Classification of Defective Photovoltaic Modules in ImageNet-Trained Networks Using Transfer Learning

Dongkun Hou, Jieming Ma, Sida Huang, Jie Zhang, Xiaohui Zhu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

9 Citations (Scopus)

Abstract

The quality inspection of photovoltaic modules is very important to power generation efficiency. Although electroluminescence can directly detect the defects in photovoltaic modules, it is difficult to get the desirable accuracy by traditional manual inspection. It thus necessitates an efficient defect identification method. This paper introduces ImageNet-trained networks for identifying the defective Photovoltaic modules. Six convolutional neural networks are realized by transfer learning. The experimental results present the Xception model has a higher accuracy for the classification of defective photovoltaic cells.

Original languageEnglish
Title of host publicationProceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2127-2132
Number of pages6
ISBN (Electronic)9781728163444
DOIs
Publication statusPublished - 24 May 2021
Event12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 - Virtual, Singapore, Singapore
Duration: 24 May 202127 May 2021

Publication series

NameProceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

Conference

Conference12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Country/TerritorySingapore
CityVirtual, Singapore
Period24/05/2127/05/21

Keywords

  • Convolutional Neural Network
  • Electroluminescence images
  • ImageNet-trained network
  • Photovoltaic module
  • Transfer learning

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