Classification of silicon solar cells using Electroluminescence texture analysis

Bastari, Alessandro; Bruni, Andrea; Cristalli, Cristina

doi:10.1109/isie.2010.5636322

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…In the process of production and processing of solar cells, due to various factors such as process defects and production environment, there are some defects in solar cells, which will reduce the conversion efficiency of solar cells. Using electroluminescence detection technology [5], the detected surface defects of battery chips mainly include the following types: over welding, fragments, hidden cracks, process pollution, and the frequency of defects.…”

Section: Methodsmentioning

confidence: 99%

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Detection method of solar cell surface defects based on C4.5-G algorithm

Jin¹,

Liu²,

He³

2023

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023)

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Section: Methodsmentioning

confidence: 99%

“…• Since information gain tends to select attributes with more values, in order to reduce the deviation caused by information gain, C4.5 algorithm introduces split information as a penalty factor, as shown in Equation ( 4), and then obtains the information gain ratio, as shown in Equation (5).…”

Section: C45 Algorithmmentioning

confidence: 99%

Detection method of solar cell surface defects based on C4.5-G algorithm

Jin¹,

Liu²,

He³

2023

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023)

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“…In the intelligent phase of fault detection, the intelligent PV panel fault detection method based on machine vision is now being gradually adopted by users. By machine vision detection, Bastari et al [28] proposed a method for texture analysis of dark defect areas in electroluminescence images to classify defective and non-defective cells. Quarter et al [29] first reported the UAV inspection system to perform non-destructive inspection of large-scale PV power plants.…”

Section: Pv Panel Fault Detectionmentioning

confidence: 99%

A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel

Niu

Zhang

et al. 2022

Applied Sciences

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Photovoltaic (PV) panels are widely adopted and set up on residential rooftops and photovoltaic power plants. However, long-term exposure to ultraviolet rays, high temperature and humid environments accelerates the oxidation of PV panels, which finally results in functional failure. The traditional fault detection approach for photovoltaic panels mainly relies on manual inspection, which is inefficient. Lately, machine vision-based approaches for fault detection have emerged, but lack of negative samples usually results in low accuracy and hinders the wide adoption of machine vision-based approaches. To address this issue, we proposed a semi-supervised anomaly detection model based on the generative adversarial network. The proposed model uses the generator network to learn the data distribution of the normal PV panel dataset during training. When abnormal PV panel data are put into the model in the test phase, the reconstructed image generated by the model does not equal the input image. Since the abnormal PV panel data do not obey the data distribution learned by the generator, the difference between the original image and its reconstructed image exceeds the given threshold. So, the model can filter out the fault PV panel by checking the error value between the original image and its reconstructed image. The model adopts Gradient Centralization and SmoothL1 loss function to improve its generalization performance. Meanwhile, we use the convolutional block attention module (CBAM) to make the model pay more attention to the defective area and greatly improve the performance of the model. In this paper, the photovoltaic panels dataset is collected from a PV power plant located in Zhejiang, China. We compare the proposed approach with state-of-the-art semi-supervised and unsupervised approaches (i.e., AnoGAN (Anomaly Detection with Generative Adversarial Networks), Zhao’s method, GANomaly, and f-AnoGAN), and the result indicates that the Area Under Curve (AUC) increases by 0.06, 0.052, 0.041 and 0.035, respectively, significantly improving the accuracy of photovoltaic panel fault detection.

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“…Tsai et al [4] proposed a self-referential method based on Fourier reconstruction technology to detect solar cell defects. Bastari et al [5] proposed a texture analysis of dark color defect areas in electroluminescent images.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning

Ni¹,

Zou²,

Li³

et al. 2018

Proceedings of the 2018 International Conference on Transportation &Amp; Logistics, Information &Amp; Communication, Smart City

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Currently, photovoltaic module manufacturers still rely on manual detection of EL images of photovoltaic modules to identify hidden defects. EL image detection is an important link in the quality control of photovoltaic modules production. Manual detection leads to slow detection speed, and the accuracy is affected by personal subjective judgment. In this paper, an intelligent defect detection method based on deep learning is proposed. The method first builds a network according to the sample characteristics. The initial network value is obtained through training. Then, the neural algorithm is used to adjust the network parameters to obtain the mapping relationship between training samples and defect-free templates. Finally, the comparison between reconstructed image and defect image is used to realize defect detection of test samples. Experiments show that the method based on deep learning can detect defects accurately and quickly.

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Classification of silicon solar cells using Electroluminescence texture analysis

Cited by 11 publications

References 10 publications

Detection method of solar cell surface defects based on C4.5-G algorithm

Detection method of solar cell surface defects based on C4.5-G algorithm

A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel

Intelligent Defect Detection Method of Photovoltaic Modules Based on Deep Learning

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