Quantifying mechanical stress effects on mono PERC and polycrystalline PV modules with EL imaging and statistical analysis

Solar photovoltaic modules convert solar energy into electrical energy, but defects like cell cracks, cell breakage, inactive cell area, and other defects impact the long-term performance and reliability of photovoltaic modules. Therefore, developing effective techniques for identifying such defects is crucial for optimising photovoltaic power plants efficiency. Using electroluminescence imaging, a method is proposed to quantify defects in mono PERC and polycrystalline solar photovoltaic modules. Various statistical measures, such as mean intensity, variance, kurtosis, and skewness of the electroluminescence intensity histogram, were compared. Healthy mono PERC modules exhibit higher values (for mean intensity: 0.530, variance: 0.867, kurtosis: 1.352, and skewness: 1.697) than defective modules (mean intensity: 0.441, variance: 0.396, kurtosis: 0.797, skewness: 1.466), indicating fewer defects with better performance. The polycrystalline photovoltaic module showed higher degradation with P max and I mp reductions of 15.13% and 14.09%, respectively, during static mechanical load testing. Results also show that modules with fewer defects exhibit better long-term performance, durability, and efficiency, particularly for mono PERC photovoltaic modules, which outperform polycrystalline modules in degradation analysis. Analysing electroluminescence images enables researchers and industry professionals to identify reliability issues in photovoltaic modules and to optimise the performance of solar photovoltaic power plants. • EL imaging reveals hidden mechanical stress defects in mono PERC and polycrystalline PV modules. • Greyscale histogram analysis boosts defect detection accuracy using standardised I-V curve testing. • Precise degradation quantification enhances PV module performance and reliability assessment. • Statistical metrics sharply distinguish healthy from defective modules, improving diagnostic precision. • AI and ML enable real-time defect monitoring, revolutionising PV maintenance and efficiency.