bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimum gust detection by nacelle-based lidar: A study on the Vestas V52
Authors: Hannesdóttir Ásta , Meseguer Urbán Albert , Spasov Filip , Larvol Antoine
Published: June 2022

In this study we use lidar measurements from a four-beam nacelle mounted lidar, together with wind turbine load measurements, to analyse events that cause extreme blade flapwise loading on the Vestas V52 at Risø campus. We define a simple gust detection algorithm that is based on fitting a linear model to the line of sight velocity measurements in order to detect changes and fluctuations in the incoming wind field. The detection algorithm is run on a whole year of measurements and we perform an iterative parameter sweep using different values for two parameters of the gust detection method. These parameters are a slope threshold and slope duration of the linear fit. The blade loads that are associated with the detected gusts are analysed to find the optimum settings of the gust detection parameters. It was found that the most extreme observed blade load was detected for a range of parameters and by setting the slope threshold to 1.2 m/s2 and duration to 4 s, we could achieve the highest average load within the parameter range. On the other hand, the highest average blade load was found by setting the slope threshold to 1.4 m/s2 and duration to 8.5 s, though the most extreme load was not detected with those settings. The gust detection method shows potential to be used as an add-on solution to a traditional wind turbine controller to mitigate extreme loading during operation.
Improved wind speed estimation and rain quantification with continuous-wave wind lidar
Authors: Jin Liqin , Angelou Nikolas , Mann Jakob , Larsen Gunner Chr.
Published: June 2022

The presence of raindrops has an adverse impact on the line-of-sight wind speed measurement of Doppler lidars. Here, we propose a method to improve the accuracy of wind speed estimation through a filtering process on rapidly sampled (3000 Hz) lidar data. For this purpose, we conducted a field study at the Risø campus of the Technical University of Denmark using a ground-based, continuous-wave Doppler lidar. Data was acquired during a three-hour period with rain. We propose that we can differentiate between the rain and aerosol back-scattering signals by assessing the maximum of the noise-normalized Doppler spectra. To reduce the influence of rain of the velocity signal, we filter away the Doppler spectra where the maximum is larger than a given threshold. The comparison between the raw and the filtered lidar data with sonic anemometer measurements acquired at the same location, shows that we can effectively remove rain signals and improve the measurement accuracy of a Doppler lidar. However, this method is not applicable when the back-scattering of aerosols and rain are characterized by the same statistics.
Rotational and blockage effects on a wind turbine model based on local blade forces
Authors: Soto-Valle R. , Gualtieri M. , Bartholomay S. , Manolesos M. , Nayeri C.N. , Bianchini A. , Paschereit C.O.
Published: June 2022

This paper describes the results of an extended experimental campaign, reporting surface pressure measurement over one of the blades of the Berlin Research Turbine (BeRT), placed in a closed-loop wind tunnel facility. BeRT is a three-bladed horizontal axis wind turbine with a 3m rotor diameter. The focus is, on the one side, on the three-dimensional effects experienced by the rotating blade, in comparison to 2D approaches by means of XFoil simulations and 2D blade section experiments. On the other side, the blockage effects are investigated between the wind turbine model, placed in the wind tunnel where a 40% blockage ratio is produced, and lifting line free vortex wake simulations, where wind tunnel walls are not considered. Additionally, CFD computations are added in the comparison, with simulations of the far-field and with the wind tunnel walls. The turbine model is studied at several operational conditions such as different blade pitch angles and turbine yaw misalignments. Results are presented in terms of local force components derived from the surface pressure measurements. It is shown that rotational augmentation is evident at the blade mid-span location despite the large blockage. Additionally, the blockage is noticed by means of an offset in both normal and tangential local forces conserving trends and features under axial inflow and yaw misalignments. It is found that the offset in forces can be counteracted by pitching the blades.
Surrogate models for predicting stall-induced vibrations on wind turbine blades
Authors: Santhanam Chandramouli , Riva Riccardo , Knudsen Torben
Published: June 2022

Stall Induced Vibrations (SIV) are an aeroelastic instability that might happen when large portions of the wind turbine blade operate in moderate stall, which leads to high internal loads that can damage the structure. Exploring the inflow space that cause SIV has a high computational cost, which quickly increases with the number of input variables, since it involves high-fidelity aeroelastic simulations. In this work, a surrogate model is used to effectively explore the behavior of SIV in a five variable inflow space consisting of wind speed, yaw angle, vertical wind shear, wind veer, and atmospheric temperature. The surrogate model, trained from the results of a few aeroelastic simulations, predicts the severity of SIV for a given inflow condition. The surrogate model is chosen by comparing the performance of a few common model types, and Artificial Neural Networks (ANN) are found to provide a good balance between accuracy and computational time. Using the ANN, the occurrence and severity of SIV in the inflow space is studied. The analysis shows that for the turbine studied, yaw angle is the most influential variable, while temperature is the least influential variable in terms of conditions that lead to occurrence of SIV. Inflow conditions with a moderate yaw angle (around 10-25 deg), high wind speeds, and high negative veer are found to be the most critical in terms of severity of SIV. The current work is expected to serve as a helping tool to decide the focus of computationally expensive simulations such as high fidelity CFD based aeroelastic simulations.
Experimental Analysis of Planet Carrier Bearing Loads under Torque and Bending Moment in a Four-Point-Suspension Drive Train
Authors: Krause A , Gnauert J , Jacobs G , Bosse D , Schmitz L
Published: June 2022

