Enhancing energy availability based on explainable two-level clustering for root-cause diagnosis of production loss events in wind farms

The economic viability of wind energy is fundamentally linked to the maximization of availability and the minimization of production losses caused by unscheduled downtime. Therefore, a quick and accurate diagnosis of the root cause of failures is critical to restoring power generation efficiency. It is vital to analyze what caused failures in the operation of wind farms so that the necessary actions can be taken to get the wind turbine producing power again in the shortest possible time. In this paper, we propose to discover patterns that can help diagnose the root cause of a failure in one or several wind turbines. For this purpose, a specific definition of an incident as an energy production loss event is proposed, as well as a selection of variables that characterize such incidents. Addressing the limitations of supervised learning in label-scarce environments, an explainable methodology based on unsupervised learning is carried out at two levels: a first level, to determine different groups of incidents through clustering, and a second level to obtain groups of incidents sharing alarms, called subclusters. Finally, a rule summarizing each subcluster is provided to explain the patterns that identify each root cause, offering rule-based explainability that deep learning models lack. Two real-world datasets composed of sensor signal records together with alarms that occurred at wind farms located in Spain and Argentina were used to evaluate the model. Experimental results show a high diagnostic accuracy, which directly facilitates a reduction in average repair time and an improvement in annual energy production.