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WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
DeepOWT v2.25.1: An updated and improved global offshore wind turbine dataset until 2025Q1
<p>DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale. It is derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive. DeepOWT provides OWT locations along with their quarterly deployment stages from 2016Q1 until 2025Q1. It differentiates between platforms under construction, OWTs which are readily deployed and offshore wind farm substations, such as transformer stations.<br><br>The dataset continues the work of <a href="https://essd.copernicus.org/articles/14/4251/2022/">10.5194/essd-14-4251-2022</a>.</p> <p>File metadata</p> <table> <tbody> <tr> <th>File</th> <th>Time</th> <th>Geometry</th> <th>Spatial extent</th> </tr> <tr> <td>DeepOWT.geojson (Dataset)</td> <td>2016Q1-2025Q1</td> <td>points</td> <td>Global</td> </tr> <tr> <td>gt_2021Q2_nsb.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_2021Q2_ecs.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>East China Sea</td> </tr> <tr> <td>gt_2021Q2_vtn.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>Southeast Vietnamese Coast</td> </tr> <tr> <td>gt_nsb_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>East China Sea</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>Southeast Vietnamese Coast</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th>Used semantic label</th> </tr> </thead> <tbody> <tr> <td>open sea</td> </tr> <tr> <td>under construction</td> </tr> <tr> <td>offshore wind turbine</td> </tr> <tr> <td>offshore wind farm substation</td> </tr> </tbody> </table> <p> </p>
Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database
<p>This dataset contains the data measured during the <strong>HELOFOW </strong>model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files.</p> <p>The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot. </p> <p>As stated in the reference paper (Leroy et al., <em>Ocean Engineering</em>, 2022):</p> <p>"As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."</p> <p> </p> <p>This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund). <br><br>HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>. </p>
Oscillations of Offshore Wind Turbines undergoing Installation I: Raw Measurements
<p><strong>Overview</strong></p> <p>This repository contains data from an offshore measurement campaign conducted during the installation of the offshore wind farm trianel wind farm borkum II (https://www.trianel-borkumzwei.de/). The wind farm consists of 32 Senvion 6XM152 turbines. The installation took place between August 2019 and May 2020.</p> <p>An offshore wind turbine undergoing installation is interesting from a research point of view for several reasons:</p> <ul> <li>Simple geometry: turbine foundation and tower are both rotationally symmetric steel tubes. Rotational symmetry also leads to (approximate) isotropical structural characteristics in the plane normal to tower and foundation.</li> <li>High Reynolds number flow: Assuming a tower diameter of 6 m, and average wind speeds ranging from 5 m/s to 12 m/s under installation conditions, Reynolds numbers range from 4.5 million to 10.5 million.</li> <li>Wave loading under full-scale conditions.</li> <li>Practical relevance to improving the competetivity of offshore wind.</li> </ul> <p>For fluid mechanics, closely monitoring offshore wind turbines under wind and wave loading thus compares to a full-scale experiment. Monitoring 32 turbines undergoing installation thus enables the measurement a broad spectrum of different states.</p> <p>The investigation into the data is ongoing, questions and contributions are welcome. The current data release still does not include all data. The dataset will thus be updated again in the future with more data to come. First analytic results can be found here:</p> <p>Sander, A, Haselsteiner, AF, Barat, K, Janssen, M, Oelker, S, Ohlendorf, J, & Thoben, K. "Relative Motion During Single Blade Installation: Measurements From the North Sea." Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. Volume 9: Ocean Renewable Energy. Virtual, Online. August 3–7, 2020. V009T09A069. ASME. <https://doi.org/10.1115/OMAE2020-18935></p> <p>Sander, A, Meinhardt, C & Thoben, KD. "Monitoring of Offshore Wind Turbines under Wind and Wave Loading during Installation" Proceedings of the EuroDyn 2020 XI International Conference on Structural Dynamics. Volume 1. Virtual, Online. November 23-26, 2020. <https://generalconferencefiles.s3-eu-west-1.amazonaws.com/eurodyn_2020_ebook_procedings_vol1.pdf></p> <p>Recordings of the conference presentations are available on youtube:</p> <ul> <li>OMAE20: https://www.youtube.com/watch?v=QcAwdv6Z4e4</li> <li>EURODYN20: https://www.youtube.com/watch?v=iL-jAe0luTw</li> </ul> <p><strong>Physical Background</strong></p> <ol> <li>An offshore wind turbine under installation conditions can be simplified as a cantilevered beam (circular cross-section, rotationally symmetric wall thickness) with an eccentric mass (nacelle with generator) vibrating transversally (fore-aft and side-side in the reference system of the nacelle) under wind and wave loads.