Deep Learning Framework for Solar Panel Fault Detection: Combining GAN and EfficientNet

The increasing adoption of solar energy systems necessitates robust solutions for addressing efficiency challenges posed by environmental conditions and operational anomalies in photovoltaic (PV) panels. This study emphasizes the importance of real-time monitoring to mitigate power losses caused by surface contamination, shading discrepancies, and thermal variations. These factors contribute to hotspot formation and accelerated degradation of solar panels. By leveraging advanced machine learning techniques, this work integrates Generative Adversarial Networks (GANs) for data augmentation and pattern recognition with EfficientNet for efficient feature extraction and classification. The combination enables accurate detection of common PV faults, such as cracks, dust accumulation, shading irregularities, and thermal hotspots, from both visible light and infrared images. This approach achieves enhanced fault detection accuracy while reducing computational overhead, demonstrating its suitability for large-scale solar installations. The proposed deep learning framework provides a scalable and reliable solution to improve the performance and lifespan of solar energy systems while minimizing maintenance efforts and operational interruptions.