CNN based automatic detection of photovoltaic cell defects in electroluminescence images

Akram, Muhammad Waqar; Li, Guiqiang; Jin, Yi; Chen, Xiao; Zhu, Changan; Zhao, Xudong; Khaliq, Abdul; Faheem, Muhammad; Ahmad, Ashfaq

doi:10.1016/j.energy.2019.116319

Cited by 206 publications

(99 citation statements)

References 18 publications

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“…Table 4 compares the F1 scores obtained in this study and scores reported in the literature for thorough analysis. The obtained F1 scores of V1 are higher than the scores reported for the isolated in-depth training and transfer learning approaches [8,14]. They are also higher than those obtained by the Kaze/VGG feature vector combined with an SVM classifier and spectral clustering algorithm [14,21].…”

Section: Results and Performance Discussioncontrasting

confidence: 54%

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Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

et al. 2020

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A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.

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Section: Results and Performance Discussioncontrasting

confidence: 54%

“…The cracks can form due to mechanical stress during transportation or manufacturing installation and maintenance of the modules [7]. A brief classification of the different crack modes is provided in Reference [8].…”

Section: Introductionmentioning

confidence: 99%

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

et al. 2020

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“…The purpose of automatic detection is to replace the manual inspection in production line, and it has two requirements: (1) different types of defects should be concerned, and (2) every single defect should be localized and classified, which is essentially an object detection task. But current researches only studied crack, break, and finger interruption 4–16 and cannot handle localization problem well for multitype defects, which is what we aimed to achieve in this paper. We sum up our main contributions as follows: (1) we gathered 5983 EL images of defective modules and labeled all of them, with 19 categories of defects found and introduced.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, many researches aiming to achieve automatic detection of defects in EL images have been done in the past decade. These studies can be divided into two groups according to their approaches: using conventional signal processing algorithms 4–8 and using artificial intelligence (AI) techniques 9–16 …”

Section: Introductionmentioning

confidence: 99%

“…Mayr et al 10 used weakly supervised learning based on ResNet50 to segment cracks in EL images and proved effective especially when the data size is limited. Akram et al 11 introduced five types of defects and did normal/defective classification of cells, but they could not detect and localize specific defects. Tang et al 12 combined generative adversarial networks with data augmentation to generate more training data and achieved good classification accuracies for images of defect‐free (84%), micro‐crack (82%), finger‐interruption (81%), and break (83%).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line

Yang

Zhan²,

Wang³

et al. 2021

Progress in Photovoltaics

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Automatic defect detection in electroluminescence (EL) images of photovoltaic (PV) modules in production line remains as a challenge to replace time‐consuming and expensive human inspection and improve capacity. This paper presents a deep learning‐based automatic detection of multitype defects to fulfill inspection requirements of production line. At first, a database composed of 5983 labeled EL images of defective PV modules is built, and 19 types of identified defects are introduced. Next, a convolutional neural network is trained on top‐14 defects, and the best