Defektanalyse von Solarzellen
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:
Project members:
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
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 , , , , , :
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 , , , , , :