Automated defect identification in electroluminescence images of solar modules

Journal Article · Wed Jul 13 00:00:00 EDT 2022 · Solar Energy

DOI:https://doi.org/10.1016/j.solener.2022.06.031· OSTI ID:1961821

Chen, Xin ^[1]; Karin, Todd ^[1]; Jain, Anubhav ^[1]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1961821
• Alternate ID(s): OSTI ID: 1877728
• Journal Information: Solar Energy, Journal Name: Solar Energy Vol. 242; ISSN 0038-092X
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
big data
computer vision
deep learning
electroluminescence image
solar module defect