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  4. Analysis of Defects on Solar Power Cells

Analysis of Defects on Solar Power Cells

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  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users
  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users

Analysis of Defects on Solar Power Cells

Analysis of Defects on Solar Power Cells

(Third Party Funds Group – Sub project)

Overall project: iPV 4.0: Intelligente vernetzte Produktion mittels Prozessrückkopplung entlang des Produktlebenszyklus von Solarmodulen
Project leader: Andreas Maier
Project members: Mathis Hoffmann
Start date: August 1, 2018
End date: July 31, 2021
Acronym:
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
URL:

Abstract

Over the last decade, a large number of solar power plants have been installed in Germany. To ensure a high performance, it is necessary to detect defects early. Therefore, it is required to control the quality of the solar cells during the production process, as well as to monitor the installed modules. Since manual inspections are expensive, a large degree of automation is required.
This project aims to develop a new approach to automatically detect and classify defects on solar power cells and to estimate their impact on the performance. Further, big data methods will be applied to identify circumstances that increase the probability of a cell to become defect. As a result, it will be possible to reject cells in the production that have a high likelihood to become defect.

Publications

  • Hoffmann M., Doll B., Talkenberg F., Brabec C., Maier A., Christlein V.:
    Fast and Robust Detection of Solar Modules in Electroluminescence Images
    18th International Conference on Computer Analysis of Images and Patterns (Salerno, September 2, 2019 - September 6, 2019)
    In: Springer, Cham (ed.): Computer Analysis of Images and Patterns 2019
    DOI: 10.1007/978-3-030-29891-3
    URL: https://www.researchgate.net/publication/335361806_Fast_and_Robust_Detection_of_Solar_Modules_in_Electroluminescence_Images
    BibTeX: Download
  • Buerhop-Lutz C., Hoffmann M., Reeb L., Pickel T., Hauch J., Maier A.:
    Applying Deep Learning Algorithms to EL-images for Predicting the Module Power
    36th European Photovoltaic Solar Energy Conference and Exhibition (Marseille, September 9, 2019 - September 13, 2019)
    In: Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition 2019
    DOI: 10.4229/EUPVSEC20192019-4CO.1.2
    BibTeX: Download

Friedrich-Alexander-Universität Erlangen-Nürnberg
Lehrstuhl für Mustererkennung (Informatik 5)

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91058 Erlangen
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