bluuebunny/crossref_metadata_2025_split huggingface dataset:
Potential of Mini Gurney Flaps as a Retrofit to Mitigate the Performance Degradation of Wind Turbine Blades Induced by Erosion
Authors: Papi Francesco , Melani Pier Francesco , Alber Jörg , Balduzzi Francesco , Ferrara Giovanni , Nayeri Christian Navid , Bianchini Alessandro 
Published: June 2022

Abstract
Leading edge erosion of wind turbine blades is still an important challenge for wind energy professionals, both at research and industrial level. While the efficiency and durability of materials and coatings are improving rapidly, it is important to explore innovative solutions at the aerodynamic design level to mitigate the adverse effects of surface erosion. For that, a preliminary analysis on the use of Mini Gurney Flaps (MGFs) is presented in the study. High-fidelity Computational Fluid Dynamic (CFD) simulations are used to evaluate the impact of severe leading edge (LE) erosion on the performance of the FFAW3-241 airfoil on the rotor blade of the DTU 10-MW Reference Wind Turbine, which is used as test case. CFD lift and drag polars of the eroded airfoil show that MGFs are able to partially recover aerodynamic efficiency caused by erosion; this suggested evaluating their use as a retrofit solution for blades that already experienced leading edge erosion damage. When tested on the DTU 10MW RWT blade, results show how, if sized correctly, MGFs perform as predicted: the lift curve is shifted back to its design value and performance is improved with respect to the eroded blade. Moreover, as one would expect, higher than optimal MGFs resulted in excessive lift increases and thus decreased performance, even in the case of LE-erosion. Although these devices behave as intended however, based on the results of this paper, performance decreases are noted at high tip-speed-ratios (TSR), due to the blade operating in off-design conditions.
How does the quantity, resolution, and scaling of turbulence boxes affect aeroelastic simulation convergence?
Authors: Liew Jaime , Larsen Gunner Chr. 
Published: June 2022

Abstract
Aeroelastic wind turbine simulations are extensively used in the turbine design process to estimate structural fatigue loading. While it is assumed that more stochastic realisations (seeds) and higher resolution simulations will converge to the ‘true’ result faster, a detailed study has not been published to support how many seeds and what resolution is required to achieve an arbitrary level of convergence. In this study, we perform a comprehensive study on the convergence of turbine fatigue statistics as a function of turbulence grid resolution, number of seeds, and the type of turbulence scaling. The study is performed using the HAWC2 aeroelastic code in conjunction with Mann turbulence fields of varying resolution. Key findings are that over 21 seeds are required to ensure a standard error of the mean of less than 5% for fatigues statistics, and a turbulence resolution of at least 2048×64×64 (in the longitudinal, lateral and vertical, respectively) is required to ensure statistical convergence of a 10 minute simulation on one turbine. The presented study provides a valuable insight into the uncertainties involved in turbine fatigue load calculations, as well as recommendations on how to mitigate them.
Effect of erosion morphology on wind turbine production losses
Authors: Saenz E , Mendez B , Muñoz A 
Published: June 2022

Abstract
The impact of wind turbine (WT) blade erosion is still a major issue today because of the associated high maintenance costs and the loss of power production. The erosion process is influenced by many physical phenomena that still need to be understood better. Especially WT manufactures and owners need to know the potential impact of different levels of blade erosion on annual energy production (AEP). The damage produced by erosion modifies the aerofoil shape and therefore its aerodynamic properties, resulting in an AEP drop. In this study, the effect of various morphologies of erosion on AEP was obtained. First, a 3D characterisation of eroded blades using a laser scanner was performed. The erosion damages were grouped into two typologies: Typology 1 covers pits and gouges, and Typology 2 covers extended loss of top coat. Based on this, 2D section geometries were defined to evaluate the effect of erosion on the aerofoil aerodynamic 2D properties, which was done using the CFD code WMB. The aerofoil used as a reference was DU96W180. It was determined that the erosion damage alters the aerodynamic behaviour of the aerofoil by reducing the Cl max and the slope of the Cl- α curve, which resulted in a drop of the aerodynamic efficiency from 40% to 72%. These detrimental effects increased depending on the damage location (i.e., pressure side and closeness to the leading edge), damage size and shape (i.e., sharp transitions from the eroded to non-eroded surfaces). To evaluate the erosion effect on the AEP, the NREL 5 MW WT was selected. All its components were kept except the last 30% outer section of the 61.5 m blade whose NACA64_A17 aerofoil was replaced by a DU96W180. The erosion damages were modelled by extending the 2D eroded geometry along this outer section. The AEP was calculated using BLADED 4.5. It was concluded that erosion resulted in a decrease of the AEP, which can be up to 1% and 6% for damages of Typology 1 and 2, respectively. This study will help to standardize and estimate the effect of the type of erosion on AEP losses for different shapes and erosion typologies.
Impact of turbulent inflow and orography on the low-frequency noise sources of a wind turbine
Authors: Wenz F. , Lutz T. , Krämer E. 
Published: June 2022

