Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns

There have been numerous reported accidents of lightning strikes damaging wind turbine blades, which poses a serious problem. In certain accidents, the blades that were struck by lightning continued to rotate, resulting in breakage due to centrifugal force. Considering this background, wind turbines situated in Japan have been mandated to be equipped with emergency stop devices. Consequently, upon detection of a lightning strike by the device installed on the wind turbine, the turbine is promptly stopped. In order to restart the wind turbine, it is necessary to verify its soundness by conducting a visual inspection. However, conducting prompt inspections can be difficult due to various factors, including inclement weather. Therefore, this process prolongs the downtime of wind turbines and reduces their availability. In this study, an approach was proposed: a SCADA data analysis method using an autoencoder to assess the soundness of wind turbines without visual inspection. The present method selected wind speed and rotational speed as effective features, employing a sliding window for pre-processing, based on previous studies. Besides, the assessment of a trained autoencoder was conducted through the utilization of the confusion matrix and the receiver operating characteristic curve. It was suggested that the availability of wind turbines could be improved by employing this proposed method to remotely and automatically verify the soundness after lightning detection.