Enhancing the Reliability and Efficiency of Solar Systems Through Fault Detection in Solar Cells Using Electroluminescence (EL) Images and YOLO Version 5.0 Algorithm

The importance of solar energy as a renewable power source has led to increased adoption of solar modules for electricity generation. However, faults in solar cells can significantly impact their performance and efficiency. Manual defect detection is time-consuming and subjective, hence the need for an intelligent and efficient detection solution. In this study, we propose a method for detecting defective solar cells in electroluminescence imaging using an advanced object detection algorithm, specifically YOLO5 version. An important step in the algorithm is to formulate the detection problem in terms of real-time detection of defects. We evaluate our method on a dataset of different types of solar modules containing a total of 240 solar cells with various defects, including finger interruptions, microcracks, electrically separated or degraded cell parts and material defects. Experimental evaluation on solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules datasets reveals that the proposed framework successfully mitigates the influence of defect image degradation. The precision and recall confidence curves indicate a moderate performance, suggesting that the framework shows promising capabilities in detecting and localizing defects. This research contributes to the widespread adoption and sustainable utilization of solar energy, ensuring the optimal performance and longevity of solar cells.