bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fatigue damage calculation of offshore wind turbines’ long-term data considering the low-frequency fatigue dynamics
Authors: Sadeghi Negin , Robbelein Koen , D’Antuono Pietro , Noppe Nymfa , Weijtjens Wout , Devriendt Christof 
Published: June 2022

Abstract
The mutual and simultaneous action of external variable forces on (offshore) wind support structures causes fatigue. Fatigue analysis in this context relies on 10-minute-long strain signals. Cycle-counting allows capturing the fatigue cycles nested within these signals but inevitably leaves some open loops called half-cycles or residuals. Some other loops are not even cycle-counted because the algorithm cannot catch low-frequency (LF) cycles spanning more than 10 minutes, as caused, e.g., by wind speed variations. Notwithstanding, LF cycles are also the most damaging since they always contain the highest range in the variable amplitude signal. Therefore, counting multiple 10-minute signals and merging the resulting histograms into one inevitably has some non-conservative effects, for it leaves the LF cycles uncounted. In this work, we avoid signal concatenation to recover the LF effect. To do so, we use the residuals sequence from the 10-minute signals as it embeds the LF information. As method validation, we compare the impact of LF fatigue dynamics recovery on the linearly accumulated damage using a real-life dataset measured at a Belgian offshore wind turbine. By defining a factor that incorporates the LF effect, we observe that after 300 days of observation, the factor converges to a fixed value.
Wind turbine gearbox fault prognosis using high-frequency SCADA data
Authors: Verma Ayush , Zappalá Donatella , Sheng Shawn , Watson Simon J. 
Published: June 2022

Abstract
Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.
Data-driven state estimation toward blade individual load-reduction of wind turbines1
Authors: Klein A , Konrad T , Abel D 
Published: June 2022

Abstract
As wind turbines tend to be build larger every year, the importance of load-reducing controls to damp the rotor blade oscillations increases. For a control algorithm to perform blade individual load-reducing strategies, online information about each rotor blade’s deflection is needed. An approach for wind turbine state estimation with special attention to the rotor blade deflections is given using a Hammerstein model driven by simulation data in a Kalman Filter (KF). The Hammerstein’s linear submodel is mainly derived as time discrete state space system via least squares, while nonlinear effects are defined in terms of their proportionality. The model noise definition required by the KF is calculated by the system model’s one-step prediction error. The simulation results show the estimate to have a standard deviation of less than 2 % in edge and 9% in flap-wise direction when using an appropriate sensor configuration. Using state-of-the-art sensors, standard deviations of less than 4% and 25% respectively are achieved. The approach is particularly attractive due to the low level of tuning required. The usage of the state estimation in a closed control loop as well as the Hammerstein model in a model-based control system offer room for promising further investigations.
Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions
Authors: Hulsman Paul , Sucameli Carlo , Petrović Vlaho , Rott Andreas , Gerds Alexander , Kühn Martin 
Published: June 2022

Abstract
This paper presents an analysis of the power loss of the upstream turbine for yaw misalignment depending on the inflow condition. High-quality experimental field data, obtained from a lidar wind profiler, met mast, GPS based yaw sensor, turbine data, and load data is used. To reduce the spreading of the power loss during yaw misalignment, the rotor effective wind speed is estimated using load data. Furthermore, the so-called power loss coefficient currently used in most engineering models, for yaw misalignment, is assessed at various inflow conditions. As main finding, the power loss coefficient increases for stronger vertical wind shear. In addition, there is an asymmetry with respect to the direction of the yaw misalignment that is enhanced by the wind shear exponent and the wind veer. Neglecting these two effects could result in in-conservative wake-steering decisions at stable stratification
A physics-, SCADA-based remaining useful life calculation approach for wind turbine drivetrains
Authors: Binsbergen Diederik van , Soares Marcelo Nesci , Pedersen Eilif , Nejad Amir R. 
Published: June 2022

