bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of a Near Wake model on the performance of a Multi-Megawatt wind turbine with an active flap
Authors: Gamberini A , Barlas T , Gomez Gonzalez A 
Published: June 2024

Abstract
The impact of including the near wake model in the aeroelastic models of an SWT-DD-120 wind turbine equipped with an active trailing edge flap system (ATEF) is studied. The ATEF installed on this turbine was tested in full scale in a series of campaigns between 2018 and 2022. Since standard BEM methods cannot accurately capture the induced velocities near the transition areas of the start and end of flap add-ons due to the strong interaction of the local trailed vorticity with the neighboring blade sections, this study aims to investigate if and how the near wake model is relevant in the aerodynamic and aeroelastic characterization of rotor blades with active flaps. It is shown that the Near Wake model, when combined with an active trailing edge flap system, has a marginal impact on the blades’ thrust-related loads, independently from the presence, actuation state, and actuation frequency of the flap. The impact on the torque-related loads is also marginal at the blade root, while it becomes more relevant in the blade area where the flaps are deployed.
Effect of Induction and Blade Elasticity Modelling on Wind Turbine Rotor Performance Predictions
Authors: Aryan Navid , Öztürk İldeniz , Putra Muhammad Juanda , Sezer-Uzol Nilay , Oğuz Elif , Greco Luca 
Published: June 2024

Abstract
This study investigates the impact of blade induction modelling on the accuracy of wind turbine rotor aeroelastic predictions. It extends the capabilities of AEOLIAN (AErOeLastic sImulAtioN), a Fluid Structure Interaction (FSI) solver based on Blade Element Momentum Theory (BEMT) coupled with a Lumped Mass approach to represent the blade structure. Herein, AEOLIAN’s analytical wake induction engineering model is replaced with the outcomes of a physically-consistent three-dimensional Free-Vortex Wake (FVW) formulation initially employed in AeroROTOR. This versatile aeroelastic simulation tool is implemented within the framework of MATLAB Simulink/Simscape-Multibody©, a modular environment suitable for industry analysts, researchers, and academic users focusing on wind turbine aero-servo-elastic applications. Furthermore, it serves to lay the groundwork for the development of advanced control laws for multi-megawatt rotors, fostering innovation in the design and optimization of the next-generation wind turbines. The presented analyses focus on predicting the aeroelastic behavior of the bottom-fixed NREL 5MW rotor in uniform axial flow over the operating range, complemented by more detailed investigations at the rated condition undergoing inflow with/without wind misalignment (yaw). The study on key performance parameters is conducted by comparing with the higher-fidelity data from available Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD) coupled with CFD.
Improved performance of k − ω SST turbulence model in predicting airfoil characteristics for a wide range of airfoil thicknesses
Authors: Adeel-Ur-Rehman A , Theron J N , Kassem H , Stoevesandt B , Peinke J 
Published: June 2024

Abstract
A variety of airfoils designed for wind turbine rotor blade applications were simulated with k − ω shear stress transport (SST) turbulence model in its standard form, the k − ω SST turbulence model with a modified a
1 constant (referred to as the a
1 method), the Spalart-Allmaras turbulence model, and the panel method XFoil. To assess their performance, the results of airfoil lift, drag, and coefficient of pressure were compared against available wind tunnel data, where available. The standard k
−ω SST turbulence model is found to over-predict Reynolds shear stresses, delay the flow separation, and under-predict the separated-flow region on the airfoil’s suction side. Airfoil thicknesses between 11% and 36% of the chord length were studied. Using a modified a
1 constant, some airfoils exhibited up to a 20% improvement in the prediction of lift and drag coefficients within the post-stall range of angle of attack (AoA). It is important to note that the a
1 method’s applicability and effectiveness is specifically tested for airfoils, and its performance is highly dependent on the airfoil geometry.
Dynamic stall measurements of a harmonic pitching NACA63-018 airfoil at high Reynolds numbers
Authors: Mikkelsen Robert , Bak Christian , Gaunaa Mac , Fischer Andreas , Olsen Anders S. , Ildvedsen Sigurd , Beckerlee Jimmie 
Published: June 2024

