Electroluminescence imaging of PV devices: Advanced vignetting calibration

IEEE Electroluminescence (EL) imaging is affected by off-axis illumination together with sensor and lens imperfections. The images’ spatial intensity distribution is mainly determined by the vignetting effect. For quantitative EL imaging, its correction is essential. If neglected, intensities can vary significantly (>50%) across the image. This paper introduces and tests four vignetting measurement methods. The quantitative comparison of different methods shows that vignetting should be characterized preferably in plane by the source of the same type as the photovoltaic (PV) device to be tested. A direct PV-based measurement in short distance with spatial inhomogeneity correction is proposed for general-purpose vignetting characterization. For precise vignetting characterization, vignetting-object separation using pattern recognition is proposed. The use of non-PV light sources for vignetting characterization can cause vignetting overcorrection and can even decrease the quality of the vignetting-corrected images

1st International round robin on EL imaging: automated camera calibration and image normalisation

Results from the first international Round Robin on electroluminescence (EL) imaging of PV devices are presented. 17 Laboratories across Europe, Asia and the US measured EL images of ten commercially available modules and five single-cell modules. This work presents a novel automated camera calibration and image scaling routine. Its performance is quantified through comparing intensity deviation of corrected images and their cell average. While manual calibration includes additional measurement of lens distortion and flat field, the automated calibration extracts camera calibration parameters (here: lens distortion, and vignetting) exclusively from EL images. Although it is shown that the presented automated calibration outperforms the manual one, the method proposed in this work uses both manual and automated calibration. 501 images from 24 cameras are corrected. Intensity deviation of cell averages of every measured device decreased from 10.3 % (results submitted by contributing labs) to 2.8 % (proposed method), For three images the image correction produced insufficient results and vignetting correction failed for one camera, known of having a non-linear camera sensor. Surprisingly, largest image quality improvements are achieved by spatially precise image alignment of the same device and not by correcting for vignetting and lens distortion. This is due to overall small lens distortion and the circumstance that, although vignetting caused intensity reduction of more than 50%, PV devices are generally positioned in the image centre in which vignetting distortion is lowest