1. General description 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 and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see Fig1_Norther_locaction.png. 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. 1.1 Summary of the shared structural information 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 "Fig2_Sensor_Network.png" 1.2 Summary of the shared geotechnical information 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). 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., Undrained shear strength). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is: "epsilon50 [-]" represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample. 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). 1.3 Summary of the shared measurement data 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. The sensor network on NRT-WTG is illustrated in in Fig. 2, whereas a description of the sensor types is presented in Tab.1. 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. Data type Sensor type Fs (Hz) Level mLAT (m) Description Acceleration (g) Piezo-electric acc. sensor (ACC) 30 15, 69, 97 3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree. Strain (micro strain) Resistive strain gauge (SG) 30 14 6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree. Strain (micro strain) Fiber-Bragg Grating strain gauge (FBG) 100 -17, -19 2 FBGs per level at 165 and 255 degree respectively. Table 1. Description of sensor types. 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. 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. In conjunction to the sensor channels presented in Tab. 1, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in Tab 2. Parameter Unit Description Wind speed m/s Wind speed as recorded in the turbine SCADA Wind direction ° Wind direction relative to North (0°) as recorded in the turbine SCADA Yaw angle ° Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA Pitch angle ° Rotor blade pitch as recorded in the turbine SCADA Rotor speed rpm Rotor speed in rotations per minute as recorded in the turbine SCADA Power kW Active power of the turbine as recorded in the turbine SCADA Table 2. List of provided SCADA parameters A summary of the selected intervals and relevant corresponding scada parameters is given in Tab 3. Scenario T1 (UTC) T2 (UTC) Windspeed RPM Pitch Parked 03/07 01:30 03/07 03:30 < 4.5 m/s ~1 ~18 ° Rated 05/07 22:30 06/07 00:30 ~15 m/s 10.5 8.1° Table 3. Selected data intervals and relevant scada parameters 2. Included in this version 2.1 Version - 0.1.0 Relevant Design information can be found in: Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx" Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx" Acceleration, strain and scada data can be found in the following parquet files: Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz" Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz" 3. Importing parquet files To import the measurement data into Python it is recommended to use pandas: import pandas as pd# Read Parquet file with Pandas: relative_file_path = 'NRT-WTG_Parked.parquet.gz' data = pd.read_parquet(relative_file_path ) 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.