SCADA dataset of a 2 MW SIEMENS wind turbine drivetrain located at a wind farm on the Baltic Sea coast in northern Poland

This dataset contains Supervisory Control and Data Acquisition (SCADA) measurements from a 2 MW Siemens wind turbine drivetrain located at a wind farm on the Baltic Sea coast in northern Poland. The data were extracted to investigate whether early indicators of a gearbox fault could be detected using data-driven analysis.

The monitoring period spans 30 days, from November 1, 2012 (00:00) to November 30, 2012 (23:50). Operational parameters were recorded at 10-minute intervals, resulting in 4,320 time-series samples for each parameter.

The dataset includes twelve process parameters describing the turbine’s operational condition, grouped into rotational dynamics, electrical power generation, and thermal conditions. Rotational parameters include wind speed, rotor speed, and generator speed. Electrical parameters include active power, generated power, reactive power, reactive power delivered, generator voltage, and generator current, representing the turbine’s power generation and load conditions. Thermal parameters include gearbox bearing temperature and two generator temperature sensors, indicating the thermal state of key components.

During operation, a gearbox bearing failure occurred and was recorded on November 9, 2012 at 13:00 (sample 1232). The dataset therefore contains both normal operational data and data preceding the fault event. In the related study, generator speed and gearbox bearing temperature were used to validate a stationarity-based anomaly detection method.

SCADA measurements represent 10-minute averaged values, typical for wind turbine monitoring systems. The dataset contains no missing or corrupted values, making it suitable for research on condition monitoring, anomaly detection, time-series analysis, and predictive maintenance of wind turbines.

Related published papers:
1) P.B. Dao, W.J. Staszewski, T. Barszcz, and T. Uhl, “Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data,” Renewable Energy, vol. 116, part B, pp. 107–122, 2018.
2) P.B. Dao, “A CUSUM-based approach for condition monitoring and fault diagnosis of wind turbines,” Energies, vol. 14, no. 11, 3236, 2021.
3) P.B. Dao, “Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data,” Renewable Energy, vol. 185, pp. 641–654, 2022.
4) P.B. Dao, “On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines,” Applied Energy, vol. 318, 119209, 2022.
5) P.B. Dao, T. Barszcz, and W.J. Staszewski, “Anomaly detection of wind turbines based on stationarity analysis of SCADA data,” Renewable Energy, vol. 232, 121076, 2024.
6) K. Zolna, P.B. Dao, W.J. Staszewski, and T. Barszcz, “Nonlinear cointegration approach for condition monitoring of wind turbines,” Mathematical Problems in Engineering, vol. 2015, Article ID 978156, 11 pages, 2015.

本数据集包含位于波兰北部波罗的海沿岸风电场的一台2兆瓦西门子风电齿轮箱传动系统的监控与数据采集（Supervisory Control and Data Acquisition, SCADA）测量数据。本次数据提取旨在探究能否通过数据驱动分析方法检测出齿轮箱故障的早期征兆。

本次监测周期共计30天，覆盖2012年11月1日00:00至2012年11月30日23:50。运行参数以10分钟为间隔进行记录，单参数共生成4320个时序样本。

本数据集包含12项描述风电机组运行状态的过程参数，分为旋转动力学、发电性能及热工况三大类别。其中旋转参数涵盖风速、转子转速与发电机转速；电气参数包括有功功率、输出功率、无功功率、馈入无功功率、发电机电压与发电机电流，用于表征风电机组的发电与负载工况；热工况参数则包含齿轮箱轴承温度与两路发电机温度传感器数据，用以反映关键部件的热状态。

运行期间，该风电机组于2012年11月9日13:00发生齿轮箱轴承故障（对应第1232号样本）。因此本数据集同时包含正常运行数据与故障发生前的历史数据。在相关研究中，研究人员以发电机转速与齿轮箱轴承温度为指标，验证了基于平稳性分析的异常检测方法。

本次SCADA测量数据为10分钟平均值，符合风电机组监测系统的常规采集标准。本数据集无缺失或损坏的样本，适用于风电机组状态监测、异常检测、时序分析及预测性维护相关研究。

已发表相关研究论文如下：
1) P.B. Dao、W.J. Staszewski、T. Barszcz及T. Uhl，《基于SCADA数据协整分析的风电机组状态监测与故障检测》，《Renewable Energy》，第116卷B辑，第107–122页，2018年。
2) P.B. Dao，《基于CUSUM方法的风电机组状态监测与故障诊断》，《Energies》，第14卷第11期，第3236页，2021年。
3) P.B. Dao，《基于SCADA数据结构突变检测的风电机组状态监测与故障诊断》，《Renewable Energy》，第185卷，第641–654页，2022年。
4) P.B. Dao，《基于Wilcoxon秩和检验的风电机组状态监测与故障检测》，《Applied Energy》，第318卷，第119209页，2022年。
5) P.B. Dao、T. Barszcz及W.J. Staszewski，《基于SCADA数据平稳性分析的风电机组异常检测》，《Renewable Energy》，第232卷，第121076页，2024年。
6) K. Zolna、P.B. Dao、W.J. Staszewski及T. Barszcz，《风电机组状态监测的非线性协整方法》，《Mathematical Problems in Engineering》，2015年卷，文章编号978156，共11页，2015年。