Enhanced Fault Identification and Classification of Wind Turbine Blades using Machine Learning

Wind energy is rapidly growing across the world. Wind Turbine generate energy without emitting greenhouse gases. Faults in the wind turbine blades can considerably impact the efficiency, reliability and safety of wind turbine operations. Traditional methods of fault detection often rely on human inspection or basic monitoring systems which is time consuming and prone to human errors. In this paper we use an enhanced fault detection and classification method for the blades in wind turbines with the help of different machine learning algorithms using the images captured by Unmanned Arial Vehicles (UAVs). By leveraging advanced ML algorithms, our approach aims to accurately detect early-stage faults and classify them based on their types, which helps more precise and timely maintenance operations. The proposed method is trained using a comprehensive dataset which includes various blade conditions, including those affected by environmental factors, material fatigue, and operational stress. Through extensive testing and validation, this work demonstrates significant improvement in detection accuracy and classification performance. This enhanced fault detection framework not only reduces downtime and maintenance cost but also contributes to the operational efficiency of the wind turbines, Supporting the sustainable growth of wind energy.