch Control (IPC) is a well-known approach used to mainly mitigate periodic blade loads, and it is usually implemented with the assistance of the multi-blade coordinate (MBC) transformation, which transforms and decouples the measured blade load signals from a rotating frame into a non-rotating tilt-axis and yaw-axis. Nevertheless, these axes still show coupling between them in practical scenarios adversely affecting the system performance. Previous studies have demonstrated the benefits of including an extra tuning parameter in the MBC, the azimuth offset, in improving the performance achieved by the IPC. However, the tuning of this parameter and its real improvements that can be obtained compared to the IPC without this offset require more research. Here, two 1P+2P IPC, with and without additional azimuth offset, are designed and applied to the 5 MW reference turbine model developed by NREL using the FAST software as a simulation platform. The controller parameter tuning is formulated as an optimization problem that minimizes the blade fatigue load according to the Dirlik index and that is resolved through genetic algorithms. To fairly analyze the improvement entailed by the addition of the azimuth offset, both optimized IPC schemes, with and without azimuth offset, are compared qualitatively and quantitatively using a classical controller as the baseline case. From the simulation results, it can be stated that the optimal IPC scheme with azimuth offset compared with the IPC scheme without offset achieves improvements of around 11% in load reduction and pitch signal effort., Team Jan-Willem van Wingerden, Team Mulders

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., Wind Energy, Team Riccardo Ferrari, Team Jan-Willem van Wingerden, Team Mulders

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several approaches, including Data-enabled Predictive Control (DeePC) and Subspace Predictive Control (SPC) is obtained and the influence of noise on closed-loop predictors is analyzed. The analysis reveals potential closed-loop correlation problems, which are closely related to well-known results in closed-loop system identification, and consequent control issues. A case study reveals the hazards of noise in data-driven control., Team Jan-Willem van Wingerden, Team Mulders

The control of Floating Wind Turbines (FWTs) is challenging, as they possess much lower natural frequencies related to the structure's rigid body motion, which creates an undesirable coupling between tower motion and the blade pitch control. As a result, the tower motion is negatively damped triggering instability. This is because of the presence of Right Half Plane Zeros (RHPZs) imposing fundamental limitation on the control bandwidth. To address this problem, different solutions were proposed with varying control structures ranging from Single-Input, Single-Output (SISO) controllers to Multiple-input, Multiple-output (MIMO) ones. In this paper, a new control structure, of Single-Input, Multiple-Output (SIMO) is proposed that is able to lift the bandwidth limitation, while using simple elements that match the industry demands., Team Jan-Willem van Wingerden, Ship Hydromechanics and Structures

Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden, Team Mulders

Wind farm controllers such as the Helix approach have shown potential in increasing plant power production through wake mixing. The concept suggests that actuating the upstream turbines' blade pitching with a specific periodic signal can induce a helix-shaped wake, thereby alleviating wind velocity deficit on downstream turbines. Wake mixing initiation by downstream turbines may also be shown advantageous for power production; however, little to no attention has been given to such an approach. Similar wake mixing is expected to be achievable at lower control costs if the downstream turbine can benefit from the periodic component already present in the wake of the upstream turbine. Such a hypothesis is studied in this work by designing a minimal control scheme where the wake acting on the downstream turbine is simulated by a periodic input disturbance. A Kalman filter is proposed for incoming input disturbance phase estimation using SCADA data. The reconstructed phase information allows synchronization of the downstream control action with the periodic input disturbance by means of a phase synchronization wake mixing controller. The periodic component was estimated with a minimal root-mean-square error and the resulting control action was in phase with the input disturbance and demonstrated satisfactory performance even with a small phase perturbation. Future work will include applications in a high-fidelity wind turbine model and wind tunnel studies., Team Jan-Willem van Wingerden

