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%.

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

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

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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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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.@en

Offshore wind farms suffer substantial energy losses due to the interference of wind turbine wakes, with estimates indicating losses of approximately 10%. This thesis aims to advance the state of the art of wind farm flow control techniques for maximizing wind farm power performance. Wind farm flow control can be divided into three categories: static induction control (SIC), wake steering control (WSC), and wake mixing control (WMC). With SIC, upstream turbines are derated to create higher velocity wakes. The losses that are incurred by derating are subsequently compensated by downstream turbines. WSC redirects the wake away from downstream turbines by misaligning a turbine with respect to the incoming wind direction. Using WMC, the wake mixing process is enhanced by continuously adjusting the operating conditions of the wind turbine. Generally, this is achieved through a periodic pitching motion of the blades. This thesis covers all three categories of wind farm flow control. First, field experiments on an onshore wind farm were carried out to examine the effectiveness of SIC. Measurements indicated a 3.3% increase in power production, as well as a significant decrease in experienced turbulence intensity during favorable ambient conditions. Second, a framework was developed for improving the estimated energy for WSC with analytical steady-state wake models. Using Gaussian process regression, the framework combines the results from an analytical wake model and large eddy simulations with varying ambient conditions, resulting in a 76% increase in estimated annual energy production with respect to the analytical wake model. Finally, a set of wind tunnel experiments were carried to study the wake of a scaled wind turbine model operating with WMC using Particle Image Velocimetry (PIV). The PIV measurements showed enhanced levels of wake recovery with WMC compared to normal operation. Furthermore, a recent TU Delft innovation called ‘the helix approach’, which induces a helical velocity profile in the turbine wake, was shown to be capable of increasing the power of a two turbine array by as much as 15%.@en

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.

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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.@en

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.@en

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

In this paper, a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at a low computational cost under heterogeneous and changing wind conditions.

Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics. This is achieved by creating so-called observation points (OPs) at each time step at the rotor plane which inherit the turbine state.

In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computationally lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible.

FLORIDyn is compared to Simulator for Offshore Wind Farm Applications (SOWFA) simulations in three- and nine-turbine cases under static and changing environmental conditions. The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three-turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model-based dynamic wind farm control.@en

In the search for a lower levelized cost of wind energy, one approach is to increase the accuracy of wind turbine measurements such as wind speed and wind direction. The sensors available on wind turbines are susceptible to local turbulence and measurement bias, which can result in suboptimal turbine performance. As an alternative, recent research has considered using the sensor measurements in a coordinated manner. With such a cooperative approach, the local wind conditions can be estimated more accurately and reliably without the need for additional measurement equipment. In this paper, a novel wind field estimation approach is presented that estimates the local wind conditions based on turbine measurements using Gaussian processes. We show that the estimation framework is able to improve the accuracy of the wind direction estimate both in an offline and online manner, as well as identify possible biases in the sensors and reduce unnecessary wind turbine yaw activity.

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This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recommendations concerning the data preparation and application of the approach are given. It is shown that both the modifier adaptation and the Bayesian optimization approach can improve the power production with overall smaller yaw misalignments in comparison to the Gaussian wake model.

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Wind farm (WF) controllers adjust the control settings of individual turbines to enhance the total performance of a wind farm. Most WF controllers proposed in the literature assume a time-invariant inflow, whereas important quantities such as the wind direction and speed continuously change over time in reality. Furthermore, properties of the inflow are often assumed known, which is a fundamentally compromising assumption to make. This paper presents a novel, closed-loop WF controller that continuously estimates the inflow and maximizes the energy yield of the farm through yaw-based wake steering. The controller is tested in a high-fidelity simulation of a 6-turbine wind farm. The WF controller is stress-tested by subjecting it to strongly-time-varying inflow conditions over 5000 s of simulation. A time-averaged improvement in energy yield of 1.4% is achieved compared to a baseline, greedy controller. Moreover, the instantaneous energy gain is up to 11% for wake-loss-heavy situations. Note that this is the first closed-loop and model-based WF controller tested for time-varying inflow conditions (i.e., where the mean wind direction and wind speed change over time) at such fidelity. This solidifies the WF controller as the first realistic closed-loop control solution for yaw-based wake steering.

