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000890204 005__ 20240712084522.0
000890204 0247_ $$2doi$$a10.4229/EUPVSEC20202020-5CV.3.13
000890204 0247_ $$2Handle$$a2128/27042
000890204 037__ $$aFZJ-2021-00793
000890204 041__ $$aeng
000890204 1001_ $$0P:(DE-Juel1)177942$$aSovetkin, Evgenii$$b0$$eCorresponding author$$ufzj
000890204 1112_ $$a37th European Photovoltaic Solar Energy Conference and Exhibition$$cLisbon$$d2020-09-07 - 2020-09-11$$wPortugal
000890204 245__ $$aPV-AIDED: Photovoltaic Artificial Intelligence Defect Identification. Multichannel Encoder-decoder Ensemble Models for Electroluminescence Images of Thin-film Photovoltaic Modules, PEARL TF-PV.
000890204 260__ $$bWIP$$c2020
000890204 300__ $$a1520 - 1527
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000890204 520__ $$aThe 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.
000890204 536__ $$0G:(DE-HGF)POF3-121$$a121 - Solar cells of the next generation (POF3-121)$$cPOF3-121$$fPOF III$$x0
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000890204 7001_ $$0P:(DE-Juel1)130284$$aPieters, Bart$$b1$$ufzj
000890204 7001_ $$0P:(DE-HGF)0$$aWeber, Thomas$$b2
000890204 7001_ $$0P:(DE-HGF)0$$aAchterberg, Elbert Jan$$b3
000890204 7001_ $$0P:(DE-HGF)0$$aWeeber, Arthur$$b4
000890204 7001_ $$0P:(DE-HGF)0$$aBjoern, Rau$$b5
000890204 7001_ $$0P:(DE-HGF)0$$aRennhofer, Marcus$$b6
000890204 7001_ $$0P:(DE-HGF)0$$aTheelen, Mirjam$$b7
000890204 773__ $$a10.4229/EUPVSEC20202020-5CV.3.13
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000890204 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a PI Photovoltaik-Institut Berlin AG, Wrangelstraße 100, 10997 Berlin, Germany$$b2
000890204 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Solar Tester, Vonderstraat 33A, 6365CR Schinnen, Netherlands$$b3
000890204 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Delft University of Technology, PVMD group, Mekelweg 4, 2628 CD Delft, Netherlands$$b4
000890204 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Helmholtz-Zentrum Berlin f ̈ur Materialien und Energie GmbH/PVcomB, Schwarzschildstr.3, 12489 Berlin, Germany$$b5
000890204 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a AIT, Austrian Institute of Technology, Donau-City-Strasse 1, 1220 Vienna, Austria$$b6
000890204 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a TNO, High Tech Campus 21, 5656AE Eindhoven, Netherlands$$b7
000890204 9131_ $$0G:(DE-HGF)POF3-121$$1G:(DE-HGF)POF3-120$$2G:(DE-HGF)POF3-100$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bEnergie$$lErneuerbare Energien$$vSolar cells of the next generation$$x0
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