Floating wind energy has attracted substantial interest since it enables the deployment of renewable wind energy in deeper waters. However, floating wind turbines are subjected to disturbances, predominantly from turbulence in the wind and waves hitting the platform. Wave disturbances cause undesired oscillations in speed and increase structural loading. This paper focuses on mitigating these disturbance effects with feedforward control using knowledge of the incoming wavefield. The control problem is formulated in an H∞ optimization framework designing two wave feedforward controllers: one to reduce rotor speed oscillations, and the other one to minimize the platform pitch motion. Mid-fidelity time-domain simulations demonstrate the improved performance of the proposed control algorithm regarding wave disturbance mitigation at the cost of higher actuator duty., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden, Ship Hydromechanics and Structures

Dynamic Mode Decomposition (DMD) is a fully data-driven method to extract a linear system from experimental or numerical data. It has proven its suitability for modeling wind turbine wakes, particularly those generated with Dynamic Induction Control (DIC), a method to reduce the wake deficit by enhancing its mixing with the surrounding flow. In this context, DMD may be used to build computationally efficient aerodynamic models suitable for model-based wind farm control algorithms. However, these standard DMD models are only valid for the flow conditions of the training data. This paper presents a novel approach to generalize a DMD model for DIC wakes from the training wind speed to various wind speeds by scaling the DMD modes. For this purpose, we first extract the DMD modes from numerical simulations of a DIC wake at a constant, homogeneous wind speed. Then, we adapt the obtained modes to a different wind speed with a scaling law for the frequency and magnitude derived from the definition of the Strouhal number. This allows for a versatile, efficient application of the DMD model in heterogeneous wind conditions at low computational costs. For validating the presented method, we model a helix wake at 6 ms-1 based on the DMD modes from Large Eddy Simulations (LES) at 9 ms-1. The DMD model coincides at a high level with validation simulations, resolving even mid- to small-scale structures., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden

To increase the contribution of offshore wind energy to the global energy mix in an economically sustainable manner, it is required to reduce the costs associated with the production and operation of offshore wind turbines (OWTs). One of the largest uncertainties and sources of conservatism in design and lifetime prediction for OWTs is the determination of the global damping level of the OWT. Estimation of OWT damping based on field measurement data has hence been subject to considerable research attention and is based on the use of (preferably operational) vibration data obtained from sensors mounted on the structure. As such, it is an output-only problem and can be addressed using state-of-the-art operational modal analysis (OMA) techniques, reviewed in this paper. The evolution of classical time- and frequency-domain OMA techniques has been reviewed; however, the literature shows that the OWT vibration data are often contaminated by rotor speed harmonics of significantly high energy located close to structural modes, which impede classical damping identification. Recent advances in OMA algorithms for known or unknown harmonic frequencies can be used to improve identification in such cases. Further, the transmissibility family of OMA algorithms is purported to be insensitive to harmonics. Based on this review, a classification of OMA algorithms is made according to a set of novel suitability criteria, such that the OMA technique appropriate to the specific OWT vibration measurement setup may be selected. Finally, based on this literature review, it has been identified that the most attractive future path for OWT damping estimation lies in the combination of uncertain non-stationary harmonic frequency measurements with statistical harmonic isolation to enhance classical OMA techniques, orthogonal removal of harmonics from measured vibration signals, and in the robustification of transmissibility-based techniques., Team Jan-Willem van Wingerden

Accurate knowledge of the input torque in wind turbine gearboxes is key to improving their reliability. Traditionally, rotor torque is measured using strain gauges bonded to the shaft. Transferring the resulting signal from the rotating shaft to a stationary data acquisition system while powering the sensing devices is complex and costly. The magnitude of the torques involved in wind turbine gearboxes and the high stiffness of the input shaft pose additional difficulties. This paper presents a new alternative method to measure the input torque in wind turbine gearboxes based on deformation measurements of the static first-stage ring gear. We have measured deformation using fiber-optic strain sensors based on fiber Bragg gratings because of their advantages compared to conventional electrical strain gauges. The present study was conducted on a Siemens Gamesa Renewable Energy gearbox with a rated power of 6 MW, in which a total of 54 fiber-optic strain sensors were installed on the outer surface of the first-stage ring gear. The gear mesh forces between the planets and the ring gear cause measurable deformations on the outer surface of the stationary ring gear. The measured strains exhibit a dynamic behavior. The strain values change depending on the relative position of the strain sensors to the planet gears, the instantaneous variations of the input torque, and the way load is shared between planets. A satisfactory correlation has been found between the strain signals measured on the static ring gear and torque. Two signal processing strategies are presented in this paper. The first procedure is based on the peak-to-peak strain values computed for the gear mesh events, and therefore torque can only be estimated when a gear mesh event is detected. The second signal processing procedure combines the strain signals from different sensors using a Coleman coordinate transformation and tracks the magnitude of the fifth harmonic component. With this second procedure, it is, Team Jan-Willem van Wingerden, Transport Engineering and Logistics

