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

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Due to the increasing share of (offshore) wind turbines, more stringent requirements on power quality have been established. Importantly, the low-voltage ride-through grid requirement states that a wind turbine must remain connected to the electrical grid after a short intermittent grid fault. In the industry mainly gain-scheduled PID-controllers are used to mitigate the effects of grid faults on turbine operation, whereas more advanced solutions have been proposed in the literature such as model predictive control or multiple parallel PI-controllers. Remarkably, all controller implementations mentioned earlier are based on feedback control, where no feedforward strategies have been discussed in the literature. However, feedforward control could improve grid fault recovery performance by exploiting the relatively known fault characteristics by virtue of the specification in the Transmission System Operator requirements. Therefore, for the first time, a norm-optimal Iterative Learning Control (NO-ILC) algorithm is presented that solves these issues by learning the feedforward signal that optimally mitigates the effects of a grid fault. The NO-ILC algorithm applies model-free learning based on iterations, in which the framework of NO-ILC has been extended to include explicit input constraints. The goal of the NO-ILC is to reduce a (quadratic) cost function on specific input and output channels whilst conforming to specific input constraints by solving an optimisation problem, with, for this study blade pitch and rotor speed as respective input and output channels. It is shown that the NO-ILC algorithm can yield improved performance on a high-fidelity model, with a 45% decrease in the cost function used by NO-ILC compared to the nominal feedback control. The optimised feedforward signals resulting from NO-ILC can be used as an analysis tool to closer match the nominal grid fault feedback controllers response with that of NO-ILC, or directly applied as a library that can supplement the feedback controllers output during a grid fault.

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

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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.

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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.

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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.@en

The expansion of the offshore wind industry in areas with high seismicity has led to engineering challenges related to the design of the offshore wind turbines (OWTs). Monopiles, i.e., tubular steel piles of large outer diameter, low aspect ratio (penetration depth over outer diameter), and relatively thin pile wall, are traditionally the preferred foundation type for OWT due to fabrication, transportation, and installation standardization. For all bottom-founded systems, soil–structure interaction (SSI) plays a crucial role in the system's response. Additional challenges arise in the case of seismic SSI as, not only the system's response, but also the seismic ground motion itself are affected by the soil characteristics. Furthermore, uncertainties related to soil properties, as derived from the soil testing campaign and interpretation, need to be thoroughly considered for OWT load calculations and the design of the support structure. The uncertainty in soil interpretation may have a large impact on the characteristics of the input seismic motion. Subsequently, SSI will affect the seismic loads acting on the support structure and the OWT. This knock-on effect of the interpretation of the soil parameters is unknown, but may be significant to account for. In fact, when a “best estimate” soil parameter set is used, the resulting seismic load may not necessarily correspond to the most probable load for the assumed seismic event. This paper investigates the influence of the uncertainty in soil parameters, as they may result from the soil interpretation, on the seismic loads. It demonstrates the skewed distribution of OWT seismic loads using a realistic design case study on a commercial OWT. Results are presented in the form of transfer functions, response spectra at mudline and normalized bending moments along the support structure. Three distinct structural components of interest are selected to evaluate the results. It is concluded that, for the analysis of OWT under seismic loading conditions in particular, it cannot be decided a priori which soil properties would result in conservative or progressive design. Based on the obtained results, recommendations are given which aim to de-risk and enhance the current design practice.

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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.@en

Nuclear norm based subspace identification methods have recently gained importance due to their ability to find low rank solutions while maintaining accuracy through convex optimization. However, their heavy computational burden typically precludes the use in an online, recursive manner, such as may be required for adaptive control. This paper deals with the formulation of a recursive version of a nuclear norm based subspace identification method with an emphasis on reducing the computational complexity. The developed methodology is analyzed through simulations on Linear Time-Varying (LTV) systems particularly in terms of convergence rate, tracking speed and the accuracy of identification and it is shown to be computationally lighter and effective for such systems, with the considered rate of change of dynamics.