</li> <li>Both wind and wave loads are stochastic and are described using statistical models.</li> <li>Wave loads are a function of the sea state. For a sea state, the most important parameters are significant wave heigh H_m0 and Wave peak period T_P. To a lesser extend, wave direction, zero upcrossing period and maximum wave height are also important. Different statistical models can be used to describe the sea state and the relationship between significant wave heigh H_m0 and wave peak period. Most prominent in the North Sea is the JONSWAP spectrum.</li> <li>Wind loads are depending on wind speed, wind direction, shear factor and turbulence intensity. Different statistical models are available to describe the wind spectrum.</li> <li>Wind and wave loads trigger a structural response of the turbine. The structural response depends on the loading spectrum as well as the transfer function. Furthermore, the structural response is strongly depending on the damping and elasticity of the structure. In turbines, damping is typically very low (~ 0.5 - 1.5 %).</li> <li>The response is dominated by the first Eigenfrequency of the turbine. As the turbine is assumed to be rotationally symmetric, the fore-aft and side-side mode are extremely close together if not indistinguishable [1].</li> <li>The response has the characteristics of a narrow-band random vibration. A narrow-band random vibration is characterized by being dominated by a single, narrow frequency peak (here: first Eigenfrequency). The amplitude envelope follows a Rayleigh distribution and the phase angle is equally distributed between 0 and 2 pi.</li> <li>If viewed from above, the structural response describes a closed curve (orbit) which can be characterized by it shape (eccentricity), mean amplitude and direction. Mathematically speaking, this is a lissajous-figure, where the time series from one response direction is plotted as a function of the time series of the second response direction.</li> </ol> <p>[1]: under installation condition</p> <p><strong>Experimental Setup</strong></p> <p>Several locations were used to record data during the installation of the wind farm. They are listed in the following table:</p> <ul> <li>helihoist-{1,2}: data recorded from the helicopter hoisting platform atop the turbine nacelle. For most installations, two sensor boxes were deployed to ensure data availability.</li> <li>tp: Measurements from the transition piece</li> <li>sbitroot: Measurements from the blade lifting yoke's blade root side. The Z-axis is aligned to the blade main axis, X-Axis is perpendicular.</li> <li>sbittip: Measurements from the tip side of the blade lifting yoke. Z-axis aligned with the blade main axis, X-axis perpendicular</li> <li>damper: Measurements from the tuned mass damper used during single blade installation</li> <li>towertop: measurements from inside the turbine tower at the upper lift plattform</li> <li>towertransfer: measurements from atop the towers during sail out from the base harbour to the installation site </li> </ul> <p><strong>Organization of data</strong></p> <p>For each turbine installation, a separate folder can be found, e.g. turbine-01 for the first and turbine-16 for the 16th turbine. Turbine numbering follows the order of installation.</p> <p>Different data sources are organized in subfolders for each turbine dataset. Unfortunately, not every data source is available for each turbine. Data sources are roughly sorted into categories. The following table lists these categories:</p> <ul> <li>location / tom : data from custom build sensor boxes. Data includes acceleration, angular acceleration, magnetic field, gnss recording and rough estimates of the eulerian angles.</li> <li>waves / wmb-sued : Sea state statistics for the installatin period of the turbine.</li> <li>waves / fino : Sea state statistics from the german research platform FINO1 located approx. 6 km from the installation site.</li> <li>waves / waveradar : Sea state statstics, recorded by a wave rider wave laser. </li> <li>wind / lidar : high fidelity wind data recorded on the installation vessel during the installation of the wind farm.</li> <li>wind / scada : 10 min. mean wind statistisc recorded on wind turbines in the vicinity of the installation site. This data is used in case no LIDAR data is available.