Bearing failures in wind turbine gearboxes still occur which results in downtimes and hence leads to an increase in the levelized cost of energy. One of these affected bearings are the planet carrier bearings as they are still subject to unexpected fatigue related failure modes, like pitting and spalling, which are mainly influenced by the load distribution in the bearing. Hence it is necessary to determine the influence of torque and bending moment loads onto the planet carrier bearings. In this work the generator sided planet carrier bearing load distribution is investigated. Therefore, strain gauges are applied onto the circumference of that bearing of the gearbox of a 1 MW wind turbine four-point-suspension drive train. The loading is measured while testing at the 4 MW wind turbine test bench at CWD. The relevant loading for the investigated generator sided planet carrier bearing is torque which leads to a tilting of the gearbox leading to two load zones in the bearing. Bending moments at the hub of the drive train do not influence the bearing load distribution as they are absorbed by the main bearing.
A Resource-Efficient Design for a Flexible Hydraulic-Pneumatic Flywheel in Wind Turbine Blades
Authors: Alhrshy L , Jauch C
Published: June 2022

The utilization of renewable energy resources significantly increases in order to reduce the impact of climate change. Wind turbines are one of the most important renewable energy sources and have an important role to play in power generation. They do, however, have to serve the increasingly variable demands of the grid. Some of these demands cannot be satisfied with the standard control mechanisms of state-of-the-art wind turbines. A hydraulic-pneumatic flywheel in a wind turbine rotor is one mechanism which, in addition to its various grid services, can also reduce the mechanical loads on the structure of a wind turbine. However, the installation of such a flywheel into rotor blades increases the weight of the blades. This paper focusses on the development of a design method for reducing the additional mass of the flywheel. This method incorporates the piston accumulators of the flywheel in the blade support structure, which allows for the replacement of parts of the blade spar caps with composite material from the piston accumulators. This enables the flywheel to be installed into the rotor blades without making the wind turbine significantly heavier.
Development of an open-source segmented blade design tool
Authors: Anderson Benjamin , Bortolotti Pietro , Johnson Nick
Published: June 2022

As wind turbines continue to grow ever larger to reduce the cost of energy, their blades follow suit, with the largest commercial offshore blades extending past 100 m. Massive blades such as these raise key transportation and manufacturing challenges, especially for land-based turbines. Segmented blades are one solution and are garnering increased industry and research interest. In this work, a detailed mechanical joint model is integrated into the Wind-Plant Integrated System Design and Engineering Model (WISDEM®), which will facilitate future segmented blade research and optimization. WISDEM is used to design a wind turbine with 100-m segmented blades. This wind turbine design is compared to other machines with 100-m monolithic blades designed for rail-transportability. The designs are compared in terms of blade mass and cost, turbine capital cost, annual energy production, and levelized cost of energy, with monolithic designs being the lightest and most economical. However, this result may vary by wind plant location. A variety of segmentation joint types exist, and they will inevitably vary in parameters such as cost, spanwise location, and physical characteristics. This work examines the sensitivity of wind turbine design drivers and annual energy production to a variety of the aforementioned parameters, using the open-source wind turbine design codes OpenFAST and WISDEM, finding that joint mass, stiffness, and location can have significant effects on design drivers.
Impact of high size distributed roughness elements on wind turbine performance
Authors: Méndez Beatriz , Pires Oscar
Published: June 2022

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A spectral model generalising the surface perturbations from leading edge erosion and its application in CFD
Authors: Meyer Forsting AR , Olsen AS , Gaunaa M , Bak C , Sørensen NN , Madsen J , Hansen R , Veraart M
Published: June 2022

Blade leading edge erosion (LEE) is a major cost driver in the wind energy sector. LEE is caused by the environmental conditions under which the blades operate. The impact energy of the airborne particles striking the leading edge determines the speed of erosion, thus LEE severity grows towards the blade tip and with a turbine’s tip speed. Currently there is no established method for assessing the aerodynamic impact of LEE, either numerically or experimentally caused by a lack of erosion topological data and its stochastic nature. Whilst previous studies investigated specific realisations of real-world erosion—modelling roughness, gouges, pinholes etc.—we propose a novel, reproducible representation of erosion, based on the superposition of waves with different frequencies and directions of propagation. Using lidar surface scans of a LE exposed to a rain erosion test, we demonstrate the possibility of representing surface perturbations from erosion by a simple spectrum, thereby allowing the mathematical representation of eroded surfaces. Furthermore, we demonstrate how the spectral model simplifies the analysis of LEE-affected aerofoils in CFD. Our study thus encompasses the workflow from rain erosion test → surface scan → spectral perturbation model → numerical erosion generation → 2D CFD → performance loss statistics.
On the combined use of Vortex Generators and Gurney Flaps for turbine airfoils
Authors: Chng L , Alber J , Ntouras D , Papadakis G , Kaufmann N , Ouro P , Manolesos M
Published: June 2022

