An international team of researchers has performed a quantitative power-loss assessment of PV modules affected by type-C cracks using electroluminescence (EL) imaging.
“This research applies high-resolution EL imaging and automatic crack segmentation to quantify type-C cracks in a sample of 100 PV modules from two manufacturers, correlating these measurements with power loss at module, substring, and cell levels,” corresponding author Jose Antonio Clavijo-Blanco told pv magazine. “Statistical characterization of power losses was performed both for the sample as a whole and by manufacturer.”
The type-C cracks – defined as cracks that create fully disconnected dark regions, leading to power loss and potentially also hot spots – were quantified across PV crystalline-silicon (c-Si) modules that have been in operation for 11 years.
A key finding of the study, was that concentrated type-C cracks in individual PV cells cause significantly higher power losses than the same total cracked area spread evenly across the panel. “This demonstrates that localized damage at the cell level is the main driver of performance reduction, highlighting the importance of analyzing cracks at the cell scale rather than only at the module or substring level,” Clavijo-Blanco highlighted.
The 100 PV modules were sourced from a 2.85 MW solar power plant in southern Spain. Half of the tested modules were from an unnamed Manufacturer 1, with capacities ranging from 210 W to 225 W, while the other half were 270 W modules from an unnamed Manufacturer 2. Each Manufacturer 1 module had 60 cells, and each Manufacturer 2 module had 72 cells. All modules were equipped with three bypass diodes, which electrically divide the modules into three sections.
The modules were examined using a practical decision-making framework consisting of three stages, namely image capture, image processing, and image analysis. The first stage consists of cleaning the module, mounting it securely, and then biasing and capturing EL images in a dark environment. The second stage includes gray tone normalization, thresholding, and grid-based segmentation.
Image: University of Cádiz
Finally, in the last stage, type-C areas are quantified, and power loss is estimated through a simplified mismatch model. The scientists then use the weighted-average degradation rate (WADR) to accept or reject a module: a sample with WADR below 11% is deemed suitable, a sample with WADR between 11% and 20% is considered degraded, and a module with WADR above 20% is considered unstable.
“The combination of quantitative crack analysis and operational decision-making in a real-world module sample represents a significant advance in PV monitoring and maintenance strategies,” said Clavijo-Blanco. “Differences observed between PV modules from different manufacturers suggest that installation or operational factors can influence degradation patterns.”
The analysis showed that 62% of Manufacturer 1 modules were suitable, 22% were degraded, and 8% were unstable; for Manufacturer 2, 30% were suitable, 60% were degraded, and 10% were unsuitable. Further statistical analysis of the sample indicates that the Generalized Extreme Value distribution best fits the power loss estimation results, with a mean of 12.77%.
“Finally, from a corrective maintenance standpoint, it was confirmed that a single crack affecting more than 20% of any individual cell could lead to power losses above 20%, classifying the module as unsuitable,” the team explained. “This finding highlights the importance of evaluating the spatial distribution of Type-C cracks, as concentrated cracks in a single cell can lead to significant losses for the entire module.”
The results of the tests were presented in “Quantitative assessment of PV modules affected by Type-C cracks using electroluminescence imaging,” published in Solar Energy. Researchers from Spain’s University of Cádiz and Tanzania’s University of Dodoma have participated in the study.
“By combining expertise from European and African institutions, the study not only strengthens international cooperation in the field of PV research but also promotes knowledge exchange and capacity building across continents,” concluded Clavijo-Blanco. “Future research will focus on improving the accuracy of power loss estimation, considering additional parameters such as temperature effects, and extending the analysis to other crack types and EL-detectable defects. Furthermore, we plan to implement machine learning techniques for automatic detection and classification of type-C cracks.”
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