Individual pitch control (IPC) is a well-known approach to reduce blade loads on wind turbines. Although very effective, IPC usually requires high levels of actuator activities, which significantly increases the pitch actuator duty cycle (ADC). This will subsequently result in an increase of the wear on the bearings of the blades and a decrease of the wind turbine reliability. An alternative approach to this issue is to reduce the actuator activities by incorporating the output constraints in IPC. In this paper, a fully data-driven IPC approach, which is called constrained subspace predictive repetitive control (cSPRC), is introduced. The output constraints can be explicitly considered in the control problem formulation via a model predictive control (MPC) approach. The cSPRC approach will actively produce the IPC action for the necessary load reduction when the blade loads violate the output constraints. In this way, actuator activities can be significantly reduced. Two kinds of scenarios are simulated to illustrate the unique applications of the proposed method: wake–rotor overlap and turbulent sheared wind conditions. Simulation results show that the developed cSPRC is able to account for the output constraints into the control problem formulation. Since the IPC action from cSPRC is only triggered to prevent violating the output constraints, the actuator activities are significantly reduced. This will help to reduce the pitch ADC, thus leading to an economical viable load control strategy. In addition, this approach allows the wind farm operator to design conservative bounds to guarantee the safety of the wind turbine control system., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

The Immersion and Invariance (II) wind speed estimator is a powerful and widely-used technique to estimate the rotor effective wind speed on horizontal axis wind turbines. Anyway, its global convergence proof is rather cumbersome, which hinders the extension of the method and proof to time-delayed and/or uncertain systems. In this letter, we illustrate that the circle criterion can be used as an alternative method to prove the global convergence of the II estimator. This also opens up the inclusion of time-delays and uncertainties. First, we demonstrate that the II wind speed estimator is equivalent to a torque balance estimator with a proportional correction term. As the nonlinearity in the estimator is sector bounded, the well-known circle criterion is applied to the estimator to guarantee its global convergence for time-delayed systems. By looking at the theoretical framework from this new perspective, this letter further proposes the addition of an integrator to the correction term to improve the estimator performance. Case studies show that the proposed estimator with an additional integral correction term is effective at wind speed estimation. Furthermore, its global convergence can be guaranteed by the circle criterion for time-delayed systems., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

The wind turbine side-side tower motion is known to be lightly damped. One viable active damping solution is realized by deploying individual pitch control (IPC) such that counteracting blade forces are created to alleviate the tower fatigue loading caused by this motion. Existing IPC methods for side-side tower damping in the literature, such as linear quadratic regulator and lead-lag controller, cannot accommodate direct optimization and tradeoff tunings of the wind turbine economic performance. In this work, a novel side-side tower damping IPC strategy under a convex economic model predictive control (CEMPC) framework is therefore developed to address these challenges. The main idea of the framework lies in the variable transformation in power and energy terms to obtain linear dynamics and convex constraints, over which the economic performance of the wind turbine is maximized with a globally optimal solution in a receding horizon manner. The effectiveness of the proposed method is showcased in a high-fidelity simulation environment under both steady and turbulent wind cases. Lower fatigue damage on the side-side tower bending moment is attained with an acceptable level of pitch activities, negligible impact on the blade loads, and minor improvement on the power production., Team Jan-Willem van Wingerden, Team Riccardo Ferrari