While Vortex Generators (VGs) and Gurney Flaps (GFs) are commonly used for airfoil flow control, studies of a combination of the two devices are rare. The present investigation aims at examining the combined effect of VGs and GFs on a 20% thick airfoil. To this end, a wind tunnel investigation coupled with a computational study was performed. The present paper presents force, pressure and Stereo Particle Image Velocimetry measurements, along with Unsteady Reynolds Averaged Navier Stokes simulations.
Propagation of wind turbine noise: measurements and model evaluation
Authors: Nyborg C M , Fischer A , Thysell E , Feng J , Søndergaard L S , Sørensen T , Hansen T R , Hansen K S , Bertagnolio F
Published: June 2022

Sound propagation from wind turbines through the atmosphere has previously been analytically examined through numerous studies, but the amount of comparisons to measurements is limited. This paper applies three different sound propagation models; The ISO 9613-2 model, the Nord2000 ray tracing model and the WindSTAR-Pro model, to evaluate the noise at receivers in the near- and far-field of a wind turbine. The evaluation is performed by accounting for different meteorological and terrain parameters. For validation purposes, the study further uses two different data sets from measurement campaigns; Loudspeaker noise and wind turbine noise measurements. The validation evidently shows that especially the WindSTAR-Pro model and the Nord2000 model provide good agreement to the measurements for some of the validation cases, while other results are heavily influenced by the turbine wake, signal to noise ratio in the measurements or directivity of the noise source.
Extreme wind speed ramp events: A measurement-based approach for improving the modelling of ultimate loads for wind turbine design
Authors: Ranka Preeti , Valldecabres Laura , Schafhirt Sebastian , Bierbooms Wim
Published: June 2022

Ramp events, i.e., significant changes in wind speed in a short time period, have become critically important to end-users. However, only a few studies address their impact on wind turbine loads. To the best of the authors’ knowledge, these results have not yet been validated with measurements. Therefore, this paper aims to investigate the impact of extreme wind speed ramps on ultimate wind turbine loads using eight months of offshore measurements. We also compare the measured loads with simulations following the International Electrotechnical Commission (IEC) extreme turbulence model, in order to improve the modelling of ultimate loads. This is because events with a 10-min horizontal wind speed standard deviation higher than the prescribed IEC turbulence class, in line with other research, are primarily associated with ramp events. They are found to be design driving for the blade root flap-wise moments below and beyond rated wind speed, but not in the transition region. The high-frequency analysis of these moments showed a sudden pitch transition from the inactive to the active region. In general, the loads associated with ramp events did not exceed the simulations. In addition, non-ramp related extreme loads around rated wind speed, which exceeded the simulations, were associated with standard deviations slightly above the normal turbulence model (NTM) of IEC for a waked turbine, indicating the impact of wake added turbulence. In conclusion, for the ultimate load analysis, the wind speed time series should include a sudden pitch transition from the inactive to the active region in addition to wake added turbulence.
Validation of crack initiation model by means of cyclic full-scale blade test
Authors: Rosemeier M , Melcher D , Krimmer A , Wroblewski W , Antoniou A
Published: June 2022

Wind turbine rotor blades are subject to highly dynamic loads and designed for life cycles of at least 20 years, which means that materials are subjected to high-cycle fatigue. Fatigue is a design-driving loading for current and future blades. Bond lines of blades are exposed to a multi-axial stress-state due to the anisotropic thin-walled blade structure and curved, tapered, twisted, and airfoil-shaped blade geometry. To eliminate undesirable failure modes and thus increase the reliability of wind turbine rotor blades, standards and guidelines recommend that the multi-axial stress-states be taken into consideration for the limit state analysis. In addition, thermal residual stresses that develop during manufacture can have a significant impact on the fatigue life of the bond line. By means of a cyclic full-scale blade test of a commercial 81.6m long offshore blade, we validate a crack initiation model, which takes into account multi-axial thermal and mechanical stress-states, as well as the probabilistic stress-life, to predict the edge of crack initiation in the adhesive as well as the span-wise position. Both observations agreed well with the simulations. All residual normal stress components and cross-sectional plane shear stress made up the major part of the mean equivalent stress, while the mechanical stress amplitude components - longitudinal, peel, and cross-sectional plane shear stress - made up the major part of the equivalent stress amplitude.
Vortex-model-based Multi-objective Optimization of Winglets for Wind Turbines using Machine Learning
Authors: Leenders Nick , Yu Wei , Gaunaa Mac , Caboni Marco , Ferreira Carlos Simão
Published: June 2022

