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

Harsh Rajesh Parikh, Yoann Buratti, Sergiu Spataru, Frederik Villebro, Gisele Alves dos Reis Benatto, Peter Behrensdorff Poulsen, Stefan Wendlandt, Tamas Kerekes, Dezso Sera, Ziv Hameiri

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Abstract

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.
Original languageEnglish
Article number8834
JournalApplied Sciences
Volume10
Issue number24
Number of pages15
ISSN2076-3417
DOIs
Publication statusPublished - 2020

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

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