This paper presents a new framework of the FLOw Redirection and Induction Dynamics (FLORIDyn) model. It is able to dynamically simulate the wake behaviour in wind farms under heterogeneous and changing environmental conditions at a low computational cost. The novelty of this work is the improved segregation of wake dynamics and wake influence: the framework creates Observation Points (OPs) at each turbine, which propagate wind field states and turbine states downstream and follow the wind direction of the free stream velocity. These observation points cover the dynamic aspects of the simulation. The OPs, along with the stored states, are now used to derive so-called Temporary Wind Farms (TWF), which approximate the effective intra-farm wind conditions at a given location. Within these TWF, the flow conditions are homogeneous and steady state. This way, arbitrary wake models can be used to calculate the farm influence on the location. The FLORIDyn framework also provides interfaces to flow field estimators, which is tested with an effective wind speed estimator. A nine turbine case is used to highlight the quality and performance of the simulation result. Compared to its predecessor, the new FLORIDyn framework decreases the computational cost by one to two orders of magnitude, which makes it a promising candidate for real-time model predictive dynamic wind farm control., Team Jan-Willem van Wingerden, Wind Energy

FLORIDyn is a parametric control-oriented dynamic model suitable to predict the dynamic wake interactions between wind turbines in a wind farm. In order to improve the accuracy of FLORIDyn, this study proposes to calibrate the tuning parameters present in the model by employing a probabilistic setting using the UQ4WIND framework. The strategy relies on constructing a surrogate model (based on polynomial chaos expansion), which is then used to perform both global sensitivity analysis and Bayesian calibration. For our analysis, a nine wind turbine configuration in a yawed setting constitutes the test case. The results of sensitivity analysis offer valuable insight into the time-dependent influence of the model parameters onto the model output. The model parameter tied to the turbine efficiency appear to be the most sensitive parameter affecting the model output. The calibrated FLORIDyn model using the Bayesian approach yield predictions much closer to the measurement data, which is equipped with an uncertainty estimate., Team Jan-Willem van Wingerden

Control-oriented models provide a basis for wind farm control to improve power production and reduce structural loading. Wake steering is considered to be one of the most promising techniques to achieve this. Wind turbine wakes under yaw misalignment are deflected downstream and have been shown to produce a curled or kidney-shaped structure. A Navier-Stokes based code called FRED was developed to model wind farm flow in 2D to perform yaw control. To tackle the differences between 2D and 3D flow, this work introduces a generalised continuity correction and wind turbine force scaling terms to the FRED framework. The effectiveness of approximating 3D results is tested by comparison with 3D simulations in the same framework. The continuity correction is now applicable to general wind directions and effective in reducing wake width and speed-up effects. The magnitude of wake deflection can be tuned using a force scaling term. However, we show that there remains a qualitative difference in the deflection profile downstream, as well as a difference in the propagation of yaw effects over time. From this study we can conclude that there is a fundamental difference between 2D and 3D flow physics in spatial and temporal dynamics which makes the 2D modelling approach challenging for control without further empirical adjustments. The necessary corrections are likely to be complex and non-physical, leading to a departure from the first principles foundation that FRED is developed from., Team Jan-Willem van Wingerden

In recent years dynamic induction control has shown great potential in reducing wake-to-turbine interaction by increasing the mixing in the wake. With these wake mixing methods the thrust force will vary in time. If applied to a floating offshore wind turbine, it will cause the platform to move. In this paper the effect of the Helix mixing approach on a DTU10MW turbine on the TripleSpar platform and its wake is evaluated. When the Helix mixing approach is applied at Strouhal equal to 0.25, the yaw movement is excited close to the eigenfrequency of the platform resulting in significant yaw angles for small blade pitch angles. To understand the impact of the motion on the wake, the yaw motion is simulated using the free wake vortex method as implemented in Qblade. Under laminar inflow, results show that the windspeed at a distance of 5 rotor diameters downstream can be increased by up to 10% compared to a fixed-bottom turbine., Large Scale Energy Storage, Wind Energy, Team Jan-Willem van Wingerden

