Deep‐learning‐based pipeline for module power prediction from electroluminescense measurements-Reference-Cited by-同舟云学术

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

Published:2021-05-20 Issue:8 Volume:29 Page:920-935
ISSN:1062-7995
Container-title:Progress in Photovoltaics: Research and Applications
language:en
Short-container-title:Prog Photovolt Res Appl

Author:

Hoffmann Mathis¹²^ORCID,Buerhop‐Lutz Claudia³^ORCID,Reeb Luca¹³,Pickel Tobias³,Winkler Thilo³²,Doll Bernd³²⁴^ORCID,Würfl Tobias¹^ORCID,Marius Peters Ian³^ORCID,Brabec Christoph³²⁴^ORCID,Maier Andreas¹⁴^ORCID,Christlein Vincent¹^ORCID

Affiliation:

1. Pattern Recognition Lab University of Erlangen–Nuremberg (FAU) Erlangen Germany

2. Institute Materials for Electronics and Energy Technology FAU Erlangen Germany

3. Jülich Research Center GmbH Helmholtz‐Institute Erlangen–Nuremberg for Renewable Energies Nuremberg Germany

4. School of Advanced Optical Technologies FAU Erlangen Germany

Funder

Bundesministerium für Wirtschaft und Energie

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pip.3416

Reference39 articles.

1. Evolution of cell cracks in PV-modules under field and laboratory conditions

2. KöntgesM Kajari‐SchröderS KunzeI JahnU.Crack statistic of crystalline silicon photovoltaic modules. In: 26th European Photovoltaic Solar Energy Conference and Exhibition Vol. 26.Hamburg Germany;2011:3290‐3294.

3. Spatial and orientational distribution of cracks in crystalline photovoltaic modules generated by mechanical load tests

4. Investigations on crack development and crack growth in embedded solar cells

5. A global/local approach for the prediction of the electric response of cracked solar cells in photovoltaic modules under the action of mechanical loads

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning for advanced characterisation of silicon photovoltaics: A comprehensive review of techniques and applications;Renewable and Sustainable Energy Reviews;2024-09

2. Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance;Sensors;2024-02-24

3. PV module power prediction by deep learning on electroluminescence images - Assessing the physics learned by a convolutional neural network;Solar Energy Materials and Solar Cells;2024-01

4. Shingle cell IV$$ IV $$ characterization based on spatially resolved host cell measurements;Progress in Photovoltaics: Research and Applications;2023-12-26

5. Progress in module level quantitative electroluminescence imaging of crystalline silicon PV module: A review;Solar Energy;2023-11