No 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.

No abstract

There have been many reports of damage to wind turbine blades caused by lightning strikes in Japan. In some of these cases, the blades struck by lightning continue to rotate, causing more serious secondary damage. To prevent such accidents, it is a requirement that a lightning detection system is installed on the wind turbine in areas where winter lightning occurs in Japan. This immediately stops the wind turbine if the system detects a lightning strike. Normally, these wind turbines are restarted after confirming soundness of the blade through visual inspection. However, it is often difficult to confirm the soundness of the blade visually for reasons such as bad weather. This process prolongs the time taken to restart, and it is one of the causes that reduces the availability of the wind turbines. In this research, we constructed a damage detection model for wind turbine blades using machine learning based on SCADA system data and, thereby, considered whether the technology automatically confirms the soundness of wind turbine blades.