Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning

[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned method...

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Veröffentlicht in:	Solar energy Jg. 198; S. 175 - 186
Hauptverfasser:	Akram, M. Waqar, Li, Guiqiang, Jin, Yi, Chen, Xiao, Zhu, Changan, Ahmad, Ashfaq
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.03.2020 Pergamon Press Inc
Schlagworte:	Accuracy Artificial neural networks Automatic defect detection Automation Datasets Deep learning Defects Develop-model transfer deep learning Electroluminescence Infrared imagery Infrared images Infrared imaging Inspection Isolated deep learning Labeling Machine learning Modules Monitoring methods Neural networks Performance enhancement Photovoltaic (PV) modules Photovoltaic cells Photovoltaics Solar energy Thermography Transfer learning Isolated deep learning Automatic defect detection Develop-model transfer deep learning Photovoltaic (PV) modules Infrared images Thermography
ISSN:	0038-092X, 1471-1257
Online-Zugang:	Volltext
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