Factors like growing data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Time marching simulations of DeePC and CL-DeePC are conducted using Hankel matrices of past data that are updated at every time step to induce potentially troublesome closed-loop correlations between inputs and noise. Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.

Wakes of upstream turbines impinge on downstream turbines in wind farms, causing power losses and increased fatigue. Wind farm control methods, such as the Helix approach, have been proposed to actively stimulate mixing of the wake with the free stream by pitching the blades dynamically. As a result, a periodic structure is forced in the wake, which increases average downstream wind velocity and thereby improves downstream turbines’ power production. However, downstream turbines could further exploit this periodic wake structure by pitching dynamically as well, but in sync with the phase of the incoming wake structure. Depending on the phase offset between the impinging wake and the downstream pitch, this creates destructive or constructive interference between the two wakes and further improves power production downstream. This work presents and experimentally validates such a control strategy for downstream wind turbines and evaluates it on a three-turbine wind farm in an experimental wind tunnel setting using scaled wind turbines. Results validate the controller's effectiveness and show that the third turbine's performance improvement is strongly influenced by the phase offset between the periodic wake components generated by the second turbine and those present in the upstream wake.

Wind farm flow control has been a key research focus in recent years, driven by the idea that a collectively operating wind farm can outperform individually controlled turbines. Control strategies are predominantly applied in an open-loop manner, where the current flow conditions are used to look up precomputed steady-state set points. Closed-loop control approaches, on the other hand, take measurements from the farm into account and optimize their set points online, which makes them more flexible and resilient. This paper introduces a closed-loop model-predictive wind farm controller using the dynamic engineering model FLORIDyn to maximize the energy generated by a ten-turbine wind farm. The framework consists of an Ensemble Kalman Filter to continuously correct the flow field estimate, as well as a novel optimization strategy. To this end, the paper discusses two dynamic ways to maximize the farm energy and compares this to the current look-up table industry standard. The framework relies solely on turbine measurements without using a flow field preview. In a 3-h case study with time-varying conditions, the derived controllers achieve an overall energy gain of 3% to 4.4% with noise-free wind direction measurements. If disturbed and biased measurements are used, this performance decreases to 1.9% to 3% over the greedy control baseline with the same measurements. The comparison to look-up table controllers shows that the closed-loop framework performance is more robust to disturbed measurements but can only match the performance in noise-free conditions.

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

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

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

Power losses at waked turbines due to the energy extraction of upstream turbines from the flow pose a major risk to the economic feasibility of wind farms. Helix active wake control has proven its potential to mitigate these wake-induced power losses by accelerating the recovery of the individual turbine wakes. This method leverages individual pitch control to induce a non-uniformly distributed force perturbation that rotates either in a clockwise (CW) or counterclockwise (CCW) direction around the rotor center. This deforms the wake into a helical shape that recovers faster than the wake of a conventionally controlled turbine. The CCW-oriented helix achieves higher power gains than the CW helix. Previous studies have identified a system of counter-rotating vortices to drive the wake recovery enhancement and the difference between CW and CCW helix. Nevertheless, a causal explanation for the creation of these vortices is still pending. This work contributes to understanding their creation by isolating the effect of the helix force perturbation on a symmetric wake from the impact of blade-related features like tip-vortices, hub vortex, or wake swirl. For this purpose, we perform Particle Image Velocimetry (PIV) measurements of a porous disc (PD) model in a wind tunnel. The PD is modified to mimic the helix but does not inherit the blade-related features present in a wind turbine wake. We observe the formation of two counter-rotating vortices in the far wake that deform the wake cross-section into a kidney shape, analogous to the structures present in the wake when helix active wake control is applied to a wind turbine. A conceptual comparison of PD wake and wind turbine wake implies that the wake swirl present in the turbine wake causes asymmetric reactions in several characteristics of the vortex system to changes in the rotational direction of the helix perturbation. Consequently, the dynamic, non-uniform helix perturbation alone is sufficient to activate the governing mechanisms that enhance the wake recovery when using helix active wake control, while blade-related phenomena are not fundamental to the principal processes.

Wind energy has witnessed a staggering development race, resulting in higher torque density demands for the drivetrain in general and the gearbox in particular. Accurate knowledge of the input torque and suitable models are essential to ensure reliability, but neither of them is currently available in commercial wind turbines. The present study explores how a subspace identification algorithm can be applied to fiber-optic strain sensors on a four-stage gearbox to obtain operational deflection shapes. An innovative measurement setup with 129 fiber-optic strain sensors has been installed on the outer surface of the ring gears to research the deformations caused by planet gear passage events. Operational deflection shapes have been identified by applying the multivariable output-error state space (MOESP) subspace identification method to strain signals measured on a serial production end-of-line test bench. These operational deflection shapes, driven by periodic excitations, account for almost all the energy in the measured strain signals. Their contribution is controlled by the torque applied to the gearbox. From this contribution, a torque estimate for dynamic operating conditions has been derived. Accurate knowledge of the input torque throughout the entire service life allows for future improvements in assessing the remaining useful life of wind turbine gearboxes.