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Commercial wind turbine blades are progressively becoming longer and more flexible; in order to achieve load reduction, the use of shape modifying devices is currently under research. While such modifications facilitate cost reduction, they also render the blade susceptible to the unstable aeroelastic phenomenon of flutter. To be able to detect the onset of flutter, and to modify the load control algorithm accordingly, it is desirable to perform online identification of system dynamics.
In this paper, a recursive subspace identification algorithm is augmented with a nuclear norm-based cost function for the rapid identification of changes in the dominant system behavior. The time-consuming singular value thresholding step involved in the identification is replaced by a fast randomized algorithm. The method developed is used to identify the changes in the dynamics of an experimental wind turbine equipped with shape-modifying actuators, and operated under controlled conditions in a wind tunnel. The proposed identification method shows high sensitivity to changes in system dynamics, and is shown capable of stably and rapidly identifying the onset of aeroelastic flutter.@en

Simulations of a stiff rotor configuration of the DTU 10MW Reference Wind Turbine are performed in order to assess the impact of prescribed flap motion on the aerodynamic loads on a blade sectional and rotor integral level. Results of the engineering models used by DTU (HAWC2), TUDelft (Bladed) and NTUA (hGAST) are compared to the CFD predictions of USTUTT-IAG (FLOWer). Results show fairly good comparison in terms of axial loading, while alignment of tangential and drag-related forces across the numerical codes needs to be improved, together with unsteady corrections associated with rotor wake dynamics. The use of a new wake model in HAWC2 shows considerable accuracy improvements.@en

Wind turbine load alleviation has traditionally been addressed in the literature using either full-span pitch control, which has limited bandwidth, or trailing-edge flap control, which typically shows low control authority due to actuation constraints. This paper combines both methods and demonstrates the feasibility and advantages of such a combined control strategy on a scaled prototype in a series of wind tunnel tests. The pitchable blades of the test turbine are instrumented with free-floating flaps close to the tip, designed such that they aerodynamically magnify the low stroke of high-bandwidth actuators. The additional degree of freedom leads to aeroelastic coupling with the blade flexible modes. The inertia of the flaps was tuned such that instability occurs just beyond the operational envelope of the wind turbine; the system can however be stabilised using collocated closed-loop control. A feedforward controller is shown to be capable of significant reduction of the deterministic loads of the turbine. Iterative feedforward tuning, in combination with a stabilising feedback controller, is used to optimise the controller online in an automated manner, to maximise load reduction. Since the system is non-linear, the controller gains vary with wind speed; this paper also shows that iterative feedforward tuning is capable of generating the optimal gain schedule online.@en

Trailing edge aps located outboard on wind turbine blades have recently shown considerable potential in the alleviation of turbine lifetime dynamic loads. The concept of the free-oating ap is speci_cally interesting for wind turbines, on account of its modularity and enhanced control authority. Such a ap is free to rotate about its axis; camberline control of the free-oating ap allows for aeroelastic control of blade loads. This paper describes the design of a scaled wind turbine blade instrumented with free-oating aps, intended for use in wind tunnel experiments. The nature of the ap introduces a coupled form of utter due to the aeroelastic coupling of ap rigid-body and blade out-of-plane modes; for maximal control authority it is desired to operate close to the utter limit. Analytical and numerical methods are used to perform a utter analysis of the turbine blade. It is shown that the potential ow aeroelastic model can be recast as a continuous-time Linear-Parameter-Varying (LPV) state space model of a low order, for which formal controller design methodologies are readily available. @en

For a time-varying plant operating in closed-loop with a stabilising controller, rapid changes in system dynamics can be detected online using recursive subspace identification methods to estimate the open-loop system behaviour. However, these methods usually involve a speed-accuracy trade-off: accurate identification can often only be achieved by slow updates, which increases the lag in the detection of changes in system dynamics. In this paper, a closed-loop, recursive subspace identification algorithm is extended with a convex cost function based on the nuclear norm. The nuclear norm heuristic exploits structure in the algorithm by enforcing a low-rank condition on the state predictor matrix. This condition reduces the variance of the estimates at the price of introducing a bias. The new algorithm is demonstrated for a system where the damping changes from positive to negative, and it is shown to successfully and consistently estimate the onset of open-loop instability, outperforming conventional recursive identification. Further, by tuning the forgetting factor in the estimation algorithm, a favourable speed-accuracy trade-off can be achieved.@en

