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.
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References
Tang, W., Yang, Q., Xiong, K., Yan, W.: Deep learning based automatic defect identification of photovoltaic module using electroluminescence images. ScienceDirect - Solar Energ. 201, 453–460 (2020)
Dhimish, M., Holmes, V.: Solar cells micro crack detection technique using state-of-the-art electroluminescence imaging. J. Sci. Adv. Mater. Devices (2019)
Menéndez, O., Guamán, R., Pérez, M., Cheein, F.A.: Photovoltaic modules diagnosis using artificial vision techniques for artifact minimization. MDPI: EN, 11(07), 1688 (2018)
Merchán, P., García, I.: On the detection of solar panels by image processing techniques. Research gate, Santiago Salamanca, 318694161 (2017)
Buerhop-Lutz, C., et al.: A benchmark for visual identification of defective solar cells in electroluminescence imagery. Eur. PV Solar Energ. Conf. Exhib. (EU PVSEC) (2018). https://doi.org/10.4229/35thEUPVSEC20182018-5CV.3.15
Deitsch, S., Buerhop-Lutz, C., Maier, A.K., Gallwitz, F., Riess, C.: Segmentation of Photovoltaic Module Cells in Electroluminescence Images. CoRR (2018). abs/1806.06530
Dhimish, M., Holmes, V., Dales, M., Mehrdadi, B.: Effect of micro cracks on photovoltaic output power: case study based on real time long term data measurements. Micro Nano Lett. 12, 803–807 (2017)
Desai, A., Injmulwar, P., Karadkhedkar, S.: Detection of micro-cracks in solar cell images. Int. J. Electr. Electron. Comput. Syst. V-4 I-2 (2016)
William, D.: Resonance Ultrasonic Vibrations (RUV) for crack detection in silicon wafers for solar cells. Graduate Theses and Dissertations (2006). http://scholarcommons.usf.edu/etd/2497
Bartler, A., Mauch, L., Yang, B., Reuter, M., Stoicescu, L.: Automated detection of solar cell defects with deep learning. In: 2018 26th European Signal Processing Conference (EUSIPCO) 2035–2039 (2018). https://doi.org/10.23919/EUSIPCO.2018.8553025
Spataru, S., Hacke, P., Sera, D.: Automatic detection of inactive solar cell cracks in electroluminescence images. In: 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), pp. 1421–1426 (2017). https://doi.org/10.1109/PVSC.2017.8366106
Zhang, N., Shan, S., Wei, H., Zhang, K.: Micro-cracks detection of polycrystalline solar cells with transfer learning. J. Phys. Conf. Ser. 1651, 012118 (2020)
Lydia, M., Sindhu, K., Gugan, K.: Analysis on solar panel crack detection using optimization techniques. J. Nano-and Electron. Phys. 9, 02004–1 (2017). https://doi.org/10.21272/jnep.9(2).02004
Dhimish, M., Mather, P.: Development of novel solar cell micro crack detection technique. IEEE Trans. Semicond. Manuf. 32(3), 277–285 (2019). https://doi.org/10.1109/TSM.2019.2921951
Teo, T.W., Abdullah, M.Z.: Solar cell micro-crack detection using localised texture analysis. J. Image Graph. 6(1), 54–58 (2018). https://doi.org/10.18178/joig.6.1.54-58
Crozier, J.L, Van Dyk, E.E., Vorster, F.J.: Identification and characterization of performance limiting defects and cell mismatch in photovoltaic modules. J. Energ. South. Afr. 26(3), pp.19–26 (2015). ISSN 2413–3051.
Energysage 2021. Monocrystalline and polycrystalline solar panels, digital image. https://www.energysage.com/solar/101/monocrystalline-vs-polycrystalline-solar-panels. Accessed 1 Apr 2021
Qiita.com 2021. Deep learning Day 4, Object Detection. https://qiita.com/381Pro/items/bbb7ddb694c70c0de5f6. Accessed 25 Mar 2021
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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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DOI: https://doi.org/10.1007/978-3-030-84760-9_57
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