Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

Chen, Xin; Karin, Todd; Libby, Cara; Deceglie, Michael; Hacke, Peter; Silverman, Timothy J.; Jain, Anubhav

doi:10.1109/jphotov.2023.3249970

Title: Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

Journal Article · Fri Mar 10 00:00:00 EST 2023 · IEEE Journal of Photovoltaics

DOI:https://doi.org/10.1109/jphotov.2023.3249970· OSTI ID:1973673

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Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Electric Power Research Institute, Palo Alto, CA (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

The effect of cracks in solar cells on the long-term degradation of photovoltaic (PV) modules remains to be determined. To investigate this effect in future studies, it is necessary to quantitatively describe the crack features (e.g., length) and correlate them with module power loss. Electroluminescence (EL) imaging is a common technique for identifying cracks. However, it is currently challenging and time-consuming to identify cracks in a large number of EL images and quantify complex crack features by human inspection. This article introduces a fast semantic segmentation method (~0.18 s/cell) to automatically segment cracks from EL images and algorithms to extract crack features. Here we fine-tuned a UNet neural network model using pretrained VGG16 as the encoder and obtained an average F1 score of 0.875 and an intersection over union score of 0.782 on the testing set. With cracks and busbars segmented, we developed algorithms for extracting crack features, including the crack-isolated area, the brightness inside the isolated area, and the crack length. We also developed an automatic preprocessing tool for cropping individual cell images from EL images of PV modules (~0.72 s/module). Our codes are published as open-source an software, and our annotated dataset composed of various types of cells is published as a benchmark for crack segmentation in EL images.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308; 32509; AC02-05CH11231

OSTI ID:: 1973673

Report Number(s):: NREL/JA-5K00-83053; MainId:83826; UUID:1de5d43e-b2e8-48c6-89a7-0f55aa78d39d; MainAdminID:68975

Journal Information:: IEEE Journal of Photovoltaics, Vol. 13, Issue 3; ISSN 2156-3381

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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