Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells. Thus, it is necessary to detect these defects and reject the defected ones from solar cells production line. Various inspection methods have been proposed based on contact and non-contact methods.
Solar cells or photovoltaic systems have been extensively used to convert renewable solar energy to generate electricity, and the quality of solar cells is crucial in the electricity-generating process. Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells.
Abstract Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex back- ground is a challenge of solar cell manufacturing. The tradi- tional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect.
Because the finger lines are as emblems and located on background of solar cell, definitely any defect (crack, hole, etc.) is appeared on the finger lines or background. In this case, defect recognition is more challenging due to misdetection.
For the specific defect types like scratches or color difference, too few images of certain defective solar cells are an important cause of the decline in multi-classification effects. But in industrial manufacturing, different types of defective solar cells will be collected and reduced to the defective grade.
One of typ- ical features of these defects is that they have different charac- teristics in different spectral ranges. Many existing solar cell defect detection methods focus on the analysis of electroluminescence (EL) infrared images un- der 1000nm-1200nm wave length.
Therefore, surface defect detection of solar cells plays a key role in controlling the quality of solar cell products during manufacturing process . As machine vision develops rapidly, an image-based defect detection method has been employed for solar cell surface quality controlling in manufacturing industry.
Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells. Thus, it is necessary to detect these defects and reject the defected ones from solar cells production line. Various inspection methods have been proposed based on contact and non-contact methods.
Solar cells with defects should be detected and eliminated in time to avoid the quality damage of solar cell module in the next step of production. Therefore, surface defect detection of solar cells plays a key role in controlling the quality of solar cell products during manufacturing process [1] .
However, in the manufacturing process, solar cells will have some surface de-fects, including broken gates, pasting spot, thick lines, dirty cells, missing corners, scratches, chromatic …
EL test reveals PV cell defects such as micro cracks, broken cells, finger interruptions and provides detailed information about production quality. In recent years, automated detection and classification systems using deep neural networks for PV module inspection have gained increasing attention. However, deep learning-based systems usually …
Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells. Thus, it is necessary to detect these defects and reject the defected …
Point defects, such as Schottky and Frenkel defects, can contribute to the formation of trap states in perovskite solar cells (PSCs). These defects introduce localized energy levels within the bandgap of the perovskite material, resulting in shallow and deep trap states.
This review provides a summary of defects in photovoltaic technology regarding perovskite solar cells and passivation strategies, as well as the latest research results and future directions. The sources of defects in PSCs involve the occurrence of voids, gaps, antisite defects, composite defects, and carrier migration in the crystals produced ...
In this paper, data analysis methods for solar cell defect detection are categorised into two forms: 1) IBTs, which depend on analysing the deviations of optical …
innovative technology for optimizing solar energy production. The author in [4] presents an innovative solar cell defect detection system emphasizing portability and low computational power. The research utilizes K-means, MobileNetV2, and linear discriminant algorithms to cluster solar cell images and create customized detection models for each cluster. This method …
Point defects, such as Schottky and Frenkel defects, can contribute to the formation of trap states in perovskite solar cells (PSCs). These defects introduce localized …
Only recently, methods for quality control of multicrystalline silicon wafers have been published, which allow the efficiency of solar cells to be predicted precisely from photoluminescence (PL) images taken in the as‐cut state. In this letter it is shown that oxygen precipitates, present in standard Czochralski silicon wafers, can cause efficiency losses of more than 4% (absolute) …
Traditional vision methods for solar cell defect detection have problems such as low accuracy and few types of detection, so this paper proposes an optimized YOLOv5 model for more accurate and comprehensive identification of defects in solar cells.
This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our …
Degradation issues identified in new cell technologies such as TOPCon and HJT underscore the importance of module stability as well as efficiency. Tom Kenning reports on the testing of...
However, in the manufacturing process, solar cells will have some surface de-fects, including broken gates, pasting spot, thick lines, dirty cells, missing corners, scratches, chromatic aberrations, etc. Solar cells with defects should be detected and eliminated in time to avoid the quality damage of solar cell module in the next step of product...
Photovoltaic cells represent a pivotal technology in the efficient conversion of solar energy into electrical power, rendering them integral to the renewable energy sector 1.However, throughout ...
This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and ...
A solar cell, also known as a photovoltaic cell (PV cell), is an electronic device that converts the energy of light directly into electricity by means of the photovoltaic effect. [1] It is a form of photoelectric cell, a device whose electrical characteristics (such as current, voltage, or resistance) vary when it is exposed to light.. Individual solar cell devices are often the electrical ...
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