Abstract
Wind tunnel test of the NACA63-018 airfoil is performed investigating high Reynolds number dynamic stall behaviour at PLCT [1]. The foil is tested within the reduced frequency range of k ≃0.03 and Reynolds number range Re=1.5-6M in the linear attached flow range and at stalled mean AoA at moderate harmonic pitch amplitudes. The foil is fitted with one centred high resolution pressure tab distribution and six spanwise distributed low resolution pressure tab sections for evaluation of spanwise correlations. With the additional tunnel instrumentation, cross comparison between different lift-drag estimates will be shown covering gage, airfoil pressure, wall pressure and wakerake, and evaluation of steady vs harmonic spanwise variations.
Improving data sharing in wind energy - structural health monitoring case study
Authors: Barber Sarah , Marykovskiy Yuriy , Abdallah Imad 
Published: June 2024

Abstract
A lack of data sharing in the wind energy sector presents a large barrier to increasing the value of wind energy through innovation. One way of improving data sharing is to make it “FAIR”: findable, accessible, interoperable and reusable. The FAIR Data Maturity Model is a tool developed by the Research Data Alliance that can be used to assess and improve the “FAIRness” of data, by quantifying the extent of its findability, accessibility, interoperability and reusability. In this work, we investigate how the FAIR Data Maturity Model could be applied to improve data sharing in the wind energy sector, via a structural health monitoring (SHM) case study. This case study is created as part of a WeDoWind challenge, and was chosen due to the high potential of SHM in reducing the costs of energy through predictive maintenance. WeDoWind is a framework for creating mutually beneficial collaborations, and the WeDoWind wind energy ecosystem is a growing ecosystem of diverse people all over the world sharing and exchanging knowledge and data. It is found that the FAIRness of the provided data set is limited due to the lack of community standards, and the absence of public data sharing services catering specifically to the wind energy context. However, the FAIR Data Maturity Model is successfully applied to improve the FAIRness of the data sets in the case study. A participant survey shows that this made data sharing easier in the context of a WeDoWind data sharing project. Finally, the project results in a set of recommendations for helping the wind energy community to improve the FAIRness of data.
Improvements to a novel learning algorithm for model-based wind turbine controllers
Authors: Fechner U , Spagnolo F , Mulders SP 
Published: June 2024

Abstract
Wind turbine controllers are nowadays ever more advanced and rely on accurate internal controller model information. Therefore a calibrated model is needed for attaining predictable controller performance and ensuring stable operation. To calibrate the internal model information, a novel learning control scheme has recently been proposed that exploits the dynamics of the closed-loop controlled wind turbine system, without the need for wind speed measurements. The learning algorithm thereby periodically excites the generator power controller input signal. An extremum-seeking demodulation scheme was used to calibrate the internal model information. This paper improves the existing learning scheme in two ways: Firstly, it investigates how the frequency of the excitation signal influences the signal-to-noise ratio. Secondly, the problem was reformulated as a root-finding problem. This requires using the in-phase component of the phase-corrected learning signal. In addition, a precalculated lookup table relates the measured in-phase component directly to model uncertainty. It was found that an increased excitation frequency improves the signal-to-noise ratio (SNR) by an order of magnitude. Combined, these contributions improve the convergence speed more than twenty times, addressing the effect of aerodynamic degradation and its consequences on controller performance.
Multi-Rotor Wind Turbine Control: Strategies for Pitch Replacement and Mechanical Load Mitigation
Authors: Tohamy Youssef E. , Giger Urs , Marten David , Schulte Horst 
Published: June 2024

Abstract
This paper presents a two-level control scheme for multi-rotor wind turbines. The first subordinate decentralized controller replaces the active pitch control with oversized generators that achieve the power limitation objective. In combination with this, the second higher-level controller mitigates the mechanical load on the main tower by adjusting the thrust forces acting on the side turbines through coordinated local torque control. The results proved that the decentralized controller managed to replace the pitch control by achieving the same power curve, and the load mitigation controller tended to equate the thrusts on both turbines, which mitigates the mechanical loads on the turbine tower. A high-fidelity QBlade model was developed and showed consistent results to the simplified model in SIMULINK.
Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models
Authors: Klein A , Wintermeyer-Kallen T , Stegink J , Kösters T , Zierath J , Nelles O , Abel D , Vallery H , Basler M 
Published: June 2024

