Intelligent Condition Monitoring of Wind Turbines Under Variable Damage Conditions

Condition monitoring of wind turbines is essential to ensure their reliable and efficient operation, reducing downtime and maintenance costs while maximizing energy generation. In recent years, advancements in sensor technologies have enabled the collection of vast amounts of data from wind turbines, including ultrasonic signals. Ultrasonic guided wave (GW) signals contain valuable information about the health of the turbine components, making them a promising source for condition monitoring. This research presents a concise and innovative approach to condition monitoring of wind turbines using machine learning in conjunction with GW signals. Ultrasonic GW signals contain valuable information about turbine health, making them promising for monitoring
purposes. The proposed methodology involves transforming ultrasonic data using advanced signal processing techniques and fine-tuning a pre-trained deep learning model for autonomous condition monitoring of composite wind turbine blade laminates. This integration significantly improves fault detection and health state classification accuracy compared to traditional methods, making it more practical for wind farm environments. The findings demonstrate the potential of this approach in enhancing wind turbine reliability and efficiency, contributing to a sustainable and eco-friendly energy generation process.