Anomaly detection in IR images of PV modules using supervised contrastive learning

Autor: Lukas Bommes, Mathis Hoffmann, Claudia Buerhop‐Lutz, Tobias Pickel, Jens Hauch, Christoph Brabec, Andreas Maier, Ian Marius Peters

Publikováno v: Progress in photovoltaics 30(6), 597-614 (2022). doi:10.1002/pip.3518

Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning has become popular for this. However, related works typically sample

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77b278f95dc9850d55f2708e52c2d7f3
https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19638

Module-Power Prediction from PL Measurements using Deep Learning

Autor: Vincent Christlein, Bernd Doll, Andreas Maier, Claudia Buerhop-Lutz, Christoph J. Brabec, Mathis Hoffmann, Ian Marius Peters, Johannes Hepp

Publikováno v: 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC).

The individual causes for power loss of photovoltaic modules are investigated for quite some time. Recently, it has been shown that the power loss of a module is, for example, related to the fraction of inactive areas. While these areas can be easily

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a78ebf4f82833efc3cd2df739957bd29
https://doi.org/10.1109/pvsc43889.2021.9519005

Deep-learning-based pipeline for module power prediction from electroluminescense measurements

Autor: Tobias Pickel, Thilo Winkler, Claudia Buerhop-Lutz, Mathis Hoffmann, Vincent Christlein, Tobias Würfl, Bernd Doll, Ian Marius Peters, Christoph J. Brabec, Andreas Maier, Luca Reeb

Publikováno v: Progress in photovoltaics 29(8), 920-935 (2021). doi:10.1002/pip.3416
Seminar, Deutschland

Automated inspection plays an important role in monitoring large‐scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules, but have not been used to determine the pow

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb99cfa46c3da6d291e09887717b0b12
https://hdl.handle.net/2128/28310

Weakly Supervised Segmentation of Cracks on Solar Cells Using Normalized Lp Norm

Autor: Vincent Christlein, Martin Mayr, Andreas Maier, Mathis Hoffmann

Publikováno v: ICIP

Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable automatic quality management during production and operation. However,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9f914782cc7d384002796cd8cfd76082
https://doi.org/10.1109/icip.2019.8803116

Fast and Robust Detection of Solar Modules in Electroluminescence Images

Autor: Christoph J. Brabec, Bernd Doll, Andreas Maier, Vincent Christlein, Mathis Hoffmann, Florian Talkenberg

Publikováno v: Computer Analysis of Images and Patterns ISBN: 9783030298906
CAIP (2)
Cham : Springer International Publishing, Lecture Notes in Computer Science 11679, 519-531 (2019). doi:10.1007/978-3-030-29891-3_46
Computer Analysis of Images and Patterns / Vento, Mario (Editor) [https://orcid.org/0000-0002-2948-741X] ; Cham : Springer International Publishing, 2019, Chapter 46 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-030-29890-6=978-3-030-29891-3 ; doi:10.1007/978-3-030-29891-3

Fast, non-destructive and on-site quality control tools, mainly high sensitive imaging techniques, are important to assess the reliability of photovoltaic plants. To minimize the risk of further damages and electrical yield losses, electroluminescenc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0adbe7478b7f89db0a2c12b7ea8d8b5e
https://doi.org/10.1007/978-3-030-29891-3_46