Abstract
A high-fidelity process chain is used to numerically investigate the low-frequency emissions from a generic wind turbine under turbulent inflow conditions. It consists of the Computational Fluid Dynamics (CFD) solver FLOWer, which is coupled to the multi-body simulation software SIMPACK to take unsteady aeroelastic effects into account. A realistic flow field for the complex terrain of Perdigão, Portugal is obtained by including the orography and vegetation in the simulation and using a precursor simulation with E-Wind to generate a site- and situation-specific inflow. The acoustic emissions are calculated with the Fflowcs-Williams-Hawkings (FW-H) acoustic solver ACCO. The simulations show that the tower emits noise caused by the blade-tower interaction (BTI) equally strong in all directions. To the sides of the turbine, this contribution is dominant, regardless of the turbulent inflow. The blades, on the other hand, emit significantly more noise under turbulent inflow, especially in streamwise direction, where they become the dominant source. The main noise mechanism here is the low-frequency part of the inflow turbulence (IT), followed by the BTI. The flow field in Perdigão causes additional large-scale variations in IT noise over time.
The detection of generator bearing failures on wind turbines using machine learning based anomaly detection
Authors: Chesterman X , Verstraeten T , Daems PJ , Perez Sanjines F , Nowé A , Helsen J 
Published: June 2022

AbstractIn this research an early warning methodological framework is developed that is able to detect premature failures due to excessive wear. The methodology follows the data-driven Normal Behavior Model (NBM) principle, in which one or more data-driven models are used to model the normal behavior of the wind turbine. Anomalous behaviour of the turbine is identified by analyzing the deviation between the observed and predicted normal behaviour. The framework consists of two pipelines, a statistics and machine learning based pipeline. The former is based on techniques like ARIMA, OLS and CUSUM. The latter makes use of techniques like Random Forest, Gradient Boosting, … Each pipeline has its strengths and weaknesses, but by combining them in an intelligent way, a more capable detector is developed. The methodology is validated on 10-minute SCADA data from a real operational wind farm. The validation case focuses on generator (front/rear) bearing failures. The goal is to predict these failures well in advance (ideally at least a month) using the developed framework, which should allow for timely adjustments to the maintenance plan. The results show that the methodology is able to accomplish this reliably.
Modelling of turbine power and local wind conditions in wind farm using an autoencoder neural network
Authors: Dou Suguang , Dimitrov Nikolay 
Published: June 2022

Abstract
Neural network models have been widely applied in wind energy to enable data-driven modelling of turbine’s power, loads, damage, and other quantities in wind farm. They have shown advantages in the flexibility, robustness, and efficiency, as well as the ease of application. An important task in data-driven modelling of wind farm is the characterization of wake-induced effects within wind farm. Because of the large number of wind turbines in wind farm and the broad range of conditions required for power output assessment, it has become relevant to use computationally efficient surrogate models that are based on parameterizations of the wind farm layout. Previous studies on surrogate models of wake-induced effects have focused on parameterizations based on manually extracted features from the wind farm geometry. The present study applies an autoencoder neural network to automatically learn the features representing the wind farm layout. Specifically, two autoencoders are studied and compared. The autoencoder provides dimension reduction and feature learning that allow representing the entire wind farm layout in just a few latent variables, thus allowing efficient and accurate surrogate models of wake-induced effects. The outputs of the autoencoder, together with any ambient wind condition variables, serve as inputs to a standard feedforward neural network that predicts the wake-induced effects for any turbine in the wind farm. Our results show that the autoencoder provides an automatic and effective way to parameterize the wind farm geometry, and facilitates the building of efficient surrogate models that are capable of accurate modeling of wake-induced effects in a wind farm with arbitrary layout.
Autoencoder and Mahalanobis distance for novelty detection in structural health monitoring data of an offshore wind turbine.
Authors: Weil Maximillian , Weijtjens Wout , Devriendt Christof 
Published: June 2022

