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175 results for “wind turbine”

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zenodo32/100

Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"

<p>These are the raw plot files from the publication &quot;Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history&quot;.</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>

opencc-by-4.0May 2022View details →
zenodo32/100

MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network

<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

MATLAB codes for paper: UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

<p>These codes belong to the following paper. Please cite our work.</p> <p>Berghout T, Benbouzid M. UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings.&nbsp;<em>Electronics</em>. 2024; 13(12):2419. https://doi.org/10.3390/electronics13122419</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Additional material for the Wind Energy manuscript "An engineering approach for the estimation of slewing bearing stiffness in Wind Turbine Generators"

<p>This documment compiles the additional material for the manuscript &ldquo;An engineering approach for the estimation of slewing bearing stiffness in Wind Turbine Generators&rdquo;. The document shows the results for all the considered design points, comparing the the proposed approach with the Finite Element model.</p>

opencc-by-4.0Sep 2018View details →
zenodo32/100

NETTUNO Experiment 1 – Wake Development in Floating Wind Turbines

<p>This dataset, collected as part of the NETTUNO research project, includes measurements from wind tunnel tests on a 1:75 scale model wind turbine. The primary focus of the experiment was to analyze how platform motion in different directions affects the aerodynamics of the wind turbine rotor and the development of its wake. The dataset consists of two components:<br>&bull; &nbsp; &nbsp;Measurements of the aerodynamic forces and moments experienced by the rotor under various platform motion conditions.<br>&bull; &nbsp; &nbsp;Wind speed measurements collected at multiple downstream distances from the rotor, capturing the velocity profiles and turbulence characteristics within the turbine's wake.<br>The dataset is designed to serve as a comprehensive benchmark for researchers and engineers working on floating wind turbine aerodynamics, offering valuable insights for optimizing wind farm layouts and for developing simulation tools.</p>

opencc-by-4.0Oct 2024View details →
dryad32/100

High-resolution modelling of uplift landscapes can inform micro-siting of wind turbines for soaring raptors

<p>Collision risk of soaring birds is partly associated with updrafts to which they are attracted. To identify risk-enhancing landscape features, a micro-siting tool was developed to model orographic and thermal updraft velocities from high-resolution remote sensing data. The tool was applied to the island of Hitra, and validated using GPS-tracked white-tailed eagles (<i>Haliaeetus albicilla</i>). Resource selection functions predicted that eagles preferred ridges with high orographic uplift, especially at flight altitudes within the rotor-swept zone (40-110 m). Flight activity was negatively associated with the widely distributed areas with high thermal uplift at lower flight altitudes (&lt;110 m). Both the existing wind-power plant and planned extension are placed at locations rendering maximum orographic updraft velocities around the minimum sink rate for white-tailed eagles (0.75 m/s) but slightly higher thermal updraft velocities. The tool can contribute to improved micro-siting of wind turbines to reduce environmental impacts, especially for soaring raptors.</p>

opencc-zeroJul 2021View details →
zenodo32/100

The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"

<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article:&nbsp;&quot;A computationally efficient engineering aerodynamic model for swept&nbsp;wind turbine blades&quot;, submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</p>

opencc-by-3.0Aug 2021View details →
zenodo32/100

Drivers of bat activity at wind turbines advocate for mitigating bat exposure using multicriteria algorithm-based curtailment

<p>data used for the paper</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Multivariate prediction on wake-affected wind turbines using graph neural networks (Eurodyn) database

<p>Database consisting of graphs generated using randomized layouts and PyWake simulations used in&nbsp;&#39;<em>Multivariate prediction on wake-affected wind turbines using graph neural networks</em>&#39;, contribution to Eurodyn 2023.&nbsp;</p>

opencc-by-4.0Mar 2023View details →
zenodo32/100

Dataset for An efficient multivariate deep learning model for monitoring mooring line tension of floating wind turbine

<p>Reference data needed for mooring line tensions prediction of a 15 MW&nbsp;wind turbine.<br> <br> This dataset contains OpenFAST outputfiles for different design load cases used in the paper.</p> <p>These data files&nbsp;are designed to be used together with the python code, which&nbsp;is available publicly on https://github.com/ramisetti/ 3SDLMooringPrediction</p>

opencc-by-4.0Sep 2023View details →
zenodo32/100

NREL 5MW wind turbine blade and tower degradation

<p><strong>Problem</strong></p><p>➔Track the degradation of one rotor blade and tower base over over as 10 months period under wind inflow and operational uncertainties</p><p><strong>Scenario</strong></p><p>➔Continuous wind turbine blade and tower stiffness degradation, emulating blade root delamination resulting in dynamic instability, leading to the tower base excess fatigue.</p><p><strong>Simulation environment and setup in FAST v8 :</strong></p><p>➔In the blade input file: modify flap and edge stiffnesses in damage regions, blade root (only 1 in 3 blades is affected). Assumed equal degradation in both edgewise and flapwise directions</p><p>➔In the blade input file: modify mode shapes coefficients (1st flap, 2nd flap and 1st edge modes)</p><p>➔In the tower input file: modify FA and SS stiffnesses in damage regions, tower base</p><p>➔In the tower input file: modify mode shapes coefficients (1st &amp; 2nd FA and 1st &amp; 2nd SS modes)</p><p>➔Note that the tower degradation only "shows up" in the last 2 monitoring periods</p><p>➔In the turbulence input file: modify wind speed, tuburlence intensity, shear exponent, horizontal and vertical inflow angles.</p><p>➔Further complication by assuming that the average input environmental conditions are not stationary over the monitoring period, emulating seasonal variations.</p><p>➔This means 120 environmental samples (and consequenly input wind field time series) are sampled from different distributions each period during the degradation process</p>

opencc-by-4.0Oct 2023View details →
ClinicalTrials.gov32/100

Prevalence of Skin Sensitization and Dermatitis Among Epoxy-exposed Workers in the Wind Turbine Industry.

ClinicalTrials.gov study NCT05891743. IPD Sharing: Not stated. Countries: 1. Publications: 5.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Data from: Out of sight of wind turbines – reindeer response to wind farms in operation

Open the record for dataset details and reuse information.

publicAug 2019View details →
dryad32/100

Data from: The design of an intelligent fault-tolerant control for floating offshore wind turbine with blade faults

Open the record for dataset details and reuse information.

publicOct 2024View details →
dryad32/100

Data from: Onshore industrial wind turbine locations for the United States up to March 2014

Open the record for dataset details and reuse information.

publicOct 2016View details →
dryad32/100

Data from: "Genome-wide microsatellite marker development from next-generation sequencing of two non-model bat species impacted by wind turbine mortality: Lasiurus borealis and L. cinereus (Vespertilionidae)" in Genomic Resources Notes accepted 1 October 2013 to 30 November 2013

Open the record for dataset details and reuse information.

publicJan 2014View details →
dryad32/100

Limitations of acoustic monitoring at wind turbines to evaluate fatality risk of bats

Open the record for dataset details and reuse information.

publicFeb 2021View details →
dryad32/100

High-resolution modelling of uplift landscapes can inform micro-siting of wind turbines for soaring raptors

Open the record for dataset details and reuse information.

publicJul 2021View details →
dryad32/100

Data from: Collision sensitive niche profile of the worst affected bird-groups at wind turbine structures in the federal state of Brandenburg, Germany

Open the record for dataset details and reuse information.

publicJan 2019View details →
dryad32/100

Data from: Automated curtailment of wind turbines reduces eagle fatalities

Open the record for dataset details and reuse information.

publicDec 2020View details →

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International Brain Laboratory public data

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