Study on Improvement of Lightning Damage Detection Model for Wind Turbine Blade

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.

Keywords

• anomaly detection
• gaussian mixture model
• lightning detection system
• lightning protection
• SCADA
• wind turbine