Abstract
Structural Health Monitoring (SHM) has seen an explosion in data gathering in the last few years. This is illustrated in the offshore wind industry through an increase in the amount of placed offshore wind turbines (OWT), a higher rate of SHM instrumented OWTs and an increase in the sampling rate. The growing data gathering has led to the interest of big data techniques in the SHM industry. This paper introduces a new more robust unsupervised novelty detection pipeline combining an autoencoder and the Mahalanobis distance and comparing this combination to both methods separately. This reduces the high dimensional input to a one dimensional novelty index for detecting anomalies in the OWT. Additionally the research considers the challenges that the downtime of non operationally essential sensors poses, a method is introduced to guarantee high model availability without losing the benefit of high fidelity.
Region-based convolutional neural network for wind turbine wake characterization from scanning lidars
Authors: Aird J A , Quon E W , Barthelmie R J , Pryor S C 
Published: June 2022

Abstract
A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.
Transfer Learning of Model-Based Neural Network for Transfer Function Inversion and Load Monitoring of Wind Turbines
Authors: Delbos F , Caleyron F. , Leroy J-M. 
Published: June 2022

Abstract
One main concern when using a generic model of a wind turbine is how can we apply it on real measurements for different turbines of the same type. This paper gives a first answer by proposing an inversion approach of a transfer function. Then that approach is tested on a real test case. Two transfer functions from two different turbines of the same farm are inverted on SCADA measurements with transfer learning of a model-base neural network. The inversion results show better predictions than ones with a transfer function classically trained with LIDAR data. An application to the prediction of damage equivalent load with a generic model is showed: the results of applying the inverted transfer function is compared to real DEL measurements and to the transfer function trained from LIDAR data.
On the ill-conditioning of the combined wind speed estimator and tip-speed ratio tracking control scheme
Authors: Brandetti L , Liu Y , Mulders SP , Ferreira C , Watson S , van Wingerden JW 
Published: June 2022

Abstract
In recent years, industrial controllers for modern wind turbines have been designed as a combined wind speed estimator and tip-speed ratio (WSE-TSR) tracking control scheme. In contrast to the conventional and widely used Kω
2 torque control strategy, the WSE-TSR scheme provides flexibility in terms of controller responsiveness and potentially improves power extraction performance. However, both control schemes heavily rely on prior information about the aerodynamic properties of the turbine rotor. Using a control-oriented linear analysis framework, this paper shows that the WSE-TSR scheme is inherently ill-conditioned. The ill-conditioning is defined as the inability of the scheme to uniquely determine the wind speed from the product with other model parameters in the power balance equation. Uncertainty of the power coefficient contribution in the latter mentioned product inevitably leads to a biased effective wind speed estimate. As a consequence, in the presence of uncertainty, the real-world wind turbine deviates from the intended optimal operating point, while the controller believes that the turbine operates at the desired set-point. Simulation results confirm that inaccurate model parameters lead to biased estimates of the actual turbine operating point, causing sub-optimal power extraction efficiency.
Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network
Authors: Bermúdez Karen , Ortiz-Holguin Eduardo , Tutivén Christian , Vidal Yolanda , Benalcázar-Parra Carlos 
Published: June 2022

Abstract
Energy is necessary for economic growth and improved well-being, but it poses a great challenge to be generated without increasing costs and avoiding pollution. A viable option is wind energy because it is a clean and renewable. However, continuous monitoring and maintenance of wind turbines is required for the further development of wind farms. Main bearing failures were identified by the European Academy of Wind Energy as a critical issue in terms of increasing the availability and reliability of wind turbines. In this work, it is proposed a hybrid neural network for main bearing failure prognosis. This network consists of a two-dimensional convolutional neural network (to extract spatial-temporal characteristics from the data) sequentially connected with a long short-term memory network (to learn sequence patterns) to predict the slow-speed shaft temperature (the closest temperature to the main bearing). The mean square error between its real measurement and its prediction gives a failure indicator. When it is greater than a defined threshold, then an alarm is triggered and gives the maintenance staff time to check the component. The advantage of this strategy is that it does not need faulty data to be trained, since it is based on a normality model, that is, it is trained with a single class of data (healthy) and does not require incurring high costs per acquisition of new sensors since SCADA data is used (which comes in all industrial size turbine). The results show that the use of a hybrid network can identify failures around four months before a fatal failure occurs.
Deep Neural Network Hard Parameter Multi-Task Learning for Condition Monitoring of an Offshore Wind Turbine
Authors: Black Innes Murdo , Cevasco Debora , Kolios Athanasios 
Published: June 2022

