bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inter-turn Short Circuit Fault Diagnosis and Severity Estimation for Wind Turbine Generators
Authors: Yan Jingyi , Senemmar Soroush , Zhang Jie 
Published: June 2024

Abstract
While preventive maintenance is crucial in wind turbine operation, conventional condition monitoring systems face limitations in terms of cost and complexity when compared to innovative signal processing techniques and artificial intelligence. In this paper, a cascading deep learning framework is proposed for the monitoring of generator winding conditions, specifically to promptly detect and identify inter-turn short circuit faults and estimate their severity in real time. This framework encompasses the processing of high-resolution current signal samples, coupled with the extraction of current signal features in both time and frequency domains, achieved through discrete wavelet transform. By leveraging long short-term memory recurrent neural networks, our aim is to establish a cost-efficient and reliable condition monitoring system for wind turbine generators. Numeral experiments show an over 97% accuracy for fault diagnosis and severity estimation. More specifically, with the intrinsic feature provided by wavelet transform, the faults can be 100% identified by the diagnosis model.
Combined Individual Pitch and Flap Control for Load Reduction of Wind Turbines
Authors: Dittmer Antje , Osterwinter Bernhard , Ossmann Daniel 
Published: June 2024

Abstract
In this paper individual pitch control is combined with trailing-edge flap (TEF) control, both designed as multi-loop proportional-integral-derivative (PID) controllers, to significantly reduce blade damage equivalent loads. OpenFAST NREL 5MW aerodynamics are enhanced with dedicated aerodynamic polars of TEF blade elements. The NREL 5MW model, including the TEF extension, is incorporated into Simulink, and PID controller parameters are optimized in a closed-loop optimization setup. The DEL decrease is confirmed via an extensive simulation campaign using the non-linear wind turbine simulation environment.
A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model
Authors: Hoghooghi H , Bertelè M , Anand A , Bottasso C L 
Published: June 2024

Abstract
This paper analyzes the formulation of a wind turbine digital shadow using a Kalman filter to estimate tower loads. At first, a simple model is considered, which is limited to the sole fore-aft dynamics of the tower. Next, to quantify the potential benefits of a more refined approach, the tower side-side motion and the blade flapwise and edgewise degrees of freedom are added to the filter-internal model. For both the simple and refined formulations, the model coefficients are optionally scheduled as functions of the inflow conditions. Preliminary results, limited to non-waked and non-misaligned conditions, indicate that the refined formulation has some positive – but limited – effects on the quality of the tower fore-aft loading. Additionally, it is found that the scheduling of the coefficients does not further improve the results.
Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control
Authors: Becker M , Allaerts D , Van Wingerden J W 
Published: June 2024

Abstract
In this work, we investigate a method to derive characteristic dynamic flow field behavior from field measurements. We further explore how these changes impact the performance of a wind farm flow control strategy. For a long time, hourly to 10-min averaged data has been the predominant form to store meteorological quantities such as wind speeds and wind directions. With the decreasing cost of digital storage and improvements in measurement technology, the assimilation of higher frequent data has become more feasible. We use one of these open-source datasets provided by the KNMI to explore what characteristic flow behavior is described in the high-frequency recordings of a Wind-LiDAR located in the North-Sea. To this end we employ a K-Means algorithm to cluster 10-min time series of wind direction changes sampled at 20 s. Our study finds that the majority of wind direction changes within this time window can be described by five main clusters with clock- and counterclockwise changes of the wind direction in the range of ±4 deg. Subsequently we investigate the implications for quasi-steady wind farm flow control. We employ look-up table yaw-steering control next to baseline control in selected cases in a turbulent Large Eddy Simulation to verify the predictions made by a dynamic parametric engineering wake model. We find good agreement between both simulation environments and use the engineering model to investigate all wind directions in 2 deg resolution. The results show that the identified wind direction changes can have a significant negative impact on the power generated by a 10 turbine wind farm. The study also shows that the fixed yaw-steering set-points are still favorable over baseline operation for wind direction changes in the range of ±1.6 deg, but can act detrimental for larger changes.
Data-driven wind turbine sensor health validation
Authors: Badarinath K , Hoebeke P , Schillebeeckx D , Yazicioglu H 
Published: June 2024

