Description of the SCADA dataset of an onshore La Houte Bourne wind farm in Villeneuve-d'Ascq, France 核心 · 已核验
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1. Dataset Overview
This dataset contains Supervisory Control and Data Acquisition (SCADA) measurements collected from an onshore wind farm located in Villeneuve-d’Ascq, France, over eight years (from 1 January 2013 to 31 December 2020). The wind farm has four wind turbines of the MM82 model, manufactured by Senvion. Those wind turbines are labeled as R80736, R80721, R80711, and R80790. The rated power of each turbine is 2 MW. Each wind turbine at the wind farm has a nominal power of 2050 kW, a cut-in wind speed of 4 m/s, and a cut-out wind speed of 22 m/s. The blade length and hub height are 40 m and 80 m, respectively.
There were 34 process parameters measured at an interval of 10 min for each wind turbine (WT), and in total 1,057,868 samples were recorded. The dataset includes many parameters, such as wind speed, absolute wind direction, outdoor temperature, rotor bearing temperature, gearbox bearing temperature, generator bearing temperature, gearbox oil sump temperature, generator speed, generated power, torque, grid voltage, grid frequency, vane position, pitch angle, and more. For each parameter, the average value, standard deviation, maximum, and minimum values were collected at every interval.
During the period from 1 January 2013 to 31 December 2016, the gearbox bearing temperature of the R80721 wind turbine reached a maximum value of 84.12°C at the data sample 70,995. This unusually high temperature indicates a failure of the gearbox bearing associated with the high-speed shaft, which was recorded on 2 January 2015 at 08:50.
The data include operational measurements recorded from wind turbines and can be used for research in wind turbine performance analysis, condition monitoring, and fault detection methods.
The SCADA dataset for the La Haute Borne Wind Farm was previously available at the link: https://www.engie.com/en/activities/renewable-energies/wind-energy. However, the dataset is currently not accessible.
Data set format: CSV (semicolon-sepa
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- 落地页
- https://data.mendeley.com/datasets/vmyg4yp3s8
- 许可证
- CC-BY-4.0 (置信:verified_official)
- 发布年份
- 2026
- 发布方
- Mendeley Data
分发点
| mendeley_data | https://data.mendeley.com/datasets/vmyg4yp3s8 |
溯源(12 条)
| 来源链接: https://data.mendeley.com/datasets/vmyg4yp3s8 日期: 2026-07-21 |
| 来源链接: https://api.datacite.org/dois/10.17632/vmyg4yp3s8 日期: 2026-07-30 |
| 来源链接: https://api.datacite.org/dois/10.17632/vmyg4yp3s8 日期: 2026-07-30 |
| 日期: 2026-07-30 |
| 日期: 2026-07-31 |
| 日期: 2026-07-31 |
| 引文: 「The following steps can be followed to reproduce. i. Import the dataset file using a data analysis environment such as R, Python, or MATLAB. ii. Perform exploratory data analysis (EDA): Examine key operational variables such as wind speed, power output, rotor speed, generator speed, and temperature measurements. Visualization tools such as time-series plots and correlation matrices can help identify operational patterns. iii. Identify turbine operational states: Compare turbines under healthy and faulty conditions to understand variations in their operational behavior. iv. You can apply statistical monitoring methods using approaches such as cointegration analysis, the Augmented Dickey–Fuller (ADF) test, and the Wilcoxon rank-sum test, or other condition monitoring algorithms to evaluate turbine operational behavior. These techniques can help identify deviations from normal turbine behavior, and these methods are available at the link below for interested readers. Different methods, as shown in the references below [1 - 4], have been developed and published using the attached SCADA dataset, including the cleaning and preprocessing procedures applied to the dataset as discussed in reference [5]. Related published papers: 1. Dao, P. B., Barszcz, T., & Staszewski, W. J. (2024). Anomaly detection of wind turbines based on stationarity analysis of SCADA data. Renewable Energy, 232. https://doi.org/10.1016/j.renene.2024.121076 2. Dao, P. B. (2022). On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines. Applied Energy, 318. https://doi.org/10.1016/j.apenergy.2022.119209 3. Dao, P. B. (2023). On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data. Energies, 16(5). https://doi.org/10.3390/en16052352 4. Knes, P., & Dao, P. B. (2024). Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach. Energies, 17(20), 5055. https://doi.org/10.3390/en17205055 5. Kijanowski, K.; Barszcz, T.; Dao, P.B. A Cluster-Based Filtering Approach to SCADA Data Preprocessing for Wind Turbine Condition Monitoring and Fault Detection. Energies 2025, 18, 5954. https://doi.org/10.3390/en18225954」 来源链接: https://data.mendeley.com/public-api/datasets/vmyg4yp3s8 日期: 2026-09-02 |
| 来源链接: https://data.mendeley.com/public-api/datasets/vmyg4yp3s8 日期: 2026-09-02 |
| 日期: 2026-09-02 |
| 日期: 2026-09-02 |
| 来源链接: https://data.mendeley.com/public-api/datasets/vmyg4yp3s8 日期: 2026-09-02 |
| 日期: 2026-09-02 |