Different Design Driving Load constraints (DDLs), are explored in this work to determine under which constraints and conditions a winglet can have an added value to the wind turbine blade design. Multi-objective Bayesian optimization is used to maximize the rotor’s power production while minimizing the flapwise DDLs. Surrogate models, created using machine learning techniques such as Gaussian Processes and Bayesian Neural Networks, are used in combination with an acquisition function, to determine what designs should be evaluated by the lifting line model AWSM, with the goal to obtain designs that lie on the Pareto front of two or more objectives. The recent Bayesian Neural Networks as surrogate model were able to find the Pareto-front most effectively in this work. Furthermore, the results show that different DDL constraints led to different winglet designs, with noticeable differences between upwind and downwind winglet designs. Winglet designs were found to be able to increase power without increasing the thrust, root flapwise bending moment and flapwise bending moment at radial locations on the blade. A noticeable increase in power was found when introducing sweep to the winglet design.
Modelling of wind turbine gear stages for Digital Twin and real-time virtual sensing using bond graphs
Authors: Mehlan F C , Pedersen E , Nejad A R
Published: June 2022

In this paper a wind turbine high-speed gear stage model is developed for the purpose of real-time virtual sensing of gear and bearing loads in a Digital Twin framework. The model requirements are: accurate representation of gear meshing and shaft dynamics, high computational efficiency and compatibility with other Digital Twin components, such as physical sensors signals and virtual sensing methods. State equations are derived analytically using the Bond Graph method and implemented in the software 20sim for simulation. As opposed to standard multi-body simulation (MBS) software, 20sim allows for higher flexibility in implementing interfaces to other Digital Twin components. The model fidelity is close to state-of-the-art MBS models considering 6 DOF body motion, however a simplified gear contact formulation is used, which assumes ideal kinematic meshing. Nonetheless, the Bond Graph model is able to accurately reproduce the inhomogeneous load distribution over the tooth flank, as well as the cyclic compression and decompression for each meshing period. The results suggest that the presented model is capable of monitoring fatigue loads in gear contacts and bearings in a Digital Twin framework.
Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes
Authors: Singh D , Dwight R P , Laugesen K , Beaudet L , Viré A
Published: June 2022

Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turbine. Stochasticity in the inflow turbulence and irregular waves results in load responses that are best represented as random variables rather than deterministic values. Moreover, the effect of these stochastic sources on the loads depends strongly on the mean environmental conditions - for instance, at low mean wind speeds, inflow turbulence produces much less variability in loads than at high wind speeds. Statistically, this is known as heteroscedasticity. Deterministic and most stochastic surrogates do not account for the heteroscedastic noise, giving an incomplete and potentially misleading picture of the structural response. In this paper, we draw on the recent advancements in statistical inference to train a heteroscedastic surrogate model on a noisy database to predict the conditional pdf of the response. The model is informed via 10-minute load statistics of the IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with OpenFAST. Its performance is assessed against the standard Gaussian process regression. The predicted mean is similar in both models, but the heteroscedastic surrogate approximates the large-scale variance of the responses significantly better.
Individual pitch control by convex economic model predictive control for wind turbine side-side tower load alleviation
Authors: Pamososuryo AK , Liu Y , Hovgaard TG , Ferrari R , van Wingerden JW
Published: June 2022

The wind turbine side-side tower motion is known to be lightly damped. One viable active damping solution is realized by deploying individual pitch control (IPC) such that counteracting blade forces are created to alleviate the tower fatigue loading caused by this motion. Existing IPC methods for side-side tower damping in the literature, such as linear quadratic regulator and lead-lag controller, cannot accommodate direct optimization and tradeoff tunings of the wind turbine economic performance. In this work, a novel side-side tower damping IPC strategy under a convex economic model predictive control (CEMPC) framework is therefore developed to address these challenges. The main idea of the framework lies in the variable transformation in power and energy terms to obtain linear dynamics and convex constraints, over which the economic performance of the wind turbine is maximized with a globally optimal solution in a receding horizon manner. The effectiveness of the proposed method is showcased in a high-fidelity simulation environment under both steady and turbulent wind cases. Lower fatigue damage on the side-side tower bending moment is attained with an acceptable level of pitch activities, negligible impact on the blade loads, and minor improvement on the power production.
On LiDAR-assisted wind turbine retrofit control and fatigue load reductions
Authors: Lio Wai Hou , Meng Fanzhong , Larsen Gunner Chr.
Published: June 2022

