Modern utility-scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for analysis to improve operation and maintenance of turbines. We analyze high frequency SCADA-data from the Thanet offshore wind farm in the UK and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible a quantitative assessment of non-stationarity in mutual dependencies of different types of data. We show that a clustering algorithm applied to the correlation matrices reveals distinct correlation structures for different states. Looking first at only one and then at multiple turbines, the main dependence of these states is shown to be on wind speed. This is in accordance with known turbine control systems, which change the behavior of the turbine depending on the available wind speed. We model the boundary wind speeds separating the states based on the clustering solution. Our analysis shows that for high frequency data the control mechanisms of a turbine lead to detectable non-stationarity in the correlation matrix. The presented methodology allows accounting for this with an automated pre-processing by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the non-stationarity into account for an analysis.

翻译：现代商用大型风力发电机配备有监控与数据采集（SCADA）系统，该系统能够收集海量运行数据，可用于改进风机的运维分析。我们分析了英国Thanet海上风电场的SCADA高频数据，采用滑动时间窗口评估多种观测量的皮尔逊相关矩阵。这使我们能够定量评估不同类型数据相互依赖关系的非平稳性。研究表明，对相关矩阵应用聚类算法可揭示不同运行状态下的独特相关结构。通过先分析单台风机再扩展到多台风机，我们发现这些状态主要依赖于风速。这与已知的风机控制系统一致——该控制系统会根据可用风速改变风机运行特性。我们基于聚类结果建立了状态边界风速模型。分析表明：对于高频数据，风机的控制机制会在相关矩阵中产生可检测的非平稳性。所提出的方法能够通过自动预处理来应对这一现象——根据风速对新数据进行分类，并将其与对应运行状态进行比对，从而在分析中考虑非平稳性。