Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

Composite materials are widely used in aerospace industries due to their light weight, strength, and various other desired properties. However, they are susceptible to various defects occurring during the manufacturing process or in service. Typical defects include porosity, wrinkles, and delamination. Nondestructive means of detection of the defects at any stage are of great importance to ensure quality and safety of composite structures. A nonintrusive removable Lamb wave system and accompanied methodology that is not material dependent are presented in this paper to detect various types of typical defects in laminated composite plates, flat or curved. Through multidimensional data acquisition and processing, abnormality in waves caused by defects is captured and presented in inspection images. The methodologies are demonstrated in two cases: delamination in a curved plate and wrinkles in a flat plate. Overall, the results show that Lamb waves using the piezoelectric transducer and laser vibrometer system can be used for various types of defects inspection in flat or curved composite plates.

Deep learning breakthrough stimulates the new research trend in infrastructure defects inspection. The lack of quality-controlled, human-annotated, free of charge, and publicly available defect datasets with sufficient amounts of data hinders the progress of deep learning in defects inspection. To boost research in deep learning-based defects inspection, we first summarize 37 publicly available defect datasets in this two-part survey. These defect datasets cover common defects in various types of infrastructure, and the taxonomy of the datasets is based on specific deep learning objectives. Besides, taking crack as the research target, we have combined the existing datasets with self-labeled crack images to establish a benchmark dataset for crack classification and segmentation. Moreover, based on the established crack dataset, we make a comprehensive comparison between state-of-the-art algorithms for classification, segmentation, and detection. In this paper, we concentrate on datasets and algorithms for defect classification. Altogether 11 classification-oriented defect datasets are summarized and demonstrated in detail. Based on the established crack dataset, we comprehensively compare existing state-of-the-art algorithms for object classification, which provides a baseline for future research in defects inspection. The companion paper of this work surveys datasets and algorithms for defect segmentation and detection, where 26 defect datasets are elaborated, and systematic comparison between state-of-the-art algorithms has been conducted. The classification algorithms illustrated in this paper are conducive to the coarse localization of the defects' position in images.

Bearing defect inspection based on machine vision

Deep learning based automatic defect identification of photovoltaic module using electroluminescence images

This paper reports a simple and systematic experiment to distinguish various types of defects in bonded wafer pairs with the dynamic blade insertion (DBI) method and real-time observation. In addition, a type of defect was observed in bonded wafer pairs after they were heated to 1273 K and unevenly cooled. It exhibits a fairly large size with coarse Newton-ring contours in IR observation. During DBI examination, these defects exhibit quite a different behavior from other types of defects (void, dust particle, etc.) with the significant intention of resisting the movement of the approaching blade. However, this defect demonstrates a very strong bonding strength in tensile testing.

This chapter describes the most important features of computational software called ‘McChasy’, which is a Monte Carlo (MC) simulation code developed for the evaluation of the Rutherford Backscattering Spectrometry data, in particular, recorded in the channeling mode (RBS/C). RBS/C is an experimental technique used in the analysis of defects in single crystals. Lattice distortions affect materials modified by ion beams or exposed to irradiation. Therefore, the analysis of damage in crystals is of high importance in materials science. Various types of defects can be created due to the interaction of ions with targets. However, RBS/C has different sensitivity to each of them so the analytical analysis of experimental data is hardly possible. MC simulations are a powerful tool used to overcome this limitation. The McChasy code simulates the movement of light ions in crystals. The software provides a fitting procedure of RBS/C spectra based on independent depth profiles of different defect types: interstitials, edge dislocations, substitutions, stacking faults or grain boundaries. The code works well not only with materials containing complex defects but also with heterostructures and superlattices. Recent improvements of the code include a unique approach of 3D-interaction between ions and target atoms. Application of the McChasy code in the analysis of crystal defects is described and possible ways of its further development are pointed out.

Electroluminescence (EL) imaging is a photovoltaic (PV) module characterization technique, which provides high accuracy in detecting defects and faults, such as cracks, broken cells interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density. However, this technique is commonly practiced only indoors—or outdoors from dusk to dawn—because the crystalline silicon luminescence signal is several orders of magnitude lower than sunlight. This limits the potential of such a powerful technique to be used in utility scale inspections, and therefore, the interest in the development of electrical biasing tools to make outdoor EL imaging truly fast and efficient. With the focus of quickly acquiring EL images in daylight, we present in this article a drone-based system capable of acquiring EL images at a frame rate of 120 frames per second. In a single second during high irradiance conditions, this system can capture enough EL and background image pairs to create an EL PV module image that has sufficient diagnostic information to identify faults associated with power loss. The final EL images shown in this work reached representative quality SNRAVG of 4.6, obtained with algorithms developed in previous works. These drone-based EL images were acquired with global horizontal solar irradiance close to one sun in the plane of the array.

