A Deep Learning-Based Framework for Automatic Detection of Defective Solar Photovoltaic Cells in Electroluminescence Images Using Transfer Learning

Abraham Kaligambe, Goro Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The utilization of electroluminescence (EL) imaging has proven to be a reliable and precise method for inspecting photovoltaic (PV) modules, due to its high spatial resolution, which allows for the detection of various types of defects. However, the manual analysis of EL images is both expensive, and time-consuming, and requires a specialist with extensive knowledge to identify a wide range of defects. In this study, we propose a deep learning-based technique for the automatic detection of defective solar cells from EL images. Specifically, we employed two convolutional neural network (CNN) architectures in our proposed framework. The first architecture is a transfer learning-based VGG16 model that has been fine-tuned with custom fully connected neural network layers to classify defective and non-defective solar cells. The second architecture is a lightweight CNN model that was created from scratch and was used as a baseline for classification comparison with the VGG16 fine-tuned model. The models were trained on a publicly available monocrystalline solar cell image dataset. To address overfitting and to increase the dataset size, we utilized data augmentation techniques. Our proposed method achieved a 95.2% accuracy on the test dataset, which is higher than in previous studies. The implementation of our proposed method will enable continuous, rapid, and precise quality inspection of solar PV plants. Proper maintenance of solar PV panels can significantly improve their efficiency, safety, and power output.

Original languageEnglish
Title of host publicationProceedings of 2023 4th International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-85
Number of pages5
ISBN (Electronic)9798350318678
DOIs
Publication statusPublished - 2023
Event4th International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2023 - Denpasar Bali, Indonesia
Duration: 2023 Aug 62023 Aug 10

Publication series

NameProceedings of 2023 4th International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2023

Conference

Conference4th International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2023
Country/TerritoryIndonesia
CityDenpasar Bali
Period23/8/623/8/10

Keywords

  • convolutional neural networks
  • defect detection
  • electroluminescence imaging
  • manual inspection
  • photovoltaic cells
  • transfer learning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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