The use of upstream wind speed measurement has motivated the development of LiDAR-assisted control for enhancing rotor speed tracking and fatigue structural load reductions. However, conventional LiDAR-assisted control designs often require altering the existing controller architecture, for example, by sending an additional feed-forward pitch angle signal to the pitch actuator or by incorporating the feed-forward pitch rate into the feedback controller. This work proposes LiDAR-assisted retrofit control solutions that can be implemented without any prior knowledge or modifications of the existing feedback controller. Specifically, the feed-forward pitch action is provoked by modifying the rotor speed measurement. Three retrofit methods are proposed according to the level of retrofit requirement and complexity. Numerical simulation results showed that all three LiDAR-assisted retrofit solutions could achieve good thrust-related load reductions. Thus, the proposed LiDAR-assisted retrofit solutions present a simple control upgrade to extend the lifetime of existing turbines.
Estimation of Damage Equivalent Loads of Drivetrain of Wind Turbines using Machine Learning
Authors: Kamel O , Hauptmann S , Bottasso C L
Published: June 2022

In the recent years machine learning techniques have attracted the attention of wind energy community to make use of the large amount of available data produced from the running wind turbines. These modern wind turbines are typically equipped with measurement systems and sensors that can provide a wealth of information about the operating conditions of the machine. Nevertheless, not all the acquired raw data can be used effectively to enhance the operation of a turbine. This work addresses the question of estimating the damage equivalent loads (DEL) of different components of a drivetrain. The estimation is based on low frequency sampled typically available SCADA measurements. Typical SCADA measurements that are used as input for the estimation model are generator rotational speed, low speed shaft torque and generator torque as well as, wind speed and direction. Several machine learning methods as random forests (RF), support vector machines (SVM), linear regression (LR), decision trees and neural networks (NN) were developed, exhibiting different behavior for each approach. The qualitative and quantitative performance of each algorithm are evaluated and compared against each other. Furthermore, analysis of importance of the input features is presented.
Performance assessment of wake mitigation strategies
Authors: Coquelet M , Moens M , Bricteux L , Crismer J-B , Chatelain P
Published: June 2022

Many control strategies have emerged in the field of wind energy to address wake interaction effects and try to maximize wind farm power production. They rely either on wake redirection or on induction control. While wake redirection is easily performed by yawing the turbine, efficient and practically deployable induction control is not as straightforward. Recent studies paved the way with periodic dynamic induction control (PDIC), a strategy that generates pulsatile patterns in the wake and increases wake remixing. This study aims at further investigating the physics behind PDIC through large eddy simulations at high resolution. Results show the wake shear layer destabilization leading to the pulsatile wake. This paper also presents a comprehensive comparison between greedy control, PDIC and static yaw. It focuses both on power and loads at the scale of a pair of in-line turbines. It shows that PDIC significantly increases fatigue loads and leads to moderate power gains, while properly oriented yaw leads to fatigue load reduction on the upstream turbine and noticeable increase of the power production.
Design of gain-scheduling cascade control for a motion compensated gripper frame
Authors: Schepers J M , Fidalgo Domingos D A , ter Braak J , van Winsen I , van Wingerden J W
Published: June 2022

The offshore wind market is growing, resulting in larger wind turbines being installed farther away from the coast and into deeper waters. This trend brings challenges to an industry strongly depending on the use of jack-up vessels. A floating vessel equipped with a Dynamic Positioning (DP) system and a Motion Compensated Gripper Frame (MCGF) provides a more efficient solution to guide XXL monopoles during the installation phase. According to strict regulations, the offset angle of monopiles shall not be over 0.25°. To ensure this, a robust control method is required to deal with gradual change in soil stifness, wave induced motions and sensor delays. The control force exerted by the gripper frame can significantly affect the floating vessel dynamics, hence, this should be optimized.

Therefore, the research goal is to analyse the system and design a robustly stable control for the MCGF. In the context of this research, cascade PID control with gain-scheduling is chosen, which is able to control the Gripper Frame during all installation steps for different soil stiffness and to reject wave disturbances.

With the use of the lumped multiplicative uncertainty structure, the changing and uncertain soil-characteristics are modelled. The sensor delay to measure the monopile inclination, has a significant impact on system stability. Robust stability criteria are validated using the Nyquist criterion and by analysing the system’s closed-loop poles. With the use of gain-scheduling it is possible to switch control settings during the installation. Time-domain results show that with this design approach the installation criteria are met such as the maximum monopile angle and the applied control force.