Abstract
To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.
Towards fine tuning wake steering policies in the field: an imitation-based approach
Authors: Bizon Monroc C , Bušić A , Dubuc D , Zhu J 
Published: June 2024

Abstract
Yaw misalignment strategies can increase the power output of wind farms by mitigating wake effects, but finding optimal yaws requires overcoming both modeling errors and the growing complexity of the problem as the size of the farm grows. Recent works have therefore proposed decentralized multi-agent reinforcement learning (MARL) as a model-free, data-based alternative to learn online. These solutions have led to significant increases in total power production on experiments with both static and dynamic wind farms simulators. Yet experiments in dynamic simulations suggest that convergence time remains too long for online learning on real wind farms. As an improvement, baseline policies obtained by optimizing offline through steady-state models can be fed as inputs to an online reinforcement learning algorithm. This method however does not guarantee a smooth transfer of the policies to the real wind farm. This is aggravated when using function approximation approaches such as multi-layer neural networks to estimate policies and value functions. We propose an imitation approach, where learning a policy is first considered a supervised learning problem by deriving references from steady-state wind farm models, and then as an online reinforcement learning task for adaptation in the field. This approach leads to significant increases in the amount of energy produced over a lookup table (LUT) baseline on experiments done with the mid-fidelity dynamic simulator FAST.Farm under both static and varying wind conditions.
Synchronized Dynamic Induction Control: An Experimental Investigation
Authors: Van Vondelen AAW , Van Der Hoek DC , Navalkar ST , Van Wingerden JW 
Published: June 2024

Abstract
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.
New methodology to estimate fatigue from acceleration time series experimentally validated in an onshore and floating offshore wind turbine
Authors: Pimenta Francisco , Mata Gonçalo , Magalhães Filipe 
Published: June 2024

Abstract
In onshore wind turbines, the association of fatigue damage with the environmental conditions based on the wind field properties have already been shown to be effective. However, most of the predictive power of such approach is based on average results, being the informative power regarding a single event extremely reduced. In this paper, a new methodology based on a reduced order model where the tower top accelerations are explicitly taken into consideration is proposed, with the adequate adaptations for floating offshore and onshore applications. After a numerical validation of the model predictions using OpenFAST, the methodology is used to reconstruct the bending moments time series of both an onshore and a floating wind turbine that are currently being monitored with accelerometers and strain gauges. The existence of this experimental data allowed for a direct comparison of the accelerations based estimates with the values obtained from the strain gauges readings and the method was proven to reproduce with great accuracy the tower bending moments time series. This conclusion sustain the claim that the proposed methodology for fatigue estimation is more robust than conventional SCADA-driven approaches and more informative regarding each individual event, making it suitable to be used in real time decision making processes.
Enabling self calibration for the wind turbine digital twin: a blending-function learning algorithm
Authors: Liu Y. , Liu C. , Hermans K. 
Published: June 2024

Abstract
The digital twin emerges as an economically viable solution for estimating signals that would otherwise necessitate additional instrumentation on wind turbines. While effective, the digital twin model may not comprehensively mirror the real-world dynamics of a wind turbine. Conversely, the long-term operation of a wind turbine over the years often results in aerodynamic degradation. Both factors contribute to sub-optimal prediction performance of the digital twin. This paper presents a novel blending-function learning algorithm designed to address this challenge. It is a data-driven approach that eliminates the need for a priori physical knowledge of wind turbine dynamics. By employing a pre-defined sinusoidal blending function, the algorithm projects the deviation between measured and estimated signals onto a lifted function space. This transformation aids in identifying potential aerodynamic degradation, which is then fed back into the digital twin for online self-calibration of wind turbine parameters. Simulation results demonstrate that the proposed learning algorithm exhibits satisfactory performance in mitigating the impact of aerodynamic degradation on digital twin predictions. It not only identifies the degradation factor but also calibrates the model parameters in an online adaptive manner, resulting in a robust representation of the wind turbine in the digital realm and thereby maintaining its prediction performance.
Towards unsupervised fault detection for offshore wind turbine cable protection systems using contrastive learning
Authors: Duthé Gregory , L’Homme Yan , Abdallah Imad , Chatzi Eleni 
Published: June 2024