Abstract
This paper describes the development of a physics-, SCADA-based model able to predict the expected lifetime for wind turbine drivetrains. A real-time coupled torsional gearbox-generator model is developed using the bond graph approach in the software 20SIM. The model uses SCADA data with a sampling frequency of one hertz to impose a load reference on the wind turbine for the simulation model. From the SCADA measurements, rotor torque is estimated and used as input load to the wind turbine rotor, while generator speed is used as reference in the control loop for maximum power point tracking. Shaft torsion is used to predict highspeed shaft radial and axial bearing loads from static equilibrium. The load amplitude and the number of stress cycles are calculated using the load duration distribution method and damage is calculated using Miner’s rule. Expected lifetime is predicted by linear extrapolation of the accumulated fatigue damage to the fatigue limit. Results show that the model can capture the torsional and electrical dynamics and that the model results agree with the reference input. The radial bearing loads match well with literature where additional sensors are used to determine the loads.
Reconstructing the bending moments time history of wind turbine tower from acceleration measurements using Gaussian processes
Authors: Pimenta Francisco , Pacheco João , Pereira Sérgio , Magalhães Filipe 
Published: June 2022

Abstract
The proper estimation of the fatigue damage accumulated over time for a given wind turbine is a key information when evaluating the remaining lifetime of the structure. While the most obvious way to do so is to instrument the structure with strain gauges, not only this is a demanding task, it also only provides information about the sections that are being monitored. In this work we propose a new methodology to estimate the fatigue damage in any section of the tower based on the accelerations time series and a single instrumented section with strain gauges. We argue that accelerations and displacements of a linear structure under stochastic loading share a well defined covariance structure. This conclusion allow us to define a Monte Carlo Markov Chain (MCMC) algorithm to constrain the parameters that best describe this structure, as well as to use all the machinery associated with gaussian processes to convert accelerations into displacements, and these into bending moments. Using artificial experimental data obtained from a previously validated numerical model as input to our method, we obtained a good agreement between the simulated and reconstructed response. The results concerning fatigue damage were also in reasonable agreement, although presenting higher deviations that must be analysed in future studies.
Model predictive control of wind turbine with aero-elastically tailored blades
Authors: Hussain R , Yue H , Recalde-Camacho L 
Published: June 2022

Abstract
The use of aero-elastically tailored blades (ATB) for large wind turbines has shown the benefit of mitigating blade loads, in a passive adaptive manner, with the design of bend-twist coupling (BTC) along the blades. The BTC design makes the blades torsionally flexible and capable of adapting to different wind speeds. However, such increased flexibility makes the turbine modeling computationally demanding and the real-time controller design more challenging. In this work, to include the ATB effect into the turbine model for control, a twofold modeling for ATB characteristics is proposed. First a static BTC distribution is added to the turbine aerodynamics to account for the blade’s pre-bend-twist design, next a second-order transfer function is introduced to approximate the blade structural dynamic response to wind speed variations. The nonlinear model of the whole ATB wind turbine is built up in Simulink, linearized and discretized into a state-space form. A model predictive controller (MPC) is developed with the actuator constraints considered. Simulation studies are conducted on a 5MW ATB wind turbine at a selected above-rated wind speed. The use of the simplified model for control is assessed and the performance of MPC is compared to the gain-scheduling baseline controller.
Flow Modelling for Wind Farm Control: 2D vs. 3D
Authors: van den Broek Maarten J. , Sanderse Benjamin , Wingerden Jan-Willem van 
Published: June 2022

Abstract
Control-oriented models provide a basis for wind farm control to improve power production and reduce structural loading. Wake steering is considered to be one of the most promising techniques to achieve this. Wind turbine wakes under yaw misalignment are deflected downstream and have been shown to produce a curled or kidney-shaped structure. A Navier-Stokes based code called FRED was developed to model wind farm flow in 2D to perform yaw control. To tackle the differences between 2D and 3D flow, this work introduces a generalised continuity correction and wind turbine force scaling terms to the FRED framework. The effectiveness of approximating 3D results is tested by comparison with 3D simulations in the same framework. The continuity correction is now applicable to general wind directions and effective in reducing wake width and speed-up effects. The magnitude of wake deflection can be tuned using a force scaling term. However, we show that there remains a qualitative difference in the deflection profile downstream, as well as a difference in the propagation of yaw effects over time. From this study we can conclude that there is a fundamental difference between 2D and 3D flow physics in spatial and temporal dynamics which makes the 2D modelling approach challenging for control without further empirical adjustments. The necessary corrections are likely to be complex and non-physical, leading to a departure from the first principles foundation that FRED is developed from.
Results of fatigue measurement campaign on XL monopiles and early predictive models
Authors: Santos Francisco d N , Noppe Nymfa , Weijtjens Wout , Devriendt Christof 
Published: June 2022