Ultimately, it is shown that robust PID control with gain-scheduling for a MCGF can ensure safe, efficient and cost-effective installation of the next generation wind turbines. Future research should focus into the definition and implementation of the gain-scheduling switching criteria.
Detection of Jacket Offshore Wind Turbine Structural Damage using an 1D-Convolutional Neural Network with a Support Vector Machine Layer
Authors: Tutivén Christian , Moreno Sueanny , Vidal Yolanda , Benalcázar Carlos
Published: June 2022

Because offshore wind turbines, particularly their foundations, operate in hostile environments, implementing a structural health monitoring system is one of the best ways to monitor their condition, schedule maintenance, and predict possible fatal failures at lower costs. A novel strategy for detecting damage in offshore wind turbine jacket foundations is developed in this work, based on a vibration monitoring methodology that reshapes the data into a multichannel array, with as many channels as correlated sensors with the predicted variable, a 1-D deep convolutional neural network to extract temporal features from the monitored data, and a support vector machine as a final classification layer. The obtained model allows the detection of three types of bar states: healthy bar, cracked bar, and bar with an unlocked bolt.
Identifying evolving leading edge erosion by tracking clusters of lift coefficients
Authors: Abdallah Imad , Duthé Gregory , Barber Sarah , Chatzi Eleni
Published: June 2022

This work proposes an approach to identify Leading Edge Erosion (LEE) of a wind turbine blade by tracking evolving and emerging clusters of lift coefficients CL
time-series signals under uncertain inflow conditions. Most diagnostic techniques today rely on direct visual inspection, image processing, and statistical analysis, e.g. data mining or regression on SCADA output signals. We claim that probabilistic multivariate spatio-temporal techniques could play an eminent role in the diagnostics of LEE specifically leveraging CL
time-series signals form multiple sections along the span of the blade. The proposed method extracts clusters’ features based on Variational Bayesian Gaussian Mixture Models (VBGMM) and tracks their spatial and temporal changes, as well as interpret the evolution of the clusters through prior physics-based assumptions. The parameters of the VBGMM are the mean, the eigenvalues and eigenvectors of the covariance matrix, and the angle of orientation of the eigenvectors. We show that the distribution of the CL
data may not show statistically separable clusters, however, the parameters of the VBGMM clusters fitted to the CL
data, allows to discriminate moving clusters primarily due to varying inflow and operating conditions, versus emerging clusters primarily due to evolving severity of the blade LEE.
Simplified dynamic inflow for control engineering models
Authors: Geisler Jens
Published: June 2022

Simplified turbine models are needed wherever computational resources are limited and the model needs to run in real-time or faster, or when it is used in analytical procedures such as linearization. These use cases are typically given in control engineering applications, especially advanced methods such as nonlinear model predictive control or state estimation. Many such models with varying levels of detail have been proposed, but most of them rely on a quasi-stationary, rotor averaged/effective representation of aerodynamic forces. Unsteady aerodynamic effects such as dynamic inflow (sometimes referred to as dynamic wake) or dynamic stall are usually neglected. But with ever more advanced methods more detailed yet still computationally efficient models are needed. Therefore, this work proposes an approach for adding the effect of dynamic inflow to any rotor effective force or moment that was previously modeled as quasi-stationary. Novel to this work is the suitability for nonlinear as well as linear models and a focus on computational efficiency while still optimally recreating the effect.
SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on Principal Component Analysis
Authors: Campoverde Lorena , Tutivén Christian , Vidal Yolanda , Benaláazar-Parra Carlos
Published: June 2022

Condition monitoring for wind turbines is essential for the further development of wind farms. Currently, many of the works are focused on the installation of new sensors to predict turbine failures, which raises the cost of wind projects. Wind turbines operate in a wide variety of environmental conditions, such as different temperatures and wind speeds that vary throughout the year season. Typically, most or all of the data available in a turbine is healthy data (operation without failure), so data-driven supervised classification methods have data imbalance problems (more data from one class). Also, when historical pre-failure data do not exist, those methods cannot be used. Taking into account the aforementioned difficulties, the stated strategy in this work is based on a principal component analysis anomaly detector for main bearing failure prognosis and its contributions are: i) this methodology is based only on healthy SCADA data, ii) it works under different seasons of the year providing its usefulness, iii) it is based only on external variables and one temperature related to the element under diagnosis, thus avoiding data containing information from other fault types, iv) it accomplishes the main bearing failure prognosis (several months beforehand), and v) the performance of the proposed strategy is validated on a real in production wind turbine.
Wind turbine load estimation using machine learning and transfer learning
Authors: Xu Guanqun , Yu Wei , Kim Taeseong
Published: June 2022

Machine learning method has always been popular to solve wind turbine related problems at a data level. However, with the limitation of the availability of relevant data, transfer learning has gained increasing attention. In this study, traditional machine learning method of artificial neural networks (ANN), together with parameter-based transfer learning method has been used to estimate wind turbine load. First, ANN load model was built for DTU 10MW wind turbine as well as NREL 5MW wind turbine. Then, parameter-based transfer learning has been applied to the above-mentioned models to estimate load for a different turbine type or two mixed turbine types. Results indicate that ANN method provides good estimation on wind turbine fatigue load. For DTU 10MW ANN model, the trend of accuracy becomes steady as the number of input samples increases and 1500 samples is deemed as the optimal number of samples for training DTU 10MW. In addition, with transfer learning, it was succeeded in building NREL 5MW model with corresponding DTU 10MW pretrained model but failed in establishing mixed dataset model neither with DTU 10MW nor with NREL 5MW pretrained model.
Wind turbine generator prognostics using field SCADA data
Authors: Peter Rudolph , Zappalá Donatella , Schamboeck Verena , Watson Simon J
Published: June 2022

