SRGAN-Based Deep Learning Framework for Wind Turbine Damage Detection from Sentinel-2 Imagery

The operational reliability of wind turbines is critical for sustainable energy production in smart grids.This study proposes a remote monitoring approach using perceptually enhanced satellite imagery.Sentinel-2 multispectral data (10 m resolution) has been processed with a Super-Resolution Generative Adversarial Network (SRGAN) to improve visual quality to a perceptual resolution of 30 cm.Although true spatial refinement is not achieved, the sharper structural details enhance classification accuracy.The data set comprises 15,000 images-10,000 SRGAN-enhanced and 5000 augmented through rotation, zoom in, increasing brightness, noise addition, and blurring.A custom Convolutional Neural Network (CNN) has been trained to classify turbines as damaged or intact, achieving 95% accuracy, a 0.99 ROC-AUC, and a 0.95 F1 score.These results demonstrate that perceptually sharpened satellite data can effectively support automated wind turbine damage detection and predictive maintenance.The proposed framework also lays the groundwork for broader real-time and multimodal monitoring and cost-efficient applications in renewable energy systems.