Anomaly Detection Using a SCADA Feature Extractor and Machine Learning to Detect Lightning Damage on Wind Turbine Blades

Abstract

Many wind turbines in Japan are damaged by lightning strikes. In particular, blades that are already damaged by lightning strikes are further damaged by continuous blade rotation. To prevent such additional damage, an emergency stop device that is triggered by lightning detection is required to be installed on wind turbines in winter lightning areas. Normally, the wind turbine restarts after soundness is confirmed by inspection. However, it is often difficult to inspect the turbine visually because of bad weather, and consequently, the wind turbine downtime is prolonged. This type of downtime is a reason for the reduced availability of wind turbines. Therefore, in this study, we consider technologies that would allow quick restart of wind turbines and improve availability, based on understanding the soundness of blades after a lightning strike, using a machine‐learning model based on supervisory control and data acquisition system data. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.