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| Hauptseite > Publikationsdatenbank > Unveiling the Potential of Ultraviolet Fluorescence Imaging as a Versatile Inspection Tool: Insights from Extensive Photovoltaic Module Inspections in Multi‐MWp Photovoltaic Power Stations |
| Hauptseite > Publikationsdatenbank > Unveiling the Potential of Ultraviolet Fluorescence Imaging as a Versatile Inspection Tool: Insights from Extensive Photovoltaic Module Inspections in Multi‐MWp Photovoltaic Power Stations |
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| Journal Article | FZJ-2025-00966 |
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2024
Wiley-VCH
Weinheim
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Please use a persistent id in citations: doi:10.1002/solr.202400566 doi:10.34734/FZJ-2025-00966
Abstract: UV fluorescence imaging (UVF) has the potential to grow into a powerful, informative, and economically attractive inspection method for photovoltaic (PV) power stations. UVF demonstrates the ability to indicate differences in polymer types and degradation states in backsheets and encapsulation materials of PV modules. The data acquisition rate of UVF measurements is 10 to 15 times higher than that achieved by state-of-the-art near-infrared spectroscopy, enabling the mapping of the bill of materials (BoMs) and degradation state for every module in large PV arrays. Combinations of UVF with vibrational spectroscopies allow the polymer BoMs to be recognized and correlated with aging phenomena such as metal corrosion or potential induced degradation. UVF imaging can also be used for conventional visualization of nonmaterial-related anomalies, such as hot cells or cell cracks. The low purchase cost of the equipment makes UVF an affordable and high-throughput diagnostic method yielding comprehensive information that would otherwise only be accessible by combining several slower and more laborious methods. Automated UVF image analysis and refinements of correlations between different BoMs and UVF pattern geometry can further unlock the potential of UVF as a straightforward tool accessible to all stakeholders and promote proactive strategies for the optimization of the reliability and performance of PV power stations.
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