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In recent years, wake steering has been established as a promising method to increase the energy yield of a wind farm. Current practice in estimating the benefit of wake steering on the annual energy production (AEP) consists of evaluating the wind farm with simplified surrogate models, casting a large uncertainty on the estimated benefit. This paper presents a framework for determining the benefit of wake steering on the AEP, incorporating simulation results from a surrogate model and large eddy simulations in order to reduce the uncertainty. Furthermore, a time-varying wind direction is considered for a better representation of the ambient conditions at the real wind farm site. Gaussian process regression is used to combine the two data sets into a single improved model of the energy gain. This model estimates a 0.60% gain in AEP for the considered wind farm, which is a 76% increase compared to the estimate of the surrogate model.

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Many wind turbines are still controlled using gain scheduled, PI(D) controllers. Another option is to use a state feedback controller, such as LQG control. However, for wind turbines that is not straight forward solution, because the system is non-linear and the impact of pitching on the states varies in time. This paper presents a variation on state feedback control. An Extended Kalman Filter is combined with gain scheduling of the state feedback gains depending on aero-dynamic sensitivities, which enables an approximation of non-linear control. This controller structure is intended to be used in more comprehensive controller optimisation studies. The scheduled state feedback controller shows good behaviour and can outperform the original PID controller.@en

The design of control strategies for floating offshore wind turbines (FOWTs) is even more difficult than for onshore and bottom-fixed offshore ones and a recognized control strategy for FOWTs is currently lacking. In order to design effective control strategies, the additional dynamics of these systems should be taken into account in the models used to solve this task. This paper presents the analytical derivation of a novel model conceived for control design purposes. In detail, the model is based on a linear description of the highly non-linear phenomena that are relevant for an FOWT. The quasi-steady assumption is used to give a description of the aerodynamic loads and how these are influenced by the main control inputs. Hydrodynamic radiation and diffraction forces are introduced by means of linear-time-invariant parametric models. Simulation results shows that the proposed linear model is able to predict the structural response of the turbine system and the floating platform effectively in the case of control inputs, wind and wave disturbances. Compared to the nonlinear high-fidelity model, the proposed model shows similar results, however, without much complexity, which is promising in the desing of FOWT control strategies.

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Floating offshore wind turbines allow wind energy to be harvested in deep waters. However, additional dynamics and structural loads may result when the floating platform is being excited by wind and waves. In this work, the conventional wind turbine controller is complemented with a novel linear feedforward controller based on wave measurements. The objective of the feedforward controller is to attenuate rotor speed variations caused by wave forcing. To design this controller, a linear model is developed that describes the system response to incident waves. The performance of the feedback-feedforward controller is assessed by a high-fidelity numerical tool using the DTU 10MW turbine and the INNWIND.EU TripleSpar platform as references. Simulations in the presence of irregular waves and turbulent wind show that the feedforward controller effectively compensates the wave-induced rotor oscillations. The novel controller is able to reduce the rotor speed variance by 26%. As a result, the remaining rotor speed variance is only 4% higher compared to operation in still water.

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Modern wind farm control (WFC) methods in the literature typically rely on a surrogate model of the farm dynamics that is computationally inexpensive to enable real-time computations. As it is very difficult to model all the relevant wind farm dynamics accurately, a closed-loop approach is a prerequisite for reliable WFC. As one of the few in its field, this paper showcases a closed-loop wind farm control solution, which leverages a steady-state surrogate model and Bayesian optimization to maximize the wind-farm-wide power production. The estimated quantities are the time-averaged ambient wind direction, wind speed and turbulence intensity. This solution is evaluated for a wind farm with nine 10 MW wind turbines in large-eddy simulation, showing a time-averaged power gain of 4.4%. This is the first WFC algorithm that is tested for wind turbines of such scale in high fidelity.

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