Abstract
This paper presents a data-driven approach for detecting anomalies in wind turbine sensors, specifically anemometers and wind vanes, and the development of a smart alerting system. The study focuses on utilizing SCADA and reanalysis (ERA5) data for accurate anomaly detection and reducing false alarms through smart change point detection algorithms. The methodology involves modeling normal behavior, detecting change points, and comparing power curves before and after these points. For anemometer anomaly detection, a three-year SCADA dataset from an offshore wind farm and a synthetic dataset is used, employing an XGBoost model and the PELT algorithm for change point detection. Wind vane anomalies are identified using a nine month dataset from seven turbines, with synthetic alterations to simulate misalignments. Results show successful detection of sudden changes in wind speed and direction, with smart alarms assisting operators in decision-making. This research enhances wind turbine condition monitoring, improving reliability and efficiency.
Wind turbine main shaft crack detection with SCADA generator speed measurement
Authors: Hougaard H. A. , Madsen M. B. , Pedersen A. S. 
Published: June 2024

Abstract
Preventing failure of structural components is a necessity for safe and economic operation of wind farms. This paper presents a novel detection method of wind turbine main shaft fractures based on data from the turbine SCADA system thus not needing retrofit sensors. The authors developed the crack detection method in the wake of a catastrophic failure on an offshore 3.6 MW wind turbine and applied it across multiple wind farms totalling more than a thousand turbines of the same type, minimising any further risk of failure. The novel method consists of tracking the amplitude of the 2P rotor harmonic in the generator speed and is found to outperform the most common metrics proposed in shaft crack detection literature: Rotor harmonics measured in fore-aft and side-side accelerations and estimates of the drivetrain torsional eigenfrequencies. The generator speed 2P amplitude of the cracked shaft turbine was significantly elevated 16 months before failure while all other benchmark metrics failed to detect the crack. The analysis showed that no other turbine in the fleet were exhibiting similar indications of main shaft cracks and indicated that any such failure would have a long lead time.
First experimental results on lifetime-aware wind farm control
Authors: Braunbehrens R , Anand A , Campagnolo F , Bottasso C L 
Published: June 2024

Abstract
The study presents the methodology, implementation, and first results of the experimental test of a lifetime-aware wind farm control (WFC) approach. The novel strategy goes beyond the conventional WFC formulations and aims to maximise profit while ensuring fulfilment of a desired lifetime. Damage due to fatigue is formulated using a cost model and is also limited by a lifetime constraint. The control strategy is based on yaw-induced wake steering and utilises a tool chain of power and damage estimators. The experiment was conducted with three model turbines in a boundary layer wind tunnel, which allowed the simulation of a realistic dynamic variation of the wind direction. The novel strategy is compared to conventional greedy operation, as well as to the classical maximum power strategy. The results show that the novel control strategy can improve the economic performance of the farm.
An advanced farm flow estimator for the real-time evaluation of the potential wind power of a down-regulated wind farm
Authors: Moens Maud , Lejeune Maxime , Chatelain Philippe 
Published: June 2024

