Photovoltaic Module

Photovoltaic (PV) modules are crucial for solar energy harvesting, but their efficiency is hampered by defects and soiling. Current research focuses on automated defect detection using computer vision, primarily employing convolutional neural networks (CNNs), often enhanced by techniques like data augmentation and knowledge distillation, to analyze images from drones or other sources. These advancements aim to improve the cost-effectiveness and efficiency of PV system maintenance and monitoring, leading to increased energy production and reduced operational costs. Unsupervised learning methods are also being explored to reduce reliance on labeled data for soiling and anomaly detection.