This paper presents a novel prognostic method to estimate the remaining useful life (RUL) of generators using the SCADA (Supervisory Control And Data Acquisition) systems installed in wind turbines. A data-driven wind turbine anomaly classification method is developed. The anomalies are quantified into a health indicator to measure the component degradation over time. An Autoregressive Integrated Moving Average (ARIMA) time series forecasting technique is then applied to predict the RUL of the wind turbine generator. The proposed method has been validated using industry field data showing accurate predictions of RUL with a 21 day lead time for maintenance of the turbine.
Experimental Analysis of the Wake Meandering of a Floating Wind Turbine under Imposed Surge Motion
Authors: Pardo Garcia L. , Conan B. , Aubrun S. , Perret L. , Piquet T. , Raibaudo C. , Schliffke B.
Published: June 2022

With the development of floating wind farms, the understanding of the far wake becomes of utmost importance because it cannot only be based on the experience acquired on fixed offshore turbine as the range of motion of the floater due to met-ocean conditions is in the same order of magnitude compared to the energetic turbulent scales of the atmosphere and to the characteristic scales of the wake. The objective of this wind-tunnel experiment is to analyse the behaviour of the wake center of a floating turbine subject to imposed surge motion. The experiment is carried out in the LHEEA wind tunnel where a 1/500 scale wind turbine model is immersed in a realistic offshore atmospheric-boundary layer. The model is actuated in surge by a linear motor able to reproduced idealised second order surge motion of a floating platform (mainly due to the waves) with a realistic amplitude and range of frequencies. Stereoscopic particle image velocimetry measurements are performed at two planes normal to the free-stream at distances of 4.6 and 8.1 diameters downstream of the turbine. Instantaneous velocity fields acquired at 14.1 Hz are individually analysed by convolution to find the location of the wake center. Results show that the wake center spreading is largely influenced by the downstream distance and only slightly affected by the surge motion. However, when analysing the wake position time series by a power spectral density, the signature of the surge motion becomes very clear for both downstream distances. These findings tell that the far wake (at least up to 8.1 diameters downstream) of a floating wind turbine has a “memory” of the motion in its frequency content. When extrapolating to a floating wind farm, this result suggests that a turbine inside a floating wind farm will be immersed in a flow including a significant dynamic signature in the range of its own motion.
A parametric study of the mooring system design parameters to reduce wake losses in a floating wind farm
Authors: Youssef Mahfouz Mohammad , Hall Matthew , Cheng Po Wen
Published: June 2022

Wake effects inside a conventional fixed bottom wind farm decrease the power produced by the downwind turbines, hence decreasing the farm’s annual energy production (AEP). However, floating offshore wind turbines (FOWTs) have the ability to relocate their positions laterally through surge and sway motions. This flexibility provides a new degree of freedom (DOF) in the floating wind farm layout, which can be used to decrease the aerodynamic interactions inside the floating wind farm and hence decrease the wake losses. The lateral movement of FOWTs can be passively controlled by the mooring system design. The mooring system’s restoring characteristics allows the FOWT to only move within a specific area in the x-y plane known as the watch circle. Current state of the art mooring system designs are following oil and gas design basis where the floating platforms are not allowed to have large lateral displacements. In this work, we use full factorial design to analyse the effect of different mooring system design parameters on the ability of the floater to relocate its position. The analysis shows that each design parameter has a different way of affecting the FOWT’s response. The mooring lines’ headings control which wind directions cause the biggest displacements in the crosswind direction. The smaller the lines’ diameters the higher the displacements of the FOWT. Finally, the longer the line length the smaller the mooring system’s stiffness and hence the larger the FOWT’s displacement. The results of this study can be used as the basis for floating wind farm optimization, in which the wind turbines are allowed to passively relocate their positions according to the wind speed and wind direction.
Experimental validation of the aero-servo design of a large-scale floating offshore wind turbine model
Authors: Taruffi Federico , Di Carlo Simone , Muggiasca Sara , Belloli Marco
Published: June 2022

