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Article

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

1
Department of Energy Technology, AAU, 9220 Aalborg, Denmark
2
School of Photovoltaic and Renewable Energy Engineering, UNSW, Kensington, NSW 2052, Australia
3
Department of Photonics Engineering, Technical University of Denmark, 4000 Roskilde, Denmark
4
PI Photovoltaik-Institut Berlin AG, Wrangelstr. 100, D-10997 Berlin, Germany
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School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(24), 8834; https://doi.org/10.3390/app10248834
Received: 27 October 2020 / Revised: 7 December 2020 / Accepted: 8 December 2020 / Published: 10 December 2020
(This article belongs to the Special Issue Fault Diagnosis and Control Design Applications of Energy Systems)
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 high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields. View Full-Text
Keywords: electroluminescence imaging; photovoltaic modules; defect classification; micro-cracks (mode A); cracks (mode B and C); finger failures; pixel intensity histogram; statistical parameters; machine learning classifiers electroluminescence imaging; photovoltaic modules; defect classification; micro-cracks (mode A); cracks (mode B and C); finger failures; pixel intensity histogram; statistical parameters; machine learning classifiers
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MDPI and ACS Style

Parikh, H.R.; Buratti, Y.; Spataru, S.; Villebro, F.; Reis Benatto, G.A.D.; Poulsen, P.B.; Wendlandt, S.; Kerekes, T.; Sera, D.; Hameiri, Z. Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning. Appl. Sci. 2020, 10, 8834. https://doi.org/10.3390/app10248834

AMA Style

Parikh HR, Buratti Y, Spataru S, Villebro F, Reis Benatto GAD, Poulsen PB, Wendlandt S, Kerekes T, Sera D, Hameiri Z. Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning. Applied Sciences. 2020; 10(24):8834. https://doi.org/10.3390/app10248834

Chicago/Turabian Style

Parikh, Harsh R., Yoann Buratti, Sergiu Spataru, Frederik Villebro, Gisele A.D. Reis Benatto, Peter B. Poulsen, Stefan Wendlandt, Tamas Kerekes, Dezso Sera, and Ziv Hameiri. 2020. "Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning" Applied Sciences 10, no. 24: 8834. https://doi.org/10.3390/app10248834

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