Before the emergence of deep learning techniques, various traditional methods were employed for anomaly detection in photovoltaic (PV) cells. These methods can be broadly categorized into two groups: statistical analysis, and signal processing.
Arena et al. proposes a robust anomaly detection method for the photovoltaic production factory scenario using Monte Carlo based pre-processing, principal component analysis, and key performance indicators to isolate anomalous conditions and trigger an alarm when exceeding a reference threshold.
Statistical analysis methods rely on the mathematical properties of the data to identify anomalies. Common techniques used for PV cell anomaly detection include hypothesis testing, regression analysis, and control charts.
More advanced CNN-based models are detection networks, such as Faster R-CNN, and YOLO, have been employed for anomaly detection in PV cells by leveraging their object detection capabilities .
In this paper, we have presented a novel PSA-YOLOv7 framework for fast anomaly detection of photovoltaic (PV) cells. We incorporate advanced techniques such as Partial Convolution and Switchable Atrous Convolution to address the challenges associated with irregular defects and defects of varying sizes.
Photovoltaic (PV) cells, which convert sunlight into electricity, play a pivotal role in harnessing solar energy . As the demand for solar power systems grows globally, ensuring the optimal performance and longevity of PV cells becomes increasingly important.
PVEL-AD：,IEEE …
We build a PV EL Anomaly Detection (PVEL-AD 1, 2, 3 ) dataset for polycrystalline solar cell, which contains 36_543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly free images and anomalous images with ten different categories.
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Segmentation of cell-level anomalies in ...
Current state-of-the-art detection methods extract barely low-level information from individual PV cell images, and their performance is conditioned by the available training data. In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images.
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Detection of failures in electrode-photovoltaic cell junctions …
Although methods like I-V curve analysis and infrared thermography are useful for detecting electrical and thermal anomalies, they have limits in terms of spatial resolution …
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A photovoltaic cell defect detection model capable of topological ...
The process of detecting photovoltaic cell electroluminescence (EL) images using a deep learning model is depicted in Fig. 1 itially, the EL images are input into a neural network for feature ...
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Fast object detection of anomaly photovoltaic (PV) cells using …
In this paper, we propose an enhanced YOLOv7-based deep learning framework for fast and accurate anomaly detection in PV cells. Our approach incorporates Partial Convolution, Switchable Atrous Convolution and novel data augmentation techniques to address the challenges of varying defect sizes, complex backgrounds.
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A PV cell defect detector combined with transformer and attention ...
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and costly...
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Photovoltaics Cell Anomaly Detection Using Deep Learning …
Anomaly and defect detection in PV cells can be performed through a variety of methods, including visual inspection, electrical testing, and computer-based image analysis. …
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Anomaly detection in electroluminescence images of …
Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic …
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PVELAD【】_pvel-ad-C…
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
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Segmentation of cell-level anomalies in electroluminescence images …
In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. The proposed modular pipeline combines three deep learning techniques: 1. object detection (modified Faster-RNN ), 2. image classification ( EfficientNet ) and 3 ...
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Research on temperature anomalies caused by the shading of …
The conclusions drawn from this model were unexpected, as partially shaded photovoltaic cells entered the hotspot environment faster than fully shaded cells, providing useful insights for photovoltaic array design. For instance, an array composed of smaller cells may outperform an array composed of larger cells.
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PVELAD【】_pvel-ad-C…
PVEL-AD 36,543 ,1 12 , （）、、、、、、、。 , 12 40000+ 。 Industrial_Data_Access_Form.docx, …
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Detection of failures in electrode-photovoltaic cell junctions …
Although methods like I-V curve analysis and infrared thermography are useful for detecting electrical and thermal anomalies, they have limits in terms of spatial resolution and the ability to discover microstructural flaws. Thermography, for example, is extremely sensitive to temperature changes, yet it could miss non-thermal flaws in solar cells. I-V analysis lacks the …
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Segmentation of cell-level anomalies in electroluminescence …
In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. …
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(PDF) Photovoltaic module dataset for automated fault detection …
PVMD_Dataset of thermal anomalies in solar cells of photovoltaic module.pdf. Content uploaded by Rotimi-Williams Bello. Author content. All content in this area was uploaded by Rotimi-Williams ...