In recent studies, the effectiveness of different so-called wake mixing strategies has been assessed in terms of wind farm power maximization. These studies show that by dynamically varying the pitch angles of a wind turbine, wake mixing can be enhanced to increase the overall power production of a wind farm. However, such strategies also increase the loads experienced by the turbine, which may disqualify such methods as viable wind farm control strategies. In this paper, an extensive analysis of the load effects of two specific wake mixing strategies, Dynamic Induction Control (DIC) and the helix approach, is presented. The damage equivalent load of critical components such as the turbine blades and tower is assessed, and the risk of fatigue damage on the blade pitch bearings is determined. This paper therefore contributes to determining the implementability of such wake mixing strategies in wind farms of the future., Team Jan-Willem van Wingerden

The significant increase in rotor diameters seen in modern wind turbines has pushed gearbox manufacturers to introduce technological innovations to increase the torque density of current designs. Driven by the need to lower the cost of energy from wind and size limitations imposed by logistic constraints in onshore wind, a trend has emerged to increase the number of planetary stages and the number of planet gears per stage. One of the main challenges of next-generation gearbox designs is sharing the load evenly between a high number of planets. This paper presents an experimental evaluation of the mesh load factor of a modern 6MW wind turbine gearbox with five planets in the first planetary stage. Results from the traditional method, based on tooth root strain gauges, and from strain measurements in the outer surface of the ring gear are described and assessed. Both experimental approaches have yielded lower mesh load factor values than the default values required in the standard "Design requirements for wind turbine gearboxes"IEC 61400-4. Since the mesh load factor is used for gear rating and sizing, a lower value allows a more optimized gearbox design, which leads to a significant improvement in torque density and cost., Team Jan-Willem van Wingerden, Transport Engineering and Logistics

Dynamic Induction Control (DIC) is a novel, exciting branch of Wind Farm Control. It makes use of time-varying control inputs to increase wake mixing, and consequently improve the velocity recovery rate of the flow and the power production of downstream turbines. The Pulse and the Helix are two promising DIC strategies that rely on sinusoidal excitations of the collective pitch and individual pitch of the blades, respectively. While their beneficial effects are evident in simulations and wind tunnel tests, we do not yet fully understand the physics behind them. We perform a systematic analysis of the dynamics of pulsed and helicoidal wakes by applying a data-driven approach to the analysis of data coming from Large Eddy Simulations (LES). Specifically, Dynamic Mode Decomposition (DMD) is used to extract coherent patterns from high-dimensional flow data. The periodicity of the excitation is exploited by adding a novel physics informed step to the algorithm. We then analyze the power spectral density of the resulting DMD modes as a function of the Strouhal number for different pitch excitation frequencies and amplitudes. Finally, we show the evolution in time and space of the dominant modes and comment on the recognizable patterns. By focusing on the modes that contribute the most to the flow dynamics, we gather insight on what causes the increased wake recovery rate in DIC techniques. This knowledge can then be used for the optimization of the signal parameters in complex layouts and conditions., Team Jan-Willem van Wingerden, Wind Energy, Delft Center for Systems and Control