Abstract
As offshore wind power expands globally, it is essential to ensure the reliable operation of components of such critical infrastructures. A less explored instance of such components, which are though essential in terms of operation, is found in subsea turbine cables and their protection systems, whose failure can incur prolonged shutdown periods and costly repairs. We propose a novel unsupervised machine learning approach exploiting use of Distributed Acoustic Sensing (DAS) data and contrastive learning for monitoring offshore wind turbine Cable Protection Systems (CPSs). A Transformer neural network adapted for time-series ingests the high-frequency, noisy DAS CPS time-series measurements, and is trained to learn a coherent representation of the data using a contrastive learning scheme that enforces temporal and positional consistency in the latent space. This latent representation can then be used to perform anomaly detection in an unsupervised manner, alleviating the need for costly labeled offshore anomaly data. We demonstrate that a coherent representation of the data is learnt by the model, which we then use to detect synthetic anomalies and an actual CPS stabilization event.
Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines
Authors: Lio Alan W.H. , Meng Fanzhong , Larsen Gunner Chr. 
Published: June 2024

Abstract
LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers.
A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.
Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations
Authors: Gagnon Louis , Lutz Thorsten 
Published: June 2024

Abstract
This paper presents a method for automatically reconstructing and smoothing surfaces from laser-scanned wind turbine blades. The aim is to accurately reconstruct turbine blade surfaces in the absence of an accurate CAD model. The input consists of a series of imperfectly aligned blade point clouds, and the output is a CFD surface mesh. The automatic process starts by segmenting the blade into as many sections as there are points in the spanwise direction of the target CFD mesh. Each segment is prepared for conversion into a periodic B-spline by undergoing angular sorting, application of the Iterative Closest Point algorithm, and light smoothing with the Savitzky-Golay filter. The final surface mesh consists of a series of B-spline airfoils with matching control points fitted on a series of spanwise nonperiodic splines. The smoothed airfoils closely match the noisy point cloud data across the entire blade. Three blades of a single turbine were scanned and meshed. The maximum distance between the blade tips of the three clouds is 2.5 cm (0.1% radius). Minor differences in airfoil profiles were observed, but they had negligible effects on lift and drag. Pitch torques were slightly more affected.
Alignment calibration and correction for offshore wind measurements using scanning lidars
Authors: Gramitzky Kira , Jäger Florian , Hildebrand Tabea , Gloria Nathalie , Riechert Jens , Steger Matthias , Pauscher Lukas 
Published: June 2024

Abstract
Long range scanning lidars offer great potential to measure wind characteristics for a variety of application in offshore wind energy. In order to derive accurate wind measurements it is critical to thoroughly calibrate the beam alignment. In offshore environments, the pitch and roll angle of a scanning lidar can be determined from sea surface levelling (SSL). This paper compares two methods to perform SSL, a method based on PPI scans and a novel method using RHI scans. Moreover, a previously described method to identify the distance at which the laser beam enters the water is extended to be applicable to larger measurement distances. Based on data from an offshore campaign in the North Sea, the differences between the RHI and PPI methods and their sensitivity to the selected elevation angles (PPI) and distances (RHI) are analysed. It is shown that both SSL methods prove to be robust for determining pitch and roll for all examined settings. The average results of the two methods show only minor differences. The standard deviation across all settings and both methods is 0.010° for pitch and 0.024° for roll. Compared to typically observed platform movements introduced by thrust on the wind turbine, the differences between different SSL settings are small. Comparison of the platform movements measured by the high sensitivity inclinometer and SSL shows that even slight differences in pitch and roll can be detected by SSL. This indicates a high level of accuracy for both methods investigated.