Automated defect identification in electroluminescence images of solar modules-Reference-Cited by-同舟云学术

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Automated defect identification in electroluminescence images of solar modules

Published:2022-08 Issue: Volume:242 Page:20-29
ISSN:0038-092X
Container-title:Solar Energy
language:en
Short-container-title:Solar Energy

Author:

Chen Xin^ORCID,Karin Todd,Jain Anubhav^ORCID

Funder

U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Solar Energy Technologies Office

Publisher

Elsevier BV

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Reference44 articles.

1. Review of microcrack detection techniques for silicon solar cells;Abdelhamid;IEEE J. Photovolt.,2014

2. CNN based automatic detection of photovoltaic cell defects in electroluminescence images;Akram;Energy,2019

3. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique;Anwar;EURASIP J. Image Video Process.,2014

4. Bedrich, K., Quantified energy labs. [Online]. Available: https://qe-labs.com/.

5. Electroluminescence imaging of PV devices: Advanced vignetting calibration;Bedrich;IEEE J. Photovolt.,2018

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2. Investigation on a lightweight defect detection model for photovoltaic panel;Measurement;2024-08

3. DuraMAT: Building a Consortium to Accelerate the Photovoltaic Module Reliability Learning Cycle;PRX Energy;2024-06-24

4. Investigation of crack detection in solar cells by context integration and attention enhanced feature pyramid network;Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition;2024-04-30

5. 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

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