The offshore wind market is growing, resulting in larger wind turbines being installed farther away from the coast and into deeper waters. This trend brings challenges to an industry strongly depending on the use of jack-up vessels. A floating vessel equipped with a Dynamic Positioning (DP) system and a Motion Compensated Gripper Frame (MCGF) provides a more efficient solution to guide XXL monopoles during the installation phase. According to strict regulations, the offset angle of monopiles shall not be over 0.25°. To ensure this, a robust control method is required to deal with gradual change in soil stifness, wave induced motions and sensor delays. The control force exerted by the gripper frame can significantly affect the floating vessel dynamics, hence, this should be optimized. Therefore, the research goal is to analyse the system and design a robustly stable control for the MCGF. In the context of this research, cascade PID control with gain-scheduling is chosen, which is able to control the Gripper Frame during all installation steps for different soil stiffness and to reject wave disturbances. With the use of the lumped multiplicative uncertainty structure, the changing and uncertain soil-characteristics are modelled. The sensor delay to measure the monopile inclination, has a significant impact on system stability. Robust stability criteria are validated using the Nyquist criterion and by analysing the system's closed-loop poles. With the use of gain-scheduling it is possible to switch control settings during the installation. Time-domain results show that with this design approach the installation criteria are met such as the maximum monopile angle and the applied control force. Ultimately, it is shown that robust PID control with gain-scheduling for a MCGF can ensure safe, efficient and cost-effective installation of the next generation wind turbines. Future research should focus into the definition and implementation of the gain-scheduling switching, Team Jan-Willem van Wingerden

This paper presents a closed-loop controller for wind farms to provide active power control services using a high-fidelity computational fluid dynamics based wind plant simulator. The proposed design enhances power tracking stability and allows for simple understanding, where each turbine is considered as a pure time-delay system. The paper investigates the control performance with different nominal power distributions in a fully waked condition and limited power availability. Results demonstrate the improvement in power production obtained by closing the control loop, compared to greedy operation. Additionally, power tracking capabilities are enhanced with a nominal power distribution favored by axial-induction, as well as the occurrence of turbine saturation and the distribution of loads., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the blade effective wind speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: a proportional-integral-notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the nonrotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well-behaved transient response than the PIN estimator., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

Dynamic induction control (DIC) has proven to be an effective method of increasing the power output for a wind farm in both simulation studies and wind tunnel experiments. By pitching the blades of a wind turbine periodically, the recovery of the low-velocity wake is accelerated, thereby increasing the energy available to downstream turbines. The wake itself of a turbine operating with DIC has not yet been studied experimentally. This paper presents a wind tunnel experiment where the wake of a wind turbine under periodic excitation is investigated. Using three-dimensional particle image velocimetry, the velocity field behind the turbine was reconstructed. Analysis of the velocity fields indicated that the available power in the wake increases when using DIC. This increase was partially due to a lower average thrust force experienced by the turbine with DIC. However, a large difference was seen between measurement results and actuator disk theory, indicating enhanced recovery of the wake is contributing to the increased energy. Instantaneous measurements visualizing the development of blade tip vortices also showed how the location of vortex breakdown, which is directly related to re-energizing the wake, shifts over time with DIC. We believe this shifting location is contributing to the enhanced wake recovery of DIC, providing more energy to downstream wind turbines., Team Jan-Willem van Wingerden, Wind Energy, Aerodynamics

Wind turbine (WT) controllers are often geared towards maximum power extraction, while suitable operating constraints should be guaranteed such that WT components are protected from failures. Control strategies can be also devised to reduce the generated power, for instance to track a power reference provided by the grid operator. They are called down-regulation strategies and allow to balance power generation and grid loads, as well as to provide ancillary grid services, such as frequency regulation. Although this balance is limited by the wind availability and grid demand, the quality of wind energy can be improved by introducing down-regulation strategies that make use of the kinetic energy of the turbine dynamics. This paper shows how the kinetic energy in the rotating components of turbines can be used as an additional degree-of-freedom by different down-regulation strategies. In particular we explore the power tracking problem based on convex model predictive control (MPC) at a single wind turbine. The use of MPC allows us to introduce a further constraint that guarantees flow stability and avoids stall conditions. Simulation results are used to illustrate the performance of the developed down-regulation strategies. Notably, by maximizing rotor speeds, and thus kinetic energy, the turbine can still temporarily guarantee tracking of a given power reference even when occasional saturation of the available wind power occurs. In the study case we proved that our approach can guarantee power tracking in saturated conditions for 10 times longer than with traditional down-regulation strategies., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden, Team Michel Verhaegen

