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2020 | OriginalPaper | Buchkapitel

Deep Learning-Based Automatic Micro-crack Inspection in Space-Grade Solar Cells

verfasst von : Sharvari Gundawar, Nitish Kumar, N. Raghu Meetei, Ganesan Krishna Priya, Suresh E. Puthanveetil, Muthusamy Sankaran

Erschienen in: Advances in Small Satellite Technologies

Verlag: Springer Singapore

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Abstract

Spacecraft power systems reliability is critical parameter for mission success. Multiple checks and inspections are carried out for each component for power subsystems. In this paper, a novel automated solar cell micro-crack inspection tool is presented which is based on convolutional neural network (CNNs) to classify space-grade multi-junction solar cells taken under electroluminescence condition. The whole system is named ELSIS, which stands for “Electroluminescence Smart Inspection System”. It is an end-to-end automated system that acquires images under electroluminescence condition as arrays, identifies each cell and classifies them into two classes namely the cells that exhibit micro-cracks and cells and those that are free of micro-crack. ELSIS is developed with the objective to reduce the tedious and time-consuming manual effort required to identify micro-cracks and to increase the reliability of the solar cell modules by minimizing the human errors which may arise during the manual inspection of thousands of solar cells for spacecraft by automating the whole process. The CNNs for ELSIS have been developed in TensorFlow framework in Python based on InceptionV3 architecture. ELSIS is augmented with the latest image processing techniques that are applied to acquire EL images, which are modules containing array of solar cells (Fig. 1). ELSIS thus identifies and produces individual solar cells from the arrays and indexed and stored. The deep learning network was trained on a large number of solar cell >6000 images such that cross-entropy of the network settles within an accepted constant value. The trained network when tested for a large sample size of ~3000 new solar cells yielded a very reliable >98% accuracy. ELSIS helps in capacity building for scale manufacturing of satellites with stringent power budget, making it imperative to have zero defect solar cells.

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Literatur
1.
Zurück zum Zitat Zimmermann CG (2006) Assessing the in-orbit impact of cell cracks -an electroluminescence study of crack propagation. In: Photovoltaic energy conversion, Conference Record, IEEE Zimmermann CG (2006) Assessing the in-orbit impact of cell cracks -an electroluminescence study of crack propagation. In: Photovoltaic energy conversion, Conference Record, IEEE
2.
Zurück zum Zitat Köntges M, Kunze I, Kajari-Schröder S, Breitenmoser X, Bjørneklett B (2011) The risk of power loss in crystalline silicon based photovoltaic modules due to microcracks. Elsevier, pp 4 Köntges M, Kunze I, Kajari-Schröder S, Breitenmoser X, Bjørneklett B (2011) The risk of power loss in crystalline silicon based photovoltaic modules due to microcracks. Elsevier, pp 4
4.
Zurück zum Zitat Moon H-G, Kim J-H (2011) Intelligent crack detecting algorithm on concrete crack image using neural network. Department of Civil and Environmental Engineering, Yonsei University, Seoul, Korea (2011) Moon H-G, Kim J-H (2011) Intelligent crack detecting algorithm on concrete crack image using neural network. Department of Civil and Environmental Engineering, Yonsei University, Seoul, Korea (2011)
5.
Zurück zum Zitat Bastari A, Bruni A, Cristalli C (2010) Classification of silicon solar cells using electroluminescence texture analysis. In: 2010 IEEE international symposium on industrial electronics, Bari, pp 1722–1727 Bastari A, Bruni A, Cristalli C (2010) Classification of silicon solar cells using electroluminescence texture analysis. In: 2010 IEEE international symposium on industrial electronics, Bari, pp 1722–1727
6.
Zurück zum Zitat Tsai D-M, Wu S-C, Li W-C (2012) Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Sol Energy Mater Sol Cells 99:250–262CrossRef Tsai D-M, Wu S-C, Li W-C (2012) Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Sol Energy Mater Sol Cells 99:250–262CrossRef
7.
Zurück zum Zitat Spataru S, Hacke P, Sera D (2016) Automatic detection and evaluation of solar cell micro-cracks in electroluminescence images using matched filters. In: 2016 IEEE 43rd photovoltaic specialists conference (PVSC), Portland, OR, pp 1602–1607 Spataru S, Hacke P, Sera D (2016) Automatic detection and evaluation of solar cell micro-cracks in electroluminescence images using matched filters. In: 2016 IEEE 43rd photovoltaic specialists conference (PVSC), Portland, OR, pp 1602–1607
8.
Zurück zum Zitat Tsai D-M, Wu S-C, Chiu W-Y (2013) Defect detection in solar modules using ICA basis images. IEEE Trans Ind Inf Tsai D-M, Wu S-C, Chiu W-Y (2013) Defect detection in solar modules using ICA basis images. IEEE Trans Ind Inf
9.
Zurück zum Zitat Anwar SA, Abdullah MZ (2015) Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. Eurasip J Image Video Process Anwar SA, Abdullah MZ (2015) Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. Eurasip J Image Video Process
10.
Zurück zum Zitat Bharathkumar S, Ashokkumar K (2006) Solar cell panel crack detection using bacterial foraging optimization algorithm. Int J Sci Eng Technol Res Bharathkumar S, Ashokkumar K (2006) Solar cell panel crack detection using bacterial foraging optimization algorithm. Int J Sci Eng Technol Res
12.
Zurück zum Zitat Velasco F (1980 Nov) Thresholding using the ISODATA clustering algorithm. IEEE Trans Syst Man Cybern 10(11):771–774 Velasco F (1980 Nov) Thresholding using the ISODATA clustering algorithm. IEEE Trans Syst Man Cybern 10(11):771–774
14.
Zurück zum Zitat LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436 LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
15.
17.
Zurück zum Zitat Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint:1609.04747 Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv preprint:1609.04747
18.
Zurück zum Zitat Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. ICLR Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. ICLR
Metadaten
Titel
Deep Learning-Based Automatic Micro-crack Inspection in Space-Grade Solar Cells
verfasst von
Sharvari Gundawar
Nitish Kumar
N. Raghu Meetei
Ganesan Krishna Priya
Suresh E. Puthanveetil
Muthusamy Sankaran
Copyright-Jahr
2020
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-1724-2_30

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