Abstract
In the present contribution, data from a measurement campaign on XL monopiles (with 9.5 MW turbines and water depths of up to 36 m) is presented. This campaign is based on data collected by three types of sensors: strain gauges (installed at the TP-tower interface), accelerations (taken at bottom, mid and upper levels of the tower) and SCADA data, comprising of wind speed, yaw angle, power, pitch angle and rpm. Additionally, wave and tidal data from a public Flemish maritime weather database is added. In a comparative analysis of XL monopiles’ and standard monopiles it was seen that the fatigue behaviour of the former radically departs from the latter, with side-to-side damage surpassing fore-aft for nominal operational conditions, and standstill fatigue damage generally being bigger than nominal. Furthermore, following a methodology described in previous OWI-Lab research, artificial neural network models are trained using the aforementioned sensor data to estimate the fore-aft and side-side tower bending moment damage equivalent loads (DEL), through the use of three months worth of data with a comparative analysis ensuing. Finally, the models’ performance is investigated for concrete operating conditions by correlating it with environmental and operating conditions (EOCs).
Effect of wind turbine size on load reduction with active flow control
Authors: Gupta Abhineet , Rotea Mario A. , Chetan Mayank , Sadman Sakib M. , Todd Griffith D. 
Published: June 2022

Abstract
Decades of wind turbine research, development and installation have demonstrated reductions in levelized cost of energy (LCOE) resulting from turbines with larger rotor diameters and increased hub heights. Further reductions in LCOE by up-scaling turbine size can be challenged by practical limitations due to a mass increase trend. On-blade, active flow control devices have the potential to disrupt this trend by allowing longer blades with less mass through an active load control system. Typically, these load control systems are developed for specific wind turbines, making it difficult to study load reduction trends with turbine size to gain further insights into the benefits and risks of this control technology. This paper quantifies the variation in load reduction, complexity, and robustness of load control systems with flow control actuators for three turbines of increasing size. It is shown that, under limited control authority, load reduction can increase with turbine size provided more elaborate control algorithms are used to preserve the bandwidth and robustness of the control system.
A data-driven approach for fault diagnosis of drivetrain system in a spar-type floating wind turbine based on the multi-point acceleration measurements
Authors: Dibaj Ali , Nejad Amir R. , Gao Zhen 
Published: June 2022

Abstract
This paper deals with the condition monitoring of a floating wind turbine drivetrain using multi-point acceleration measurements. Single sensor data obtained from drivetrain system may provide insufficient information about the health condition due to the complicated structure and applied loading on this system. As a result, multi-point measurements are required to be employed for reliable fault diagnosis. The shared information between the multi-point measurements can be used for identifying the system’s condition. In this study, the fault diagnosis of the floating wind turbine drivetrain system is performed using a data-driven approach. Fault cases are considered in bearings most likely to damage. A combined principal component analysis (PCA) and deep convolutional neural network (CNN) is proposed to extract common and fault-related information between the measurements on the one hand and to classify different health conditions of the drivetrain on the other. It will be demonstrated that PCA-based information provides more satisfactory fault diagnosis results than individual sensor data. The method is numerically validated using the acceleration responses obtained from a 5-MW reference drivetrain model installed on a spar-type floating wind turbine.
Multi-scale Navier-Stokes analysis of geometrically resolved erosion of wind turbine blade leading edges
Authors: Ortolani Andrea , Castorrini Alessio , Sergio Campobasso M 
Published: June 2022

Abstract
A multi-scale computational fluid dynamics analysis of wind turbine blade leading edge erosion is presented. The test case is a large set of eroded blade sections. These are obtained by fitting the resolved eroded leading edge geometry of the outboard part of a multi-megawatt offshore wind turbine to the NACA633-618 airfoil. The erosion geometry measured by a blade laser scan is geometrically resolved in the aerodynamic simulations, whereas the aerodynamic effects of unresolved lower-amplitude scales are accounted for by using distributed surface roughness models. The simulations also account for the laminar-to-turbulent transition of the blade boundary layers with and without distributed roughness. An existing semi-empirical model and simulations of the nominal airfoil enable one to estimate the roughness level needed to trip leading edge boundary layer transition at the considered Reynolds number of 9 million. It is found that a) the mean roughness heights of the observed geometry perturbations are well above the critical roughness height, and b) consideration of either large or small erosion scales in isolation results in underestimating the airfoil performance impairment.
Wind Turbine Rotor aerodynamic imbalance detection using CNN
Authors: Hübner G.R. , da Rosa L. D. , de Souza C. E. , Pinheiro H. , Franchi C.M. , Morim R.B. , Ekwaro-Osire S. , Dias J.P. , Dabetwar S. 
Published: June 2022