Abstract
This work aims at verifying the predictions of OnWaRDS, an open-source wake modelling framework that captures the main features of the wake dynamics, including its meandering, in ancillary services scenarios. OnWaRDS brings together Lagrangian flow modeling and flow sensing and runs in parallel with a wind farm environment (here Large-Eddy simulations coupled to an Actuator Disk model (LES-AD)) in order to use the available rotor states to predict the flow field. The performances of OnWaRDS are first assessed when it runs synchronously with the LES-AD of a down-regulated wind farm and tends to mimic the LES-AD behavior. This synchronous mode implies a continuous feeding of the wake model with LES-AD rotor data. Then, OnWaRDS is used in a predictive mode, in order to predict an alternate reality for the wind farm. In this study, OnWaRDS aims at evaluating, in real-time, what the potential power production would be when the LES-AD is down-regulated to provide operating reserve capacity to the electricity network. Switching to a predictive mode implies that certain measurements at the wind turbine level can no longer be used, because the flow and the rotor behavior change between LES-AD and OnWaRDS. The second part of this study thus aims at verifying the predictions of OnWaRDS, and highlighting the impact of switching from a synchronous to a predictive mode in OnWaRDS.
Maximizing value through optimized annual selection of Pareto-optimal wind turbine operating strategies
Authors: Requate Niklas , Meyer Tobias , Hofmann René 
Published: June 2024

Abstract
Wind turbines as an investment aim at turning an initial expense into a profit. Naturally, the aim of developers and operators alike is to create an economic benefit that is as high as possible. Little changes to a wind turbine structure are possible once it is built, and wind as the ultimate power source cannot be influenced. The only available means to achieve a high economic benefit is through improved operations and maintenance. We developed a method for optimizing the operation of wind turbines that builds on condition-based adaptation of the power setpoint. It aims at balancing loads and power generation such that lifetime is increased, and the total economic value is improved over simpler operating schemes. In this contribution, we extend the method with an annually varying selection of different optimal operating strategies from an initial multiobjective optimization. We show three case studies that highlight the potential of annual variation and the margin with which it improves on operating at nominal power and over a fixed optimized operating strategy. In each case study, the net present value can be improved slightly over a lifetime-constant operating point that is selected once from the Pareto-front.
Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel
Authors: Hulsman P. , Howland M. , Göçmen T. , Petrović V. 
Published: June 2024

Abstract
This paper introduces a framework to assess a hybrid data-driven wake steering strategy combining the advantages of an analytical model for the initial data gathering together with a data-driven model for modelling error correction. The main building blocks of the control strategy (required input domain, data gathering and model generation) are evaluated within the wind tunnel, utilising a newly developed setup that can simulate dynamic wind direction variations and which was specifically designed to examine wake steering control strategies. The control strategy employs a machine-learning algorithm to generate a turbine and wake model based on the measurement data, allowing this approach to be extrapolated for an arbitrarily large wind farm. The machine-learning model is used to estimate the optimal yaw angle in a closed-loop format. To implement the developed control strategy, steady measurements are analysed first to determine the required input variables. This is followed by an investigation of the potential power gain and the impact of utilising a grid-search procedure. The procedure minimises adverse conditions as it collects the required data points, still achieving a power gain.
Results of wear endurance runs of wind turbine pitch bearings
Authors: Stammler Matthias , Geibel Martin 
Published: June 2024

Abstract
Pitch bearings of wind turbines connect the rotor blade and the rotor hub. They have to cope with highly dynamic and stochastic loads and oscillating movements for lifetimes of 20 years and more. While several test rigs for pitch bearings are in operation, only very limited data is publicly accessible. Oscillating bearings typically suffer from wear, and this work covers the results of wear endurance tests of full-scale pitch bearings. Four bearings were tested approximately 120 days each under realistic conditions which included dynamic loads in five degrees of freedom. The bearings did not show any or very limited signs of raceway wear, in contrast to results of small-scale tests and short-term tests under worst-case conditions.
A General Method For The Diagnosis Of Wind Turbine Systematic Yaw Error Based Solely On SCADA Data
Authors: Astolfi D , Pasetti M , Lombardi A , Terzi L , Girard N , Poncet P , Masson J , Dieudegard T , Castellani F 
Published: June 2024