Within a wind farm, each wind turbine extracts kinetic energy from the flow to convert it into electric energy. Unavoidably, this reduces the downstream availability of kinetic energy, diminishing the power generation of turbines operating in the waked region. These wake-induced power losses cumulate throughout the wind farm, posing a risk to its economic feasibility. One method that mitigates these power losses is helix active wake control. By leveraging individual blade pitch control, it induces an uneven thrust distribution over the rotor plane, which rotates either in clockwise (CW) or counterclockwise (CCW) direction around the rotor center. The wake deforms into a helical shape that recovers faster than the wake of a conventionally controlled turbine and thereby increases the total generated power. Notably, the CCW helix consistently outperforms the CW helix across all available studies. This work investigates the physical principles underlying these wake recovery enhancements using large eddy simulations (LES) of a wind turbine exposed to laminar, uniform flow. We observe a spatially coherent helical vortex structure in the wake boundary, which actively transports mean kinetic energy into the wake and, therefore, poses a fundamental contributor to the wake recovery enhancement. The opposing rotational directions of CW and CCW helixes result in distinct interactions of the helical vortex with the hub vortex, leading to different wake recovery mechanisms. In the investigated laminar inflow, the CCW helix has transported 44.8% more mean kinetic energy into the wake than the CW helix up to a streamwise position of 5D, explaining their differing efficacies observed in previous studies.

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

Periodic wakes are created on upstream wind turbines by pitching strategies, such as the Helix approach, to enhance wake mixing and thereby increase power production for wind turbines directly in their wake. Consequently, a cyclic load is not only generated on the actuating turbine’s blades but also on the waked wind turbine. While the upstream load is the result of the pitching required for wake mixing, the downstream load originates from interaction with the periodic wake and only causes fatigue damage. This study proposes two novel individual pitch control schemes in which such a periodic load on the downstream turbine can be treated: by attenuation or amplification. The former method improves the fatigue life of the downstream turbine, whereas the latter enhances wake mixing further downstream by exploiting the already-present periodic content in the wake; both were validated on a three-turbine wind farm in high-fidelity large-eddy simulations. Fatigue damage reductions of around 10% were found in the load mitigation case, while an additional power enhancement of 6% was generated on the third turbine when implementing the amplification strategy. Both objectives can easily be toggled depending on a wind farm operator’s demands and the desired loads/energy capture tradeoff.

Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential in modern, highly loaded gearbox designs, as maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Unfortunately, the traditional method of measuring dynamic mechanical torque using strain gauges placed on the outer surface of a rotating shaft and transmitting the resulting signal is unsuitable for serial deployment due to technical and economic constraints. An alternative method based on fiber-optic strain sensors placed on the stationary outer surface of the gearbox ring gear has been proposed. Like shaft torsion, the radial deformation of the ring gear is proportionate to the rotor torque. Placing the sensors on a stationary component is a cost-effective alternative for serial implementation because the need for complex and expensive data transfer via wireless transmission or a slip ring is eliminated. In this paper, we present the results of an extensive field experiment conducted to evaluate the torque measurement accuracy of this novel sensing solution installed on the gearbox of a Gamesa G97 2-MW wind turbine at the National Renewable Energy Laboratory’s Flatirons Campus. Torque measurements derived from fiber-optic strain sensors placed on the ring gear of the planetary stage are compared to conventional torque measurements from strain gauges placed on the main shaft. Two different torque estimation data processing methods were evaluated, with the method based on operational deflection shapes providing the most accurate results with an average normalized root mean square error below 0.7% for a load revolution distribution analysis. The effect of operating conditions on the torque estimate was also investigated, and the third planet-passing operational deflection shape was found to be the least sensitive to nontorque load-related effects. The fiber-optic strain sensors’ successful operation during the complete test campaign has demonstrated a robust and accurate solution for fleet-wide enhanced gearbox remaining useful life estimation.

Floating offshore wind turbines pave the way to accessing deep-water regions with abundant wind resources. However, they face specific control challenges, such as the negative damping problem and increased model complexity. Since model-based control is becoming increasingly demanding, a model-free, data-driven approach is considered. Additionally, floating wind turbines are susceptible to rough environmental disturbances. Feedforward information, such as wave elevation measurements from wave radars, may be included in the controller to lessen the impact of disturbances. Although waves have been shown to increase rotor speed oscillations and turbine loads, wave-preview-based methods have only recently been explored. To this end, this paper first proposes a modified Data-enabled Predictive Control formulation that includes past and future information about measurable disturbances. The feasibility of this control strategy is then demonstrated for floating wind turbines through mid-fidelity simulations. The model-free, feedforward controller uses a preview of wave forces acting on the floating platform and aims for rotor speed regulation. Simulations indicate that the data-driven approach has potential for floating wind turbine control, and including wave feedforward action reduces the amplitude of rotor speed oscillations.

