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| Hauptseite > Publikationsdatenbank > PV-AIDED: Photovoltaic Artificial Intelligence Defect Identification. Multichannel Encoder-decoder Ensemble Models for Electroluminescence Images of Thin-film Photovoltaic Modules, PEARL TF-PV. |
| Hauptseite > Publikationsdatenbank > PV-AIDED: Photovoltaic Artificial Intelligence Defect Identification. Multichannel Encoder-decoder Ensemble Models for Electroluminescence Images of Thin-film Photovoltaic Modules, PEARL TF-PV. |
| Contribution to a conference proceedings | FZJ-2021-00793 |
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2020
WIP
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Please use a persistent id in citations: http://hdl.handle.net/2128/27042 doi:10.4229/EUPVSEC20202020-5CV.3.13
Abstract: The Solar-Era.net project PEARL TF-PV, [1], aims to reduce the uncertainties in the operation of thin-film solar power plants. To this end, one of the main parts of the project is the gathering of performance data and electroluminescence (EL) images of different types of thin-film solar cells and modules (see abstract of Mirjam Theelen et al, this conference). Detailed, local information on the module performance is obtained using EL imaging, which may provide early warning signs of degradation. A large number of samples (over 6000 modules) are analyzed, ranging from cells and modules produced in the different laboratories of the project partners to industrially produced modules used in power plants. Measurements are performed in laboratories as well as outdoor directly at the power plants location. All gathered data is stored in a database that in turn is used to develop a failure catalogue for thin-film modules that describes typical defects, visible with EL in various technologies, and their influence on the solar modules reliability and lifetime. In this work we present a novel image segmentation approach, aiming to identify commonly occurring defects in thin-film modules. We are building on top of the encoder-decoder neural networks framework, that have established itself as a standard tool in many other image processing applications. We demonstrate our software, PV-AIDED, is capable of fully automatic and fast EL image processing of full-sizes modules. We are able to reliably identify frequently occurring defects in thin-film modules, such as shunts and so called “droplets”. The framework is general and applicable to other types of defects, other types of PV images, as well as other types of PV technology.
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