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Defect Analysis of Electroluminescence Images of PV CELL

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 300)

Abstract

In Current world, photovoltaic cells are thought of as a renewable and environment friendly resource of power on earth. It transforms direct light coming from the sun into electricity with no emission and also helpful in the conservation of the natural. But, solar cells suffer some problems which may be optical or mechanical defects which consist of micro crack, the scale of crack, and therefore which comes with the side effect from electrical or electromechanical interference during the image acquisition. All the above points cause degradation in energy generation, and additionally if this issue occurs at manufacturing end then it’ll have a big impact on the sector and makes it very hard to spot the panel within the later stage. This paper through image processing techniques presents a combination of varied advanced computer vision methods to de-noise EL images and supply the labelled data for future extraction and efficiently identify the micro cracks in Solar PV cells at any given stages.

Keywords

  • PV cell crack detection
  • Deep learning
  • U-Net
  • Fully convolutional network
  • Image segmentation
  • Image classification

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  • DOI: 10.1007/978-3-030-84760-9_57
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Correspondence to Dinesh Rathod .

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Rathod, D., Goswami, A. (2022). Defect Analysis of Electroluminescence Images of PV CELL. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_57

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