An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants

Abstract

In this paper, we present the latest research results on the analysis of images taken during the condition assessment of solar cells and solar power plants. We aimed to summarize the most recent articles for 2024 and 2025. The annual volume of solar panels produced is expected to increase in the future. As imaging condition assessment technologies develop, the convolutional neural network models must follow this trend. In the field of real-time detection, CNN models will play an extremely important role because the faster any potential faults are identified, the quicker the response time during manufacturing and PV plant inspections. As part of CNN implementation in large PV power plants, IR and RGB imaging modes are very useful to detect failure sources. While IR imaging is useful in detecting heating from faults within PV panels or from nearby wiring, RGB imaging can detect mechanical defects such as broken glass planes, discolorations, and delamination. The implementation of these thus provides a higher chance of detecting solar panel damage and PV farms’ performance degradation or possible failure, resulting in a reduction in power generation interruptions. This will also allow faster and more efficient intervention and decision-making by operators in case of problems.