Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis

This work explores the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behavior of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behavior. This study is centered on a small WF of five WTs and uses the observation that the same signals from different WTs in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, averaged signals from the SCADA system are used and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations are explored. The signals are encoded using the Discrete Cosine Transform, which reduces the problem’s dimensions. A hierarchical tree is built in each time window. Clusters are formed by pruning the tree using a threshold interpretable in terms of distance. It is unnecessary to work with an a priori known number of clusters. A protocol for enumerating the clusters based on the tree’s shape is then established, making it easier to follow the evolution of the clusters over time. The capability to automatically identify WTs whose signals differ from the group’s behavior can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.