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...
Various defects in PV cells can lead to lower photovoltaic conversion efficiency and reduced service life and can even short circuit boards, which pose safety hazard risks . As a result, PV cell defect detection research offers a crucial assurance for raising the caliber of PV products while lowering production costs. Figure 1.
To demonstrate the performance of our proposed model, we compared our model with the following methods for PV cell defect detection: (1) CNN, (2) VGG16, (3) MobileNetV2, (4) InceptionV3, (5) DenseNet121 and (6) InceptionResNetV2. The quantitative results are shown in Table 5.
Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention.
Many methods have been proposed for detecting defects in PV cells , among which electroluminescence (EL) imaging is a mature non-destructive, non-contact defect detection method for PV modules, which has high resolution and has become the main method for defect detection in PV cells .
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.
Visualizing feature map (The figure illustrates the change in the feature map after the SRE module.) We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively capturing diverse defect features, particularly for small flaws.
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...
Abstract: The multiscale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as network deepens. To address …
This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the...
Photovoltaic (PV) cells convert sunlight directly to electricity. Fabricated from a wide variety of materials using many different processing techniques, these devices are used for terrestrial power generation, as well as commercial, military, and research space power applications. PV cell characterization involves measuring the cell''s electrical performance characteristics to …
The detection of defect types of photovoltaic (PV) panel is a crucial task in PV system. Existing detection models face challenges in effectively balancing the trade-off between detection accuracy and resource consumption. To address this issue, this paper proposes a new defect detection method for PV panel based on the improved YOLOv8 model ...
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively capturing diverse defect...
The detection of defect types of photovoltaic (PV) panel is a crucial task in PV system. Existing detection models face challenges in effectively balancing the trade-off …
Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention. However, existing methods ...
This paper focuses on defect detection in photovoltaic cells using the innovative application of deep learning techniques. Through extensive exploration and experimentation with a variety of deep learning models, we have gained valuable insights into the potential of these models to accurately classify PV cells as either defective or non-defective. The results …
Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we …
In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category …
The derived features from solar panel images provide a significant source of information for photovoltaic applications such as fault detection assessment. In this work, a method for classifying between the normal and a defective solar cell was implemented using EL imaging with selected digital image processing techniques through the Support ...
In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category weight assignment, which effectively mitigates the impact of the problem of scant data and data imbalance on model performance; (2) to propose a featur...
The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality …
Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we propose an enhanced YOLOv7-based deep learning framework for fast and accurate anomaly detection in PV cells.
Abstract: The multiscale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as network deepens. To address this problem, an attention-based top-down and bottom-up architecture is developed to accomplish multiscale feature fusion. This architecture, called bidirectional ...
The derived features from solar panel images provide a significant source of information for photovoltaic applications such as fault detection assessment. In this work, a method for …
This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the...
The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem ...
A solar cell (also known as a photovoltaic cell or PV cell) is defined as an electrical device that converts light energy into electrical energy through the photovoltaic effect. A solar cell is basically a p-n junction diode. Solar cells are a form of photoelectric cell, defined as a device whose electrical characteristics – such as current ...
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
Therefore, this paper proposes a high-efficiency photovoltaic cell defect detection method based on improved YOLOX. First, the transfer learning training strategy is adopted to accelerate model convergence, which can also avoid the problem of insufficient accuracy due to the small number of defect samples. Secondly, to suppress the interference ...
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They do not detect thin clear objects because the light can travel straight through the object to the receiver. 2) Retroreflective photoelectric sensor. Retroreflective Photoelectric Sensors have the emitter and receiver together in the same component. For the Retroreflective Sensor to work, the sensor''s emitter needs to be pointed at a reflector and aligned, so the light travels from the ...
applicable to micro-crack cell detection and not applicable to multiple defects classification. In [18], the authors applied deep neural networks for cracks and missing corners detection in solar cells. However, the dataset used in this method is small. In another research [19], the author employs a deep belief network for defect detection in ...
2.1 EL Test in photovoltaic cell defect detection . The principle of EL test in photovoltaic cell defect detection is that when a photovoltaic cell is electrifying positively, the electron and hole recombination releases power by emergent photon and an electroluminescent spectrum with 700-1200 nm wavelength is formed. Then the defect part of
Scanning probe microscopy (SPM) has enabled significant new insights into the nanoscale and microscale properties of solar cell materials and underlying working principles of photovoltaic and optoelectronic technology.
Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. …
Therefore, this paper proposes a high-efficiency photovoltaic cell defect detection method based on improved YOLOX. First, the transfer learning training strategy is …
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively capturing diverse defect...
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