The current trend in the evolution of wind turbines is to increase their rotor size in order to capture more power. This leads to taller, slender and more flexible towers, which thus experience higher dynamical loads due to the turbine rotation and environmental factors. It is hence compelling to deploy advanced control methods that can dynamically counteract such loads, especially at tower positions that are more prone to develop cracks or corrosion damages. Still, to the best of the authors’ knowledge, little to no attention has been paid in the literature to load mitigation at multiple tower locations. Furthermore, there is a need for control schemes that can balance load reduction with optimization of power production. In this paper, we develop an Economic Model Predictive Control (eMPC) framework to address such needs. First, we develop a linear modal model to account for the tower flexural dynamics. Then we incorporate it into an eMPC framework, where the dynamics of the turbine rotation are expressed in energy terms. This allows us to obtain a convex formulation, that is computationally attractive. Our control law is designed to avoid the "turn-pike" behavior and guarantee recursive feasibility. We demonstrate the performance of the proposed controller on a 5MW reference WT model: the results illustrate that the proposed controller is able to reduce the tower loads at multiple locations, without significant effects to the generated power., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

Wind turbines are prone to structural degradation, particularly in offshore locations. Based on the structural health condition of the tower, power de-rating strategies can be used to reduce structural loads at the cost of power losses. This paper introduces a novel closed-loop switching control architecture to constrain the thrust in individual turbines. By taking inspiration from developments in the field of reference governors, an existing demanded power tracking controller is extended by a thrust tracking controller. The latter is activated only when a user-defined constraint on fore-aft thrust force is exceeded, which can be set based on the actual damage status of the turbine. Having a down-regulation with monotonic aerodynamic load response, a simple linear thrust tracking controller is proposed. Such a scheme can reduce aerodynamic loads while incurring acceptable losses on power production which, in a wind farm setting, can be compensated for by other turbines. Large eddy simulations demonstrate the performance of the proposed scheme on satisfying thrust constraints., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Riccardo Ferrari, Team Jan-Willem van Wingerden

The ever-increasing power capacities of wind turbines promote the use of tall and slender turbine towers. This poses a challenge from a fatigue loading perspective by the relocation of the first and lightly-damped tower side-side natural frequency into the turbine operating regime, promoting its excitation during nominal operation. The excitation of this resonance can be aggravated by periodic loading in the presence of rotor mass and/or aerodynamic imbalance. Earlier work already presented a method to prevent the side-side excitation using a combination of model demodulation and quasilinear parameter varying model predictive control techniques. However, the method does not incorporate features for active control for side-side load mitigations. Because the information of the beforementioned periodic side-side loading is unknown and unmeasurable in practical scenarios, this paper presents a Kalman filtering method for its estimation in a demodulated fashion. The Kalman filter employs an extended demodulated wind turbine model augmented with random walk models of the periodic load. The simulation result demonstrates the effectiveness of the proposed method in estimating the periodic load components along with unmeasurable tower states in their demodulated form. These estimates pose an opportunity for use in future advanced controller designs for active load reductions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Jan-Willem van Wingerden, Team Riccardo Ferrari

Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design)., Team Jan-Willem van Wingerden

Wind farm flow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm flows have been essential for exploring control strategies such as wake redirection and dynamic induction control. Free-vortex methods can provide a computationally efficient way to model wind turbine wake dynamics for control optimisation. We present a control-oriented free-vortex wake model of a 2D and 3D actuator disc to represent wind turbine wakes. The novel derivation of the discrete adjoint equations allows efficient gradient evaluation for gradient-based optimisation in an economic model-predictive control algorithm. Initial results are presented for mean power maximisation in a two-turbine case study. An induction control signal is found using the 2D model that is roughly periodic and supports previous results on dynamic induction control to stimulate wake mixing. The 3D model formulation effectively models a curled wake under yaw misalignment. Under time-varying wind direction, the optimisation finds solutions demonstrating both wake steering and a smooth transition to greedy control. The free-vortex wake model with gradient information shows potential for efficient optimisation and provides a promising way to further explore dynamic wind farm flow control., Team Jan-Willem van Wingerden, Wind Energy