High penetration of wind energy is pushing wind farms (WFs) to offer grid support capabilities, such as active power tracking. One of the main challenges in active power tracking for WFs is the interaction of wind turbines (WTs) through their wakes. This reduces the available wind in downstream WTs, leading them to saturation, while also affecting structural loading. With the increasing number of WTs in individual WFs, the computational and communication complexity of implementing centralized control architectures grows, posing challenges for real-world applications. In this article, we present a novel distributed control approach for active power tracking for WFs, namely multirate consensus-based distributed control (MCDC). The MCDC is designed to ensure that tracking errors caused by WT saturation are equally compensated throughout the WF, while only requiring local information exchanges between WTs. Furthermore, the proposed controller ensures that WT aerodynamic loading is balanced across the WF in a distributed manner. Finally, the overall power reference is distributed via a leader–follower consensus algorithm, resulting in a fully distributed approach. Our control approach facilitates the WF modularity and sparsity, which reduces the costs associated with control design and its applicability. Throughout this article, we demonstrate the effectiveness of the proposed MCDC through high-fidelity simulations, presenting performance comparable to the centralized control.

Benchmarking numerical models is essential for validating their accuracy and ensuring consistency across simulation platforms. This study presents a comparative benchmark analysis of two widely used Large Eddy Simulation (LES) codes, AMR-WIND and NREL SOWFA-6, focusing on wind turbine rotor performance, wake dynamics, and atmospheric boundary layer (ABL) representation. The evaluation includes an actuator line model (ALM)-based uniform inflow wind turbine simulation and ABL precursors under neutral and unstable conditions. The uniform inflow wake analysis examined differences in wind turbine induction and wake development between the two codes. Additionally, neutral and unstable atmospheric boundary layer precursors were generated for an offshore environment and compared. Results indicate a difference in wake breakdown location between the codes (one contributing factor was the difference in numerical schemes used for the advection terms.) The number of actuator points required for smooth velocity distribution across the rotor was higher for SOWFA-6 than AMR-WIND. In ABL precursors, time-averaged flow fields showed strong agreement, though minor discrepancies in turbulence were observed, particularly in unstable conditions, affecting coherence analysis. The energy distribution across wavenumbers showed a good match between the codes, with slight discrepancies observed in the large and small wavenumber regions. The cutoff wavenumber was found to be similar for both codes. Lateral and vertical coherence at small and large separations were in close agreement for the neutral ABL. However, in the unstable ABL, notable differences in coherence were observed between the codes for separations greater than 40 m.

Denser turbine spacing in wind farms leads to increased wake interactions, causing power losses when each turbine operates under its own greedy control scheme. To mitigate these effects, research is exploring strategies that consider the entire wind farm rather than singular turbines. The so-called helix approach has recently gotten significant attention from the research community. It aims to reduce wake losses through periodic individual pitch control. Wake steering on the other hand uses yaw actuation to laterally deflect the wake away from downstream turbines. In this paper, we adapt and validate a steady-state surrogate model to compute the time-averaged velocity field behind a wind turbine operating with the helix approach. The model is tuned using data from Large Eddy Simulations. We compare the helix model to wake steering and baseline operation in a wind farm case study, demonstrating that the helix approach offers promising benefits under specific wind conditions.

Wind farm control optimizes wind turbines collectively, implying that some turbines operate suboptimally to benefit others, resulting in a farm-level performance increase. This study presents a novel control strategy to optimize wind farm performance by synchronizing the wake dynamics of multiple turbines using an extended Kalman filter (EKF)-based phase estimator in a Helix control framework. The proposed method influences downstream turbine wake dynamics by accurately estimating the phase shift of the upstream periodic Helix wake and applying it to its downstream control actions with additional phase offsets. The estimator integrates a dynamic blade element momentum model to improve wind speed estimation accuracy under dynamic conditions. The results, validated through turbulent large-eddy simulations in a three-turbine array, demonstrate that the EKF-based estimator reliably tracks the phase of the incoming Helix wake, with slight offsets attributed to model discrepancies. When integrated with the closed-loop synchronization controller, significant power enhancement with respect to the single-turbine Helix can be attained (up to +10 % on the third turbine), depending on the chosen phase offset. Flow analysis reveals that the optimal phase offset sustains the natural Helix oscillation throughout the array, whereas the worst phase offset creates destructive interference with the incoming wake, which appears to negatively impact wake recovery.

Clustering multiple turbines in close vicinity gives rise to efficiency losses due to the energy extraction of upstream turbines, a phenomenon known as the wake effect. The risk wake-induced power losses pose for the economic feasibility of wind farm projects motivated several methodologies aimed at mitigating the wake effect by dynamically exciting one operational parameter of the upstream turbine. Among them are dynamic yawing, which sinusoidally varies the yaw angle of the turbine with the wind, and helix active wake control, which dynamically manipulates the turbine thrust. This study is the first to explore the potential of exciting two operational parameters simultaneously by synergizing dynamic yawing and helix active wake control. Therefore, we conduct wind tunnel experiments using a yawable porous disc model modified to mimic the effect of the helix on the flow. A particular focus is put on the relative orientation between helix and dynamic yawing. Results indicate that wake recovery enhancements achievable by synergizing helix and dynamic yawing are in the same range as both methods individually; however, at 50% lower excitation frequencies than only helix and 10° smaller yawing amplitudes compared to only dynamic yawing.

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

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