Abstract
This paper investigates the use of convolutional neural networks (CNN) to automatically detect aerodynamic imbalances in horizontal axis wind turbines (WTs). The database is assembled with low-frequency acquisition rates, similar to those obtained using supervisory control and data acquisition (SCADA) systems. The methodology considers imbalances caused by pitch errors only, which might occur due to installation faults or pitch control errors. The measured raw data is initially processed using traditional statistical techniques. Next, the Gramian Angular Field (GAF) method is used to transform the statistical data into images, and then, a CNN is trained to identify aerodynamic rotor imbalance. The proposed methodology is evaluated under numerical simulations of a 1.5 MW wind turbine, and the accuracy and feasibility of the method are demonstrated. The paper demonstrates that it is possible to detect an aerodynamic imbalance in wind turbine rotors from statistics descriptors of nacelle IMU translational accelerations and wind speeds, considering a sampling frequency of above 0.05 Hz, and using an artificial intelligence technique.
Continuous grid frequency support with variable synthetic inertia and feedforward pitch angle adjustment
Authors: Gloe Arne 
Published: June 2022

Abstract
Grid frequency support by wind turbines is increasingly demanded by grid operators around the globe. By meeting these requirements, the wind turbines leave their optimal operating point and may even be pushed out of their safe operating range during severe grid events. In the past years, the Wind Energy Technology Institute developed a controller for grid frequency support in collaboration with Suzlon Energy. This work seeks to improve this controller by adding a pitch angle adjustment depending on the operating point of the wind turbine and the power adjustment for grid support. The controller performance is analysed for three scenarios and for the whole operating range of the NREL 5 MW WT. Simulations show, that the adjustment lowers the risk for overspeed at the cost of an increased pitch rate. By contrast, the aerodynamic efficiency is not increased by the additional control loop. The effect on the tower depends heavily on the wind situation during and shortly after the grid frequency support event.
Economic nonlinear model predictive control of fatigue for a hybrid wind-battery generation system
Authors: Anand Abhinav , Loew Stefan , Bottasso Carlo L. 
Published: June 2022

Abstract
An economic nonlinear model predictive controller (ENMPC) is formulated for a wind turbine-battery hybrid generation system. The controller aims to maximize the operational profit of the generator by balancing between generated wind power and turbine tower fatigue as well as battery cyclic fatigue. Other than the tower fore-aft fatigue, tower side-side fatigue is also considered to assess impact on overall economic performance. A moving horizon estimator (MHE) is formulated to provide meaningful initialization to the ENMPC in presence of plant model mismatch.The formulated controller utilizes the parametric online rainflow counting (PORFC) approach for direct cyclic fatigue cost minimization within ENMPC. The closed-loop simulation shows significantly higher profit compared to a realistic base-case scenario and relatively higher profit compared to another economic controller.
Aeroelastic modal dynamics of floating wind turbines in anisotropic conditions based on Floquet analysis
Authors: Schinas P , Manolas D. I. , Riziotis V. A. , Voutsinas S. G. 
Published: June 2022

Abstract
A reduced order model of a floating wind turbine with 22 degrees of freedom has been implemented and validated in comparison to the multibody, hydro-servo-aero-elastic code hGAST. The multi-blade Coleman transformation is applied and Floquet stability analysis is performed. This allows on one hand, to verify the actual Floquet implementation against the standard Coleman-based eigenvalue analysis in cases the transformed dynamic system has constant coefficients and on the other hand to analyze the effects of different anisotropies on stability. Mass imbalance of the rotor and yaw misalignment have been specifically addressed. In the mass imbalance scenario, the homogeneous results are not substantially altered while in the wind yaw scenario, there is slight change of the flapwise natural frequencies and of the tower lateral frequency as well as of the corresponding damping characteristics.
Using The Helix Mixing Approach On Floating Offshore Wind Turbines
Authors: Berg Daniel van den , Tavernier Delphine de , Wingerden Jan-Willem van 
Published: June 2022

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