Abstract
Breaking the curse of small datasets in machine learning is but one of the major challenges that cause several real-life prediction problems. In offshore wind application, for instance, this issue presents when monitoring an asset in an attempt to reduce its infant mortality failures. Another challenge could emerge when reducing the number of sensors installed in order to limit the investment in monitoring systems. To tackle these issues, the aim of this article is to investigate the impact of small data-set on conventional machine learning methods, and to outline the improvement achievable by the implementation of transfer learning approach. It provides a solution to mitigate this issue by applying a hard parameter multi-task learning approach to the supervisory control and data acquisition data from an operational wind turbine, allowing for smaller datasets to efficiently predict the status of the gearbox’s vibration data. Two experiments are carried out in this paper. The first is to envisage the possibility of using hard parameter transfer on the operational data from two wind turbines. The second is to compare the results of this model to the findings from a conventional deep neural network model trained on the data from a single turbine.
Wake state estimation of downwind turbines using recurrent neural networks for inverse dynamics modelling
Authors: Farrell W , Herges T , Maniaci D , Brown K 
Published: June 2022

Abstract
Presented in this work is a novel approach to estimate absolute lateral wake center position on the rotor plane of a waked turbine using turbine load and operating state information. The approach formulates the estimation of the absolute lateral wake position as an inverse dynamics problem and utilizes a recurrent neural network to model the inverse mapping between the wake center position and select turbine output channels. The technique is validated on experimental data collected from experiments at the Scaled Wind Farm Technology (SWiFT) facility and numerical simulations of the site in the wind farm simulator FAST.Farm. Estimator performance and analysis of optimal conditions for estimation are discussed.
Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant
Authors: Simley Eric , Debnath Mithu , Fleming Paul 
Published: June 2022

Abstract
Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thereby increasing net energy production. But research suggests that the effectiveness of wake steering strongly depends on atmospheric conditions such as stability. In this paper, we investigate results from a two-turbine wake-steering experiment at a commercial wind plant to assess the impact of stability and five other atmospheric variables on wake-steering performance. Specifically, for different atmospheric condition bins we compare the ability of the controller to achieve the intended yaw offsets, the power gain from wake steering, and the reduction in wake losses. Further, we analyze wake-steering performance as a function of wind speed to eliminate the confounding impact of different wind speed distributions in different atmospheric conditions. Overall, we find that wind direction standard deviation is the best predictor of wake-steering performance, followed by turbulence intensity and turbulent kinetic energy. The results suggest the importance of adapting wake-steering control strategies to different atmospheric conditions.
Impact of calibrated soil-monopile-interaction model on resonance frequencies
Authors: Sastre Jurado C , Stuyts B , Weijtjens W , Devriendt C 
Published: June 2022

Abstract
Offshore wind turbines (OWTs) are sensitive, dynamic structures that require accurate estimation of the natural frequency of the support structure. This requires an integrated analysis of the wind turbine structure, including a realistic representation of the foundation response. This paper studies how the natural frequency analysis is influenced by the soil-monopile interaction model following the PISA guidance. A case study of an instrumented OWT at a Belgian offshore wind farm is presented. Comparing results between the calculated and monitored natural frequencies clearly demonstrates that a realistic representation of the foundation stiffness is crucial for accurate calculation of the OWT dynamics. The integrated analysis of the OWT based on the calibrated foundation model yields very accurate results.
The effect of damage position on Operational Modal Analysis of wind turbine blades for SHM
Authors: Ernst R , Lauridsen P , Klein C , Buffetti B 
Published: June 2022

Abstract
Operational Modal Analysis (OMA) is an attractive technology for Structural Health Monitoring (SHM) for wind turbine blades as it covers the entire blade with very limited instrumentation. This work points out some caveats inherent in this technology that must be taken care of for successful implementation. In particular, we discuss how the location of damage affects the detection performance. Additionally, measurements of OMA of MW scale wind turbine generator blades are reported. These recordings show that the measurement uncertainty varies within different mode-types, mode-numbers and external conditions. We present tools to the practitioner with which the best modes for damages of certain types and positions can be selected and blind spots on the blade can be estimated. In R&D, validation of OMA type SHM often means inflicting damages at arbitrary positions on a test blade. The results imply that this needs to be augmented with numerical analysis to ensure proper coverage of the critical details in the blade. Finally, incorporating learning’s from field measurements, we discuss strategies to improve resolution for small damages and bring more transparency regarding the detection limits for this monitoring technique.