Wind energy has reached a high degree ofmaturity: for wind-rich onshore locations, it is already competitive with conventional energy sources. However, for low-wind, remote and offshore regions, research efforts are still required to enhance its economic viability. While it is possible to reduce the cost of energy by upscaling wind turbines, it is believed that we may be approaching a plateau in turbine size. Beyond this plateau, the material costs associated with the high dynamic turbine loads would outweigh the benefits of increased energy capture. To postpone this plateau, research is currently being carried out in the active control of loads for lightweight, flexible rotors. Traditional control for wind turbines involves the use of fixed-structure low order controllers, the gains of which are often hand-tuned separately for each turbine class. However, for the increasingly multivariable plant, such time-invariant approaches may no longer yield good performance. As such, the thesis focusses specifically on datadriven control for these flexible turbines. First, different data-driven approaches in the literature are evaluated and categorised as two-step approaches; which involves distinct online identification and control steps; and direct approaches, which uses data to iteratively tune fixed-structure controller gains. The approaches need to be modified to be made tractable in real time for implementation on wind turbines. For time-varying plants, such as wind turbines, it is often interesting to performidentification repeatedly over time for the two-step data-driven approach. Conventional recursive identification is extended in this thesis through the use of the nuclear norm. The benefit of the nuclear norm is evident in that it increases responsiveness of the algorithm, through the suppression of the effect of external noise. Identification can be readily combined with repetitive control for reducing periodic loads in the Subspace Predictive Repetitive Control (SPRC) technique. SPRC can be performed in a restricted basis function subspace, thus reducing the computational complexity and providing smooth control signals. The control law is stabilising and performs well as long as the identification converges to relatively good estimates, and the system dynamics change slowly. For varying wind speed, the approach above would require continuous reïdentification. As an alternative, a direct data-driven approach, Iterative Feedback Tuning (IFT) has been extended to gain-schedule tuning and for designing a Linear Parameter- Varying (LPV) controller for an LPV plant. This requires an exponential increase in the number of tuning experiments per iteration; however, structure can be used to reduce computational complexity. IFT-LPV converges to a locally optimal low-order controller. These data-driven approaches are evaluated for the load control of flexible rotors. A review of the state of the art shows that, for the low-frequency region of the load spectrum, full-span pitch control has demonstrable control authority. For higher frequencies, among the new actuators, it is found that trailing-edge flaps have the highest level of technological maturity. Aeroservoelastic simulations are carried out to show the potential of the data-driven approaches. SPRC is able to adaptively tune itself to achieve average blade load reductions close to those achieved by conventional approaches under similar conditions. For these load reductions the actuator duty is roughly half of that with the conventional approach. IFT-LPV has been used to tune a feedforward controller that works on similar basis functions scheduled on the azimuth. It can provide the correct control action irrespective of wind conditions. To expand the load control design space, pitch control is designed to stabilise an upwind turbine in yaw, without the yaw drive. This approach enables a trade-off between blade and support structure loads. SPRC is then investigated with wind tunnel experiments for pitch control of a scaled wind turbine. It reduces deterministic loads by over 60%with strict control over the pitch activity, and can also compensate for asymmetric blade control authority and changed operating conditions adaptively. Further, on this setup, the concept of IPC has been shown to perform yaw stabilisation for an upwind turbine for the first time. The setup blades are then redesigned to include free-floating trailing-edge flaps. First-principles models are set up for the system, and it is found that the system shows a low wind-speed form of flutter; this is validated experimentally. Recursive identification, using the nuclear norm, is able to track the unstable mode damping, and detect flutter twice as fast as conventional methods. Finally, a feedforward controller is tuned using IFT for combined pitch and flap control; the load peaks at 1P and 2P are almost entirely attenuated. IFT is also able to tune an linear gain schedule for operation across a range of wind speeds. It is concluded that iterative methods for data-driven control perform well for the highly uncertain control problem of flexible rotor load alleviation. For this, use has to be made of the structure of the problem. The two-step approach (such as SPRC), with combined recursive identification and control law synthesis, provides a convex first approximation of the desired controller. With the help of direct approaches, (like IFT-LPV), the controller structure can be reduced and fine-tuned to improve the control performance. Such quasi-feedforward data-driven approaches can complement the existing turbine control structure and achieve enhanced load control performance for flexible rotors. @en