Can automatic defects classification of PV cells be performed in electroluminescence images?
The present study focuses on automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN), are used for the solar cell defect classifications.
Is Automatic Defect Classification possible in PV cells?
Automatic defect classification in PV cells is presumed to be possible using CNN architecture and other feature extraction techniques such as histograms of oriented gradients (HOG), KAZE, SIFT, and speeded-up-robust features (SURF).
How are solar cell defects classified?
In the given study, solar cell defects are divided into seven classes: one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT), and speeded-up-robust features (SURF) are used to train the SVM classifier. The performance results are then compared.
What is a photovoltaic (PV) cell?
Photovoltaic (PV) power is generated when PV cell (i.e. solar cell) converts sunlight into electricity. As the industrial-level of PV cell, mono- and multi-crystalline silicon solar cells are taking the highest market share (over 97%) . In producing solar cells, invisible microcracks or defects in the Si wafer are common during process steps.
How do we classify defects of solar cells in electroluminescence images?
We classify defects of solar cells in electroluminescence images with two methods. One approach uses a support vector machine for fast results on mobile hardware. The second method with a convolutional neural network achieves even higher accuracy. Both methods allow continuous monitoring for defects that affect the cell output.
Can SVM and CNN be used to classify solar cell defects?
In this research, SVM (Support Vector Machine) and CNN (Convolutional Neural Network) methods are presented for the classification of solar cell defects using their features. The successful classification of defects in a polycrystalline silicon PV cell is a challenging task due to its background texture.
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E-ELPV: Extended ELPV Dataset for Accurate Solar Cells …
Starting from 44 EL images of photovoltaic (PV) modules, which consisted in 18 monocrystallyne modules and 26 polycrystalline modules, the work in [] proposed a segmentation strategy in order to extract the various cells from the modules this process, the authors were able to extract 2624 cells.
AUTOMATIC CLASSIFICATION OF DEFECTIVE …
In this project, we propose an automated classification strategy us-ing mainstream multi-class classification methods (e.g. Sup-port Vector Machines (SVM) and Random Forest …
Detection and classification of photovoltaic module defects …
This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module …
Attention classification-and-segmentation network for micro …
Micro-crack anomaly detection is a crucial part of the quality inspection of photovoltaic (PV) module cells. However, due to the complex background and the lack of sufficient anomaly samples, it ...
Papers with Code
The dataset contains 2,624 samples of $300times300$ pixels 8-bit grayscale images of functional and defective solar cells with varying degree of degradations extracted from 44 different solar modules. The defects in the annotated images are either of intrinsic or extrinsic type and are known to reduce the power efficiency of solar modules. All images are normalized with respect …
Automatic Classification of Defective Photovoltaic Module Cells …
Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge …
Anomaly detection in electroluminescence images of …
The ELPV dataset is an open dataset for the anomaly detection and classification of photovoltaic cells. This dataset was presented in Buerhop-Lutz ... Efficient cell segmentation from electroluminescent images of single-crystalline silicon photovoltaic modules and cell-based defect identification using deep learning with pseudo-colorization.
Solar photovoltaic panel cells defects classification using …
Four distinct variations are identified in the Electroluminescence Photovoltaic (ELPV) benchmark datasets [6]: functional, moderate, mild, and severe. The classifications …
Remote anomaly detection and classification of solar photovoltaic ...
PV modules in the industry are produced mainly by crystalline silicon (c-Si) technology with over 90% of the market. The crystalline silicon PV module contains glass on the surface, polymers in encapsulant and back sheet foil, aluminum in the frame, silicon in solar cells, copper in interconnectors, silver in contact lines, and other heavy metals such as tin and lead.
Automated defect identification in electroluminescence …
Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL …
Types of photovoltaic cells
Although crystalline PV cells dominate the market, cells can also be made from thin films—making them much more flexible and durable. One type of thin film PV cell is amorphous silicon (a-Si) which is produced by depositing …
Photovoltaic cell defect classification based on integration of ...
The classification module was used to discriminate non-defective PV cells from defective cells. They used the same dataset as Ge et al., 2021, Demirci et al., 2021 and their used dataset is divided into four main classes by determining the …
A benchmark dataset for defect detection and classification …
Benchmark IoU and recall metrics are provided for 5 of the 24 labelled classes. Electroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules …
Photovoltaic cell defect classification using …
Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as …
Segmentation of cell-level anomalies in ...