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CNN-based automatic detection of photovoltaic solar module anomalies …
Liu Q, Liu M, Wang C, and Wu QJ An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images Sol Energy 2024 267 Crossref Google Scholar
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Photovoltaic System Thermal Inspection using Computer vision
Thermal anomalies, such as hotspots or temperature variations, can indicate potential issues within the panels, such as cell defects, shading, soiling, or electrical mismatches. The goal of photovoltaic system thermal inspection is to detect and diagnose problems early, allowing for timely maintenance or repairs to optimize the system''s performance and prevent …
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Photovoltaic Cells Anomaly Classification Using Deep Learning ...
Deep neural networks can accurately predict anomalies and classify types of anomalies. Using PV cell electroluminescence, we propose convolution neural network techniques based on …
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PVEL-AD：,IEEE …
We build a PV EL Anomaly Detection (PVEL-AD 1, 2, 3 ) dataset for polycrystalline solar cell, which contains 36_543 near-infrared images with various internal …
Learn More
Photovoltaic Cells Anomaly Classification Using Deep Learning ...
Photovoltaic Cells Anomaly Classification Using Deep Learning Techniques ... Deep neural networks can accurately predict anomalies and classify types of anomalies. Using PV cell electroluminescence, we propose convolution neural network techniques based on residual network architecture and ensemble technology to accurately predict and classify anomalous …
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Photovoltaic Cell Anomaly Detection Enabled by Scale …
In this study, we introduce a novel framework for anomaly detection in the PV panel systems, leveraging multiscale linear attention and scale distribution alignment learning (MLA-SDAL). Initially, we employ a feature extraction framework based on the multihead linear attention to facilitate the deep-level feature modeling. This ...
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[2106.10962] Segmentation of cell-level anomalies in ...
In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components'' life. Of all defects, cell-level anomalies can lead to serious failures and may affect surrounding PV modules in the long run. These fine defects are usually captured with high spatial resolution …
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Anomaly detection in electroluminescence images of …
Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and anomaly detection of electroluminescent images for solar cell quality evaluation.
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[2106.10962] Segmentation of Cell-Level Anomalies in ...
A photovoltaic (PV) panel can have different types of anomalies depending on the element it affects and the loss of productivity it causes. Major anomalies such as panel degradation, electrical disconnection or hot spots, cause heat emission under abnormal functioning, and thus the damaged areas can be easily revealed using infrared imagery (IR).On the other hand, cell …
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JOURNAL OF LA PVEL-AD: A Large-Scale Open-World Dataset for …
Because, some anomalies are often in the interior of photovoltaic cells, which cannot be photographed directly by optical or infrared camera. But visual inspection using EL imaging technology allows to easily identify anomaly inflicted to solar cells either by external environmental influences such as bump during the manufacturing process, or due to the prior …
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Photovoltaic Cells Anomaly Classification Using Deep Learning ...
Deep neural networks can accurately predict anomalies and classify types of anomalies. Using PV cell electroluminescence, we propose convolution neural network techniques based on residual network architecture and ensemble technology to accurately predict …
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Photovoltaics Cell Anomaly Detection Using Deep Learning …
Anomaly and defect detection in PV cells can be performed through a variety of methods, including visual inspection, electrical testing, and computer-based image analysis. These methods aim to identify and classify different types of anomalies and defects, including cracks, hotspots, broken cells, shading, etc.
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Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution ...
In this study, we introduce a novel framework for anomaly detection in the PV panel systems, leveraging multiscale linear attention and scale distribution alignment learning …
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