Image Preprocessing for Outdoor Luminescence Inspection of Large Photovoltaic Parks

Pascal Kölblin, Alexander Bartler and Marvin Füller
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Pascal Kölblin: Institute for Photovoltaics and Research Center SCoPE, University of Stuttgart, 70569 Stuttgart, Germany
Alexander Bartler: Institute of Signal Processing and System Theory, University of Stuttgart, 70569 Stuttgart, Germany
Marvin Füller: Institute for Photovoltaics and Research Center SCoPE, University of Stuttgart, 70569 Stuttgart, Germany

Energies, 2021, vol. 14, issue 9, 1-20

Abstract: Electroluminescence (EL) measurements allow one to detect damages and/or defective parts in photovoltaic systems. In principle, it seems possible to predict the complete current/voltage curve from such pictures even automatically. However, such a precise analysis requires image corrections and calibrations, because vignetting and lens distortion cause signal and spatial distortions. Earlier works on crystalline silicon modules used the cell gap joints (CGJ) as calibration pattern. Unfortunately, this procedure fails if the detection of the gaps is not accurate or if the contrast in the images is low. Here, we enhance the automated camera calibration algorithm with a reliable pattern detection and analyze quantitatively the quality of the process. Our method uses an iterative Hough transform to detect line structures and uses three key figures (KF) to separate detected busbars from cell gaps. This method allows a reliable identification of all cell gaps, even in noisy images or if disconnected edges in PV cells exist or potential induced degradation leads to a low contrast between active cell area and background. In our dataset, a subset of 30 EL images (72 cell each) forming grid ( 5 × 11 ) lead to consistent calibration results. We apply the calibration process to 997 single module EL images of PV modules and evaluate our results with a random subset of 40 images. After lens distortion correction and perspective correction, we analyze the residual deviation between ideal target grid points and the previously detected CGJ after applied distortion and perspective correction. For all of the 2200 control points in the 40 evaluation images, we achieve a deviation of less than or equal to 3 pixels. For 50% of the control points, a deviation of of less than or equal to 1 pixel is reached.

Keywords: PV modules; electroluminescence imaging; EL image processing; camera calibration; lens distortion; pattern detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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