The first module (cell detection) takes an image of an entire PV module and extracts from it all the PV cells, detecting them one by one. Then, each cropped cell is processed on the second module (cell classification) and is labeled as non-defective or defective.
4.5. Types of PV technology and recent innovations
The polycrystalline cells are slightly less efficient (~12%). These cells can be recognized by their mosaic-like appearance. Polycrystalline cells are also very durable and may have a service life of more than 25 years. The cons of this type of PV technology are mechanical brittleness and not very high efficiency of conversion.
Photovoltaic cell defect classification using convolutional …
Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients …
Defect Detection in Photovoltaic Module Cell Using CNN …
Initially, the system performs a binary classification on the input images, distinguishing between defective and normal photovoltaic (PV) cells. Subsequently, defective …
CNN based automatic detection of photovoltaic cell defects …
Photovoltaic (PV) modules experience thermo-mechanical stresses during production and subsequent life stages. These stresses induce cracks and other defects in the modules which may affect the power output [1].Cell cracking is one of the major reasons for power loss in PV modules [2].Therefore, PV modules and cells need to be monitored during …
Automatic Classification of Defective Photovoltaic Module Cells …
Abstract: Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge …
Efficient deep feature extraction and classification for …
Using this dataset, (Deitsch et al., 2019) performed PV cell classification on the original dataset with 4-class (i.e. Non-defected, Possibly normal, Possibly defected and Defected). Classification with SVM and CNN is performed, and 82.44% and 88.42% accuracy is achieved for SVM and CNN, respectively.
A dataset of functional and defective solar cells extracted …
The dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of functional and defective solar cells with varying degree of degradations extracted from 44 different solar modules.
Classification of anomalies in electroluminescence images of solar PV ...
However, these cells may suffer from various anomalies like finger-interruptions, disconnections, cracks, breaks, etc. Such defects can seriously affect the output power of the PV module [4], [5]. To evaluate the PV degradation, the characterization methods can be applied using the I–V curve acquisition of the PV module''s electric properties.
Deep learning-based automated defect classification in ...
EL imaging is a state-of-art imaging technique employed to test PV cells and modules, that was originated by ... Feature Extraction, Supervised and Unsupervised Machine Learning Classification of PV Cell Electroluminescence Images. In 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE …
Automatic Classification of Defective Photovoltaic Module Cells …
In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are …
Automatic classification of defective photovoltaic module cells …
We classify defects of solar cells in electroluminescence images with two methods. One approach uses a support vector machine for fast results on mobile hardware. The second method with a convolutional neural network achieves even higher accuracy. Both methods …
What is Solar Module? Types of Solar Modules
2. Polycrystalline Solar Modules. PolyCrystalline solar modules are solar modules that consist of several crystals of silicon in a single PV cell. Polycrystalline PV panels cover 50% of the global production of modules. These modules are commonly used in Solar rooftop systems in Delhi, covering 50% of global module production. They are slightly ...
Automatic fault classification in photovoltaic modules using ...
A damage in the bypass diode can be observed by a heating in a series-connected string of cells. A PV module with defect in the bypass diode will have about 33% reduction in the power output in comparison to ... four different scenarios are considered: (1) detection of defects in PV modules, (2) classification of defects in PV modules using ...
Automatic Classification of Defective Photovoltaic …
In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are …
Automatic Classification of Defective Photovoltaic …
Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the ... PV cell by overlaying it with a grid consisting of n ncells. The center of each grid cell specifies the position at which a feature ...
Detection and classification of photovoltaic module defects …
Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four …
Defect detection and quantification in electroluminescence images of ...
In summary, a DC current is forced through a PV module or string of PV modules to generate electron-hole pairs in the device, simulating the effect of the photons when the module is exposed to sunlight. A specialized camera captures the image which is then analyzed manually or automatically for defect detection and classification.
Automatic Classification of Defective …
Qualitative defect classification results in a PV module previously not seen by the deep regression network. The red shaded circles in the top right corner of each solar cell specify the ground ...
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