Offshore floating wind turbines are such complex systems from the point of view of global dynamics that numerical models are often insufficient in describing completely and correctly their behavior. In this framework large scale models deployed in a natural outdoor environment are a valid complement in understanding the real features of the system and in updating or validating codes. In the case of wind turbines adopting a large scale allows to better reproduce the aerodynamic behavior of the rotor. Objective of the present work is the validation of the aerodynamic design of a large-scale 1:15 model of the DTU 10 MW wind turbine deployed on a multipurpose floating platform, built in the framework of the Horizon 2020 project termed as “The Blue Growth Farm Project”. It is requested to assess the effective aerodynamic characteristics of the rotor so to understand if the physical design is well respecting the aerodynamic properties requested in the design phase. Particular care is put in the correct reproduction of the operating parameters as part of the assessment of the control system. For initial analysis a good compliance between design and physical model is found. Regarding the aerodynamic characteristics, a good agreement between the experimental Cp values and the numerical curves is observed. Overall, the physical rotor aerodynamic design is validated and the power controller steady-state performances are assessed.
Multi-fidelity Analyses of Rotor Loads of Floating Offshore Wind Turbines with Wind/Wave Misalignment
Authors: Ortolani A. , Papi F. , Bianchini A. , Persico G. , Drofelnik J. , Campobasso M.S.
Published: June 2022

Reliable predictions of the aero- and hydrodynamic loads of fixed-bottom and floating offshore wind turbines are paramount for assessing fatigue life and designing load and power control systems. However, significant uncertainty affecting aerodynamic predictions still exists. This study presents cross-comparative analyses of the predictions of aerodynamic loads and power of fixed-foundation and floating wind turbine rotors with and without yaw errors using time- and frequency-domain Navier-Stokes Computational Fluid Dynamics, and the Blade Element Momentum theory. The considered test case is the National Renewable Energy Laboratory 5 MW reference turbine, assumed to be mounted in the floating case on a semi-submersible platform and undergoing pitching motion about the tower base. Although the overall qualitative agreement of the low- and high-fidelity predictions is found to be fair in all cases, for the considered regimes the agreement between the two methods is better for the pitching rotor in aligned wind than for the yawed flows regardless of the tower motion.
Development of a Genetic Algorithm Code for the Design of Cylindrical Buoyancy Bodies for Floating Offshore Wind Turbine Substructures
Authors: Benifla Victor , Adam Frank
Published: June 2022

The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for Floating Offshore Wind Turbine substructures. The presented work is based on a previously studied buoyancy body design that allows an efficient manufacturing process and integration into different substructures. In this study, an optimization framework using a particular Genetic Algorithm is developed to parameterize the cylindrical geometry of the buoyancy body and optimize its design in terms of cost, considering loads acting on the structure as well as manufacturing and floater specific dimension restrictions. In this paper, the specific characteristics of this particular buoyancy body are highlighted and the implementation of the optimization process is detailed. Preliminary results for a given study case are presented and discussed before pointing out potential cost reductions and future improvements to be made with regards to the presented optimization work.
A Flexible, Multi-fidelity Levelised Cost of Energy Model for Floating Offshore Wind Turbines Multidisciplinary Design, Analysis and Optimisation Approaches
Authors: Sykes V , Collu M , Coraddu A
Published: June 2022

As the UK takes a step towards a greener, cleaner future aiming to be net zero by 2050, continuous development of the power network is required. A clear solution is offshore wind, having already proved its feasibility and success in nearshore sites. However, a large majority of near shore sites in the UK are already being utilised. The next step is to move into deeper waters and utilise the stronger, more consistent wind resources. A solution could be floating offshore wind which is still in its infancy, with only a few operational floating wind farms installed. Building upon the multidisciplinary design, analysis, and optimisation framework (MDAO) for floating offshore wind turbines (FOWT) being developed at the University of Strathclyde, called FEDORA, the aim of this work is to refine the LCoE model adopted by FEDORA, and applying it to perform the optimisation of the floating offshore OC3 SPAR. There is limited data on cost, therefore Hywind Scotland Pilot Park will be used as a basis for the LCoE model, allowing the results to be validated. This model is not restricted to SPARs, as it establishes a general methodology to calculate the life cycle cost of floating offshore wind farms. Utilising the improved cost model this work finds four optimised SPAR structures for four different maximum angles of inclination which can be experienced in the wind turbines operation. The improved cost model has a much higher accuracy, highlighting the initial cost model underestimates the cost of the SPAR structure by around half.
Updates on the OpenFAST Lidar Simulator
Authors: Guo Feng , Schlipf David , Zhu Hailong , Platt Andy , Cheng Po Wen , Thomas Florian
Published: June 2022

Lidar systems are able to measure the wind speed remotely by detecting the aerosol movement caused by wind. A nacelle-based lidar system scanning the wind in front of a wind turbine can provide a preview of the incoming wind before the wind interacts with the turbine. Implementing a realistic lidar simulator into the wind turbine aero-elastic simulation tool can be beneficial for various wind energy related fields, such as lidar-assisted control, load validation, and load monitoring. In previous work, a lidar simulation module has been integrated into the open-source aero-elastic simulation tool OpenFAST, covering already lidar characteristics like different scan patterns, the volume averaging along the beam, and the coupling with the nacelle motion due to turbine tower dynamics. This paper focuses on adding further features to the lidar simulation module of OpenFAST to make the lidar simulation more realistic: the evolving turbulence, the blade blockage effect, and the adjustable data availability. Further, the wind preview quality with the new features is assessed.