Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing h...

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Bibliographic Details
Main Authors: Harsh Rajesh Parikh, Yoann Buratti, Sergiu Spataru, Frederik Villebro, Gisele Alves Dos Reis Benatto, Peter B. Poulsen, Stefan Wendlandt, Tamas Kerekes, Dezso Sera, Ziv Hameiri
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Applied Sciences
Subjects:
electroluminescence imaging
photovoltaic modules
defect classification
micro-cracks (mode A)
cracks (mode B and C)
finger failures
Online Access:https://www.mdpi.com/2076-3417/10/24/8834

Internet

https://www.mdpi.com/2076-3417/10/24/8834

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