Abstract
Accurate diagnosis of systematic errors affecting wind turbine operation is fundamental to maximize the energy capture. Based on this, this work deals with the systematic yaw error, occurring when the wind vane sensor is incorrectly aligned with the rotor shaft. The objective is formulating a method for individuating the presence and estimating the amount of systematic yaw error, based solely on Supervisory Control And Data Acquisition (SCADA) data analysis. The state of the art is based on inferring the presence of the static yaw error by detecting an under-performance. Therefore, there is a gap as regards the estimation of how much the static yaw error really is. The proposed method introduces major methodological novelties for tackling such issue. Indeed, nacelle wind speed measurements are used in this work because the presence of the systematic yaw error has a detectable effect on them and they are not influenced by the control system. Furthermore, this work is the first in the literature employing measurements collected with wind turbine stopped, which allows circumventing the presence of the assembly angle induced by the rotor rotation. The proposed method is based on the idea that, if two nacelle anemometers are present, the ratio between the two wind speed measurements should change in presence of a static yaw error. Through a test case discussion, it is shown that with the proposed method it is possible to estimate the static yaw error as reliably as with LiDAR measurements.
Design of a robotic platform for hybrid wind tunnel experiments of floating wind farms
Authors: Fontanella A. , Palombini G. , Piffer A. , Giberti H. , Belloli M. 
Published: June 2024

Abstract
Wind tunnel experiments incorporating factors like realistic ambient wind conditions, merging of multiple wakes, and active wake controls are needed to understand and improve modeling of floating wind farms. A key technology for this kind of experiments is the robotic system emulating the wind turbine motion. This article addresses the design of a robotic platform with three degrees-of-freedom (surge, pitch, and yaw) specifically tailored for wind tunnel experiments on floating wind farms. This robotic system aims to accurately reproduce the motion spectrum of floating wind turbines of 10-22MW and to simulate rotor-atmospheric wind interactions. The robotic platform has a compact design to be positioned in multiple units inside the wind tunnel and avoid disturbing the wake of the wind turbine on top of it. To achieve these goals, the wind turbine is partially integrated in the robotic platform that employs a parallel kinematic scheme and has all actuators close to the wind tunnel floor.
Quality assessment of the GPM IMERG product for lifetime prediction of turbine blades in complex terrain
Authors: Dimitriadou Krystallia , Hasager Charlotte Bay , Nouqueret Elena Cantero , Hannesdóttir Ásta 
Published: June 2024

Abstract
Wind turbine blades may suffer leading edge erosion when rain hits the blades extremely fast, resulting in blade damage that will negatively impact power production. Since wind turbines are growing in size, this translates into higher tip speeds when the blades rotate and, therefore, are more prone to erosion. Wind turbines in mountainous terrain may also suffer erosion due to the high winds and precipitation rates. Therefore, it becomes important to estimate blade lifetimes in wind farm sites with terrain complexity. Blade lifetime prediction models utilize a time series of rainfall intensity, wind speeds, and a turbine-specific tip speed curve. In our study, we assess the quality of the Integrated Multi-satellitE Retrievals for GPM (IMERG) final product in a blade lifetime prediction model for a mountainous area during the period 2015-2020. We first compare the IMERG rainfall intensities against in situ observations at 28 stations in Navarra in Northern Spain. We find that the two datasets are closer to agreement when the rainfall intensities are aggregated in monthly rather than 30-minute temporal scales with correlation coefficients between 0.74 - 0.93. We calculate the average annual rainfall in the period, and we find that IMERG over(under)estimates precipitation in 15 (8) stations, in line with previous studies that have pinpointed the limitations of IMERG in complex terrain. We then input the 30-minute IMERG, in situ rainfall intensities, and the 30-minute New European Wind Atlas (NEWA) wind speeds, extracted at each station location and interpolated at 119 m height, into a blade lifetime model. Our results indicate blade lifetimes of 6-17 years in 13 stations, with the in situ data to provide, on average, longer estimates than the IMERG product. Despite the limitations, we conclude that the satellite-based precipitation from IMERG may become a useful dataset for the lifetime estimation of wind turbine blades in complex terrain, with calibration and adjustments of the IMERG data.
Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations
Authors: Méndez Beatriz , Saenz Ernesto , Pires Óscar , Cantero Elena , Bech Joan , Polls Francesc , Peinó Eric , Udina Mireia , Garcia-Benadí Albert 
Published: June 2024

