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Researchers from Zhejiang University, Quantified Energy and the University of New South Wales have developed a one-shot quantitative luminescence diagnostic framework for photovoltaic systems. The method converts a single luminescence image into spatially resolved power-loss maps using AI-assisted degradation identification and a device-physics-based reconstruction model. It separates recombination and resistance losses, retrieves local electrical responses and predicts module power without requiring images under multiple operating conditions. The researchers validated the framework across 300 PV modules covering different failure modes. Relative module-power prediction errors were reported as low as 0.5%. The approach is intended to support diagnostics from individual cells and modules to strings and utility-scale PV installations, with potential applications in inspection and maintenance planning.
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