Automated defect identification in electroluminescence images of solar modules

Computer imaging of electroluminescence (EL) has been successfully applied to solar cell inspection in recent years, as EL image intensities reflect the efficiency levels and/or defects in sc-Si and mc-Si solar cells. In this paper, we propose a novel computational scheme for pseudo colorization of EL images to highlight defect regions in solar cells for human inspection. Specifically, given a template EL image and pseudo color labels on its defect regions, we impose the pseudo colors to other grayscale EL images, with respect to different defect types and image structures, by template feature clustering and pseudo color transferring. Our experiments show that the proposed approach indeed improves the readability of EL images and provides better visualization of solar cell defects.

Photovoltaic (PV) cells can be characterized using current-voltage (I-V) as well as electroluminescence (EL) characteristics. However, unlike I-V, the potential of EL characteristics has not been well discussed in the literature. In this work, the impact of different types of defects on the EL characteristics is studied, and the possible use of EL characteristics in the qualitative and quantitative investigation of defects is unveiled. Defective field-aged and new modules with artificially induced defects were taken for experimental study. EL current-intensity (I-ɸ) and EL voltage-intensity (V-ɸ) characteristics were studied for the cells suffering from major defects such as cracks, busbar interconnect failure, and PID-shunting. It is observed that I-ɸ characteristic is useful in the investigation of shunting defects, whereas V-ɸ characteristic is in diagnosing defect that increases series resistance. Bulk defects within a cell affect EL calibration constants and increase cell-to-cell mismatch within a module. The findings of this work would be useful for extracting valuable information from EL images regarding defects and degradations.

Electroluminescence (EL) imaging is one of the most common characterisation techniques for photovoltaic cells and modules. EL images contain information on both the open-circuit voltage (Voc) and series resistance (Rs) of the device. However, separating the two effects and identifying features related to each parameter can be challenging. In this study, a novel approach for decomposing EL images into Voc and Rs maps using a convolutional neural network architecture is presented. A deep learning model was first trained on paired EL and photoluminescence images that were generated using a simulation tool. Results obtained using the validation set show that the trained model is able to accurately differentiate between features related to Voc and Rs in EL images, thus replacing the need for multiple types of measurements. The proposed method presents a unique approach to analyse EL images, unlocking new capabilities that have the potential to advance solar cell characterisation.

In the field of photovoltaics, electroluminescence (EL) imaging has proven useful in determining the degradation of solar panels, primarily through use of expensive equipment within a confined lab setting. This study explored the potential of conducting EL imaging using relatively inexpensive equipment. Factors that heavily influenced the quality of EL images included voltage/current settings for the purpose of forward biasing the test panel, camera settings (aperture, shutter speed, and ISO), and amount of external light (not EL) detected by the camera. Once EL images were captured, the images with the best quality were analyzed and modified through image processing and photo editing. The quality of the final images was then validated through comparison with images captured in a controlled lab with higher quality equipment. Moreover, the results of transporting a solar panel are shown to be clearly observable using low-cost EL imaging.

Cracks in solar cells are one of the problems responsible for low efficiency among photovoltaic (PV) modules and these cracks are hard to avoid. Cracks usually can occur during transportation, handling or manufacturing process. Micro cracks degrade the power output in photovoltaic modules due to the inactive cell area. Micro-cracks are invisible to detect by naked eye and hence the Electroluminescence (EL) imaging were introduced to analyse the cracks in PV module. The electroluminescence is the most useful method to detect the cracks in the solar cell. A si-CCD camera was used in the dark condition to capture the EL image under forward bias conditions. In this study, mono-crystalline and multicrystalline were taken from the outdoor condition which were exposed for almost eight years and were analysed using electroluminescence imaging. From the electroluminescence imaging, the cracks were measured. The cracks were analysed by pattern and both panels were compared. From the study it was found that multicrystalline photovoltaic module shows the more crack compared with monocrystalline photovoltaic module. The crack in the individual solar cell and their relative efficiency in the two different types of crystalline modules have been also been presented.

Visualization of X-ray Fourier images of deformation fields for various types of defects in monocrystals

Defect engineering in the MOSLED structure by ion implantation