EL Dataset of PV modules

Version 1.5

Buerhop-Lutz, Claudia; Deitsch, Sergiu; Maier, Andreas; Gallwitz, Florian; Berger, Stephan; Doll, Bernd; Hauch, Jens; Camus, Christian; Brabec, Christoph J., 2020, "EL Dataset of PV modules", https://doi.org/10.26165/JUELICH-DATA/GCBNMA, Jülich DATA, V1

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Description	This repository provides a dataset of solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules. The dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of functional and defective solar cells with varying degree of degradations extracted from 44 different solar modules. The defects in the annotated images are either of intrinsic or extrinsic type and are known to reduce the power efficiency of solar modules. All images are normalized with respect to size and perspective. Additionally, any distortion induced by the camera lens used to capture the EL images was eliminated prior to solar cell extraction. Every image is annotated with a defect probability (a floating point value between 0 and 1) and the type of the solar module (either mono- or polycrystalline) the solar cell image was originally extracted from. The individual images are stored in the images directory and the corresponding annotations in labels.csv. --More explanations in the README file-- (2018)
Subject	Computer and Information Science; Earth and Environmental Sciences; Engineering
Keyword	EL-imaging, visual inspection, machine learning
Related Publication	Buerhop-Lutz, C.; Deitsch, S.; Maier, A.; Gallwitz, F.; Berger, S.; Doll, B.; Hauch, J.; Camus, C. & Brabec, C. J. A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery. European PV Solar Energy Conference and Exhibition (EU PVSEC), 2018. doi: 10.4229/35thEUPVSEC20182018-5CV.3.15
Institute	IEK-11

	1 File
	elpv-dataset-master.zip github.com/zae-bayern/elpv-dataset/ ZIP Archive - 89.0 MB - Aug 6, 2020 - 173 Downloads SHA-256: d6ab63010ebe5e7212dd3218545c2f182a0af791cae99f1381a0283506a7a73e Zip-file of repository	Download

Citation Metadata

Dataset Persistent ID	doi:10.26165/JUELICH-DATA/GCBNMA
Publication Date	2020-08-06
Title	EL Dataset of PV modules
Alternative URL	https://github.com/zae-bayern/elpv-dataset
Author	Buerhop-Lutz, Claudia (ZAE Bayern) Deitsch, Sergiu (Technische Hochschule Nürnberg Georg Simon Ohm) Maier, Andreas (Friedrich-Alexander-Universität Erlangen-Nürnberg) Gallwitz, Florian (Technische Hochschule Nürnberg Georg Simon Ohm) Berger, Stephan (ZAE Bayern) Doll, Bernd (ZAE Bayern) Hauch, Jens (ZAE Bayern) Camus, Christian (ZAE Bayern) Brabec, Christoph J. (ZAE Bayern)
Contact	Use email button above to contact. Buerhop-Lutz, Claudia (Forschungszentrum Jülich)
Description	This repository provides a dataset of solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules. The dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of functional and defective solar cells with varying degree of degradations extracted from 44 different solar modules. The defects in the annotated images are either of intrinsic or extrinsic type and are known to reduce the power efficiency of solar modules. All images are normalized with respect to size and perspective. Additionally, any distortion induced by the camera lens used to capture the EL images was eliminated prior to solar cell extraction. Every image is annotated with a defect probability (a floating point value between 0 and 1) and the type of the solar module (either mono- or polycrystalline) the solar cell image was originally extracted from. The individual images are stored in the images directory and the corresponding annotations in labels.csv. --More explanations in the README file-- (2018)
Subject	Computer and Information Science; Earth and Environmental Sciences; Engineering
Keyword	EL-imaging, visual inspection, machine learning
Related Publication	Buerhop-Lutz, C.; Deitsch, S.; Maier, A.; Gallwitz, F.; Berger, S.; Doll, B.; Hauch, J.; Camus, C. & Brabec, C. J. A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery. European PV Solar Energy Conference and Exhibition (EU PVSEC), 2018. doi: 10.4229/35thEUPVSEC20182018-5CV.3.15 Deitsch, S.; Buerhop-Lutz, C.; Maier, A. K.; Gallwitz, F. & Riess, C. Segmentation of Photovoltaic Module Cells in Electroluminescence Images. CoRR, 2018, abs/1806.06530 arXiv: 1806.06530 Deitsch, S.; Christlein, V.; Berger, S.; Buerhop-Lutz, C.; Maier, A.; Gallwitz, F. & Riess, C. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, Elsevier BV, 2019, 185, 455-468. doi: 10.1016/j.solener.2019.02.067
Depositor	Denz, Janine
Deposit Date	2020-08-06
Kind of Data	solar cell electroluminescence images

FZJ Metadata

Institute	IEK-11
PoF III topic	Solar cells of the next generation (POF3-121)
PoF IV topic	Acquisition before PoF IV (2021-2026) (POF4-898)

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