GMM-Based Lightning Damage Detection for Wind Turbines Under De-Rated Operation Using the Scaled Power Curve

Many countries are actively promoting the large-scale deployment of wind power generation, both onshore and offshore. However, damage to wind turbines caused by winter lightning has become a growing concern in Japan. Japan has made efforts since an early stage to establish legal frameworks for reducing lightning damage; nevertheless, lightning damage to wind turbines remains a problem that has not been completely eradicated. After a wind turbine has been struck by lightning, it is restarted only after its structural integrity has been verified; however, the current method relies on visual inspection by workers, making accurate and rapid inspections difficult. One approach to solving this problem is to use anomaly detection techniques based on SCADA data. Research is currently underway to implement this approach. However, anomaly detection methods based on SCADA data have been criticized for their limited ability to accommodate multiple operating modes, including de-rated operation. In this study, we propose the “scaled power curve” as a robust feature that is less affected by operating modes, with its effectiveness verified through anomaly detection. This method showed improved anomaly detection accuracy compared to using the original power curve as a feature; moreover, in the present case, the method remained effective under de-rated operation. By using this feature, it is expected that a lightning damage detection model can be developed, contributing to improved availability of wind turbines.