A Holistic Approach to Defect Detection in Solar Modules: Leveraging Lifecycle Data for Improved Performance

The efficient maintenance and optimization of solar modules are critical for sustaining high energy yields over their operational lifetimes. This research introduces a comprehensive system designed to enhance lifecycle traceability and defect detection in solar modules using a combination of advanced image analysis and machine learning techniques. By leveraging Convolutional Neural Networks (CNN), You Only Look Once (YOLO) object detection, and deep learning, the system analyzes thermal and normal imaging data as well as current-voltage (IV) characteristics and curves. The proposed framework enables the detection of common faults, such as hotspots, cell cracks, and degradation patterns, which can impact performance and safety. Integrated data management and tracking capabilities facilitate end-to-end lifecycle monitoring, providing accessible, organized insights for stakeholders involved in solar module maintenance and diagnostics. The model shows an accuracy of 90%. The results show that the system not only improves accuracy in fault identification but also allows efficient storage and retrieval of diagnostic data, presenting a robust solution for advancing photovoltaic asset management