Abstract
Precipitation has an effect on wind power at several levels. It affects the wind current, blade status, wake development and power production. Power production is affected by the harmful effect of precipitation on the blades eroding its surface and altering their aerodynamic performance. In the past decades, wind has been characterized using different techniques, but less effort has been devoted to precipitation measurement. In this work, the results of an experimental campaign performed at a high altitude complex terrain site to characterize precipitation using high resolution observations are presented. The campaign, carried out at CENER’s experimental wind farm (Alaiz) during 2023 within the framework of the Horizon Europe AIRE project, lasted nine months and different precipitation types (rain, snow, graupel) were recorded using a Micro Rain Radar (MRR), a Parsivel disdrometer and a rain gauge co-located with an instrumented wind mast with anemometers and wind vanes at different heights. Two case studies are selected to illustrate the wide range of variability found in precipitation conditions, particularly during the cool season. Precipitation characterization is very challenging at high temporal resolution, making necessary measurement campaigns with different precipitation equipment to optimize their performance and optimise its calibration. The study of precipitation profiles with MRR will support the study of precipitation impingement on wind turbine blades responsible of blade erosion. Moreover, these measurements will contribute to create the link between in-field wind farm data, laboratory experiments in rain erosion test rig and blade damage models necessary to improve wind turbine and wind farm design and operation.
Uncertainty and bias on velocities determined from an arc-scanning lidar
Authors: Mann Jakob , Hannesdóttir Ásta , Coimbra Isadora 
Published: June 2024

Abstract
Accurate determination of wind speed offshore is important for the progression of offshore wind energy. Arc-scanning lidars offer precise measurements of both wind speed and direction. They can be placed on a fixed footing, such as a transition piece of a fixed-bottom wind turbine, or on the coast. However, the procedure to derive the wind vector relies on the assumption of homogeneous flow, i.e., that the wind vector is constant along the scanning arc. In this study, we derive a theoretical expression for the wind speed bias due to inhomogeneity in the mean flow. We show that inhomogeneity in the flow will mostly affect the wind component tangential to the arc. The dominating term in the bias equation is equal to the range gate distance times the gradient of the wind speed away from the lidar in the direction along the arc, i.e. crudely, how fast the wind component away from the lidar changes with the scan angle. Atmospheric simulations using the Weather Research and Forecast (WRF) model of flow near mountainous coasts (Madeira Island), where the wind gradients are supposed to be largest, are used to estimate the gradient and, thereby, the bias in a real case. Errors in special situations exceed 50%.
The impact of blockage and wakes on seven power performance tests conducted at two wind farms
Authors: Bleeg J , Vishwakarma P , Del Hoyo M , Simmons L 
Published: June 2024

Abstract
Blockage and wakes can potentially influence wind turbine power performance measurements (PPM), distorting the measured power curve relative to the true performance of the test turbine. In this study, we take a previously proposed method to correct for the impact of blockage and wakes and test it on seven PPM conducted in simple terrain at two wind farms. Each PPM was completed according to the prevailing standard from the International Electrotechnical Commission. The correction factors derive from Reynolds-Averaged Navier-Stokes simulations of the two wind farms, and we apply these factors to the measured wind speed data on a record-by-record basis. The wind speed corrections clearly reduce variability in the measured power coefficients for the three PPM affected by long-distance wakes. The corrections do not reduce performance variability within the other four PPM. They also do not reduce differences in measured performance between the turbines at each wind farm, though numerical site calibrations do. Given these results, and considering uncertainty in the measurements, including confounding influences like terrain and turbine differences, validation against many more PPM is likely needed to adequately assess the reliability and utility of the correction method.