</li> <li>wind / anemometer : During some of the installations, anemometers were present on the installation vessel. These recordings are sorted into this sub-subfolder.</li> <li>wind / fino : Additonal wind statistics recorded by the FINO research station. Least recommended for investigations, as these recordings were taken approx. 6 km from the installation site.</li> </ul> <p>The zenodo data set includes 16 zip archives (for 16 turbines) as well as one zip archive including environmental data. The following lists the folder structure of the turbine-04.zip archive (with most of the data files removed for clarity).</p> <pre><code class="language-bash">└── turbines ├── turbine-04 │ ├── helihoist-1 │ │ └── tom │ │ └── clean │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-27-17_2019-09-01-11-54-00.csv │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-54-00_2019-09-01-12-20-44.csv │ ├── sbitroot │ │ └── tom │ │ └── clean │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-06-48-53_2019-09-07-07-17-16.csv │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-07-17-16_2019-09-07-07-45-59.csv │ ├── towertop │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-18-55-52_2000-01-06-20-30-10.csv │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-20-30-11_2000-01-06-22-04-31.csv │ ├── towertransfer │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-03-11-53_2019-08-31-04-00-09.csv │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-04-00-10_2019-08-31-04-48-19.csv │ └── tp │ └── tom │ └── clean │ ├── turbine-04_tp_tom_clean_2019-08-31-18-34-45_2019-08-31-19-07-52.csv │ ├── turbine-04_tp_tom_clean_2019-08-31-19-08-00_2019-08-31-19-40-43.csv </code></pre> <p>The following list the contents of the environment.zip archive. Note that again most of the data files have been removed for clarity. </p> <pre><code class="language-bash">└── environment ├── waves │ └── wmb-sued │ ├── wmb-sued_2019-08-15.csv │ ├── wmb-sued_2019-08-16.csv │ ├── wmb-sued_2019-08-17.csv └── wind └── lidar ├── lidar_2019-08-03.csv ├── lidar_2019-08-04.csv ├── lidar_2019-08-05.csv </code></pre> <p><strong>TOM data description</strong></p> <p>The abbreviation <strong>TOM</strong> referes to <em>Tower Oscillation Measurement</em> and the data that was acquired using a specific set of sensor boxes built by university of Bremen for this specific purpose. These Sensor Boxes were initially designed to measure accelerations and GPS tracks of offshore wind turbine towers undergoing installation. During the installation of the wind farm Trianel Windpark Borkum II they were subsequentially used to track the complete installation with a focus on single blade installation</p> <p>Data from the TOM devices comes as CSV files. The firmware of the boxes was designed, such that data was written into 10 MB sized txt files instead of one large txt file in order to circumvent data corruption due to power loss. However, this leads to a few milliseconds of missing data between log files. Additionally, jitter due to IO-Operations is present in the data as well and data should under all circumstance be resampled befor further analysis.</p> <p>Parameters provided by the TOM devices are listed in the following:</p> <ul> <li>epoch : machine readable time stamp based on the unix epoch in UTC [s] </li> <li>runtime : time since last boot of the tom device [ms] </li> <li>latitude : Degrees latitude in decimal writing. For Trianel: [degree due North] </li> <li>longitude : Degrees longitude in decimal writing. For Trianel: [degree due East] </li> <li>elevation : elevation above mean sea level [m] </li> <li>rot_{x,y,z} : Rotational acceleration around the three cartesian axis {x,y,z} of the TOM box. [degree / s^2] </li> <li>acc_{x,y,z} : linear acceleration in each of the three cartesian axis {x,y,z} in the reference system of the TOM box [m/s^2] </li> <li>mag_{x,y,z} : magnetic field strength in each of the local cartesian TOM box axis {x,y,z} [micro-Tesla] </li> <li>roll : eulerian roll angle (angle around the x Axis of the tom box) [degree] </li> <li>pitch : eulerian pitch angle (angle around the y Axis of the tom box) [degree] </li> <li>yaw : eulerian yaw angle (angle around the z Axis of the tom box) [degree] </li> </ul> <p><strong>LIDAR wind data description</strong></p> <p>A Leosphere WindCube LIDAR was mounted on the installation vessel <em>Taillevent</em> to record the atmospheric boundary layer during installation. The data recorded by the lidar was exported as csvs and provided by the vessel operator. The raw data includes the following parameters</p> <ul> <li>epoch : time stamp as a unix epoch in UTC [s]</li> <li>wind_speed_N : The wind speed at the N'th return level [m/s]</li> <li>wind_dir_N : Wind direction at the N'th return level in the vessels reference frame [degree]</li> <li>wind_dir_N_corr : Wind direction at the N'th return level due North [degree due North]</li> <li>heigh_N : The height of the lidar return level [m] </li> </ul> <p>Data is resampled to a 1 s return interval. </p> <p><strong>wave data description</strong></p> <p>Wave data was recorded using a waverider wave buoy (DWR-G). The wave rider buoy was located at 54 00' 238'' degree North and 6 26' 553'' degree East. In decimals: 54.0031096 North, 6.4425532 East. Based on the raw data, sea state statistics were derived with a return period of 30 minutes.</p> <p>The data comes as csv, column oriented text files. The first line entails parameter names and units and is commented out by a hashbang (unix commentary). The data is a combination of two different data files as provided by the buoy: \*.HIS Data Text File (History of Spectrum parameters) and \*.HIW Data Text File (History of Wave Statistics). This results in the two timestamps in the data file because history of wave statistics data is available immediately after each measurement time period, whereas history of spectrum parameters take approx. 5 minutes to calculate by the buoy and thus have a slightly later time stamp. For actual postprocessing purposes, a third timestamp rounded to full half hour is used. Parameters included in the data files are:</p> <ul> <li>epoch: number of seconds since 1st of January 1970 in UTC. Common time stamp format in computing. [s]</li> <li>Tp Tp := 1 / fp, peak period, the frequency at which S(f) is maximal [s]</li> <li> Dirp peak direction, the direction at f = fp [deg due North]</li> <li> Sprp peak spread, the directional spread at f = fp [s]</li> <li> Tz Tz := sqrt(m0 / m2), zero-upcross period [s]</li> <li> Hm0 Hm0 := 4*sqrt(m0), the significant waveheight [m]</li> <li> TI TI := sqrt(m[-2] / m0), integral period [s]</li> <li> T1 T1 := m0 / m1, mean period [s]</li> <li> Tc Tc := sqrt(m2 / m4), crest period [s]</li> <li> Tdw2 Tdw2 := sqrt(m[-1] / m1) [s]</li> <li> Tdw1 Tdw1 := sqrt(m[-1,2] / m0) [s]</li> <li> Tpc Tpc := m[-2] * m1 / m0 ^ 2, calculated peak period [s]</li> <li> nu nu := sqrt((T1 / Tz) ^ 2 - 1), band width parameter [-]</li> <li> eps eps := sqrt(1 - (Tc / Tz) ^ 2), bandwidth parameter [-]</li> <li> QP QP := 2 * m[1,2] / m0 ^ 2, Goda's peakedness parameter [-]</li> <li> Ss Ss :=2 * pi / g * Hs / Tz ^ 2, significant steepness [-]</li> <li> TRef TRef, reference temperature (25deg) [C]</li> <li> TSea TSea, sea surface temperature [C]</li> <li> Bat Bat, battery status (0..7) </li> <li> m[n] m[n] := Integral from f=0 to f=Inf over S(f) * f ^ n </li> <li> m[n,2] m[n,2] := Integral from f=0 to f=Inf over S(f) ^ 2 * f ^ n </li> <li> Percentage Percentage of data with no reception errors [%] </li> <li> Hmax Height of the highest wave [cm] </li> <li> Tmax Period of the highest wave) [s] </li> <li> H(1/10) Average height of 10% highest waves [cm] </li> <li> T(1/10) Average period of 10% highest waves [s] </li> <li> H(1/3) Average height of 33% highest waves [cm] </li> <li> T(1/3) Average period of 33% highest waves [s] </li> <li> Hav Average height of all waves [cm] </li> <li> Tav Average period of all waves) [s] </li> <li> Eps bandwidth parameter </li> <li> #Waves Number of waves</li> </ul> <p>Note: the m's are moments of the power spectral density S(f).</p> <p> </p> <p> </p>
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement
<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>
Data generated for study of simultaneous design of wind turbines and cable layout in offshore wind
<p>This set of files contains the results of the models proposed in the manuscript: "Pérez-Rúa, J.-A. and Cutululis, N. A.: A Framework for Simultaneous Design of Wind Turbines and Cable Layout in Offshore Wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-47, in review, 2021."</p>
Global offshore wind turbine analysis with Sentinel-1 - supplementary data
<p>Gloabl offshore wind turbine analysis with Sentinel-1 - supplementary data</p> <p>The files are supplementary data of the publication:</p> <p>Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications</p> <p>which is currently under review in the International Journal of Applied Earth Observation and Geoinformation</p> <p>supplementary_data_B_OWT_height_capacity.csv holds 50 pairs of offshore wind turbine hub heights and the corresponding installed capacities along with the offshore wind farm project name, the number of turbines of this wind farm, and the source the information originates from.</p> <p>supplementary_data_B_DeepOWT_1_21_2_plus.geojson is the extended version of the DeepOWT data set (https://zenodo.org/record/5933967) with all of the derived attributes in the respective publication e.g. OWT hub height and installed capacity.</p>
One year time series of relative electric (onshore and offshore) wind turbine power datasets
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric wind turbine (WT) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Flanders (onshore) and Belgium (offshore) as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric WT power was derived by dividing the measured electric WT power by the monitored peak electric WT power multiplied by 100 %.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
<p>The expanding use of wind farms as a source of renewable energy can impact bird populations due to collisions and other factors. Globally, seabirds are one of the avian taxonomic groups most threatened by anthropogenic disturbance; adequately assessing the potential impact of offshore wind farms (OWFs) is important for developing strategies to avoid or minimize harm to their populations. We estimated avoidance rates of OWFs — the degree to which birds show reduced utilization of OWF areas — by Sandwich Terns <em>Thalasseus sandvicensis</em> at two breeding colonies in western Europe: Scolt Head (United Kingdom) and De Putten (the Netherlands). We modeled GPS tracking data using integrated Step Selection Functions (iSSFs) to estimate the relative selection of habitats at the scale of time between successive GPS relocations – in our case 10 minutes, in which terns traveled ca. 2 km on average. The foraging ranges of birds from each colony overlapped with multiple OWFs. iSSFs considered distance from the colony and habitat characteristics (water depth and sediment grain size) and movement characteristics. Macro-avoidance rates, where 1 means complete avoidance, were estimated at 0.54 (95% CrI = 0.35, 0.7) for birds originating from Scolt Head and 0.41 (95% CrI = 0.21, 0.56) for those from De Putten. Estimates for individual OWFs also indicated avoidance but were associated with considerable uncertainty. Our results were inconclusive with regard to the behavioral response to the areas directly surrounding OWFs (within 1.5 km); estimates suggested indifference and avoidance and were associated with large uncertainty. Avoidance rate of OWFs significantly increased with turbine density, suggesting OWF design may help to reduce the impact of OWFs on Sandwich Terns. The partial avoidance of OWFs by Sandwich Terns implies that the species will experience risks of collision and habitat loss due to OWFs constructed within their foraging ranges.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
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Data from: The design of an intelligent fault-tolerant control for floating offshore wind turbine with blade faults
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Site-specific Design Load Cases for floating offshore wind turbine applications I : Historical data
<p>This document includes a brief description of the <a href="https://leopard.tu-braunschweig.de/receive/dbbs_mods_00077703" target="_blank" rel="noopener">first database</a> on the site-specific Design Load Cases (DLCs) based on historical metocean data. The dataset includes metocean data, statistical analysis and site-specific DLCs across the three areas of study defined in the INF4INiTY project: Scottish Sea, Baltic Sea and Adriatic Sea. In addition to the dataset, this deliverable includes a Graphical User Interface (GUI) for the analysis of specific locations within these three areas and the generation of the site-specific DLCs.<br>The aim of this initial version of the database is to provide a first characterisation of the areas of interest in order to use the DLCs on the design of the different innovations planned in various work packages (WPs) INF4INiTY. As the project proceeds, the second database will extend the site-specific DLCs including forecasted data for different horizons and under diverse climate change scenarios.<br>The deliverable is divided into six brief sections describing the (i) the GUI, (ii) characteristics of the data, (iii) the three areas of study and technological requirements, (iv) historical metocean data, (v) site-specific statistical analysis and reporting, and (vi) site-specific DLCs.</p>
Figures and graphics used in "Optimizing Strength of Shared Anchors in an Array of Floating Offshore Wind Turbines"
<p>All figures and graphics used in tables are included in PDF format here. Instructions on how to reproduce the MATLAB-generated plots (or similar plots) are included in the README of the software package, also cited in the paper.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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