Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects.In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono-and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42 %. The SVM achieves a slightly lower average accuracy of 82.44 %, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.

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Degradation of backsheets (BSs) of commercial silicon PV modules is currently recognized as a source of reduced module performance and module failure. Monitoring of the BS state in the field is possible by using non-destructive and highly informative near-infrared absorption (NIRA) spectroscopy. Application of NIRA for the analysis of multi-layer polyethylene terephtalate (PET) based BSs, which dominate the PV module market, is challenging due to a large variety of possible BS configurations that show only small differences in NIRA spectra. In the present work, a spectroscopic tool for the structural identification of PET-based BSs is introduced. The method is based on a principal component analysis of a database of 250 representative NIRA spectra of BSs of different types. It allows a BS with an unknown structure to be assigned to one of 12 different types based solely on its NIRA spectrum. The identification was successfully validated on a test collection of 45 selected BSs and shown to be feasible for the field deployment. Further automation of NIRA measurements and spectral analysis are expected to elevate the proposed tool to the level of a nonintrusive high-throughput field analysis of the BS composition and state in operating PV module grids.multispectral Raman imaging, polyethylene terephtalate, principal component analysis, PV module backsheets, spectral characterization | INTRODUCTIONDegradation of polymer components of commercial silicon PV modules is currently recognized as one of the major factors limiting module performance and lifetime, and causing security and financial risks for the stakeholders of PV installations. [1][2][3] Typically, UV irradiation has the largest impact on polymer encapsulants of Si wafers while climatic stress (high/low temperatures, humidity) affects mostly the polymeric backsheets (BSs) designed to provide a mechanical support to solar cells as well as to insulate and protect the cells from the environmental factors. [1][2][3][4][5][6] The degradation of both encapsulant and BS can influence and accelerate each other, for example via partial BS decomposition by the products of encapsulant hydrolysis and, vice versa, via encapsulant deterioration by moisture and oxygen penetrating through a partially decomposed BS. 1,3,[5][6][7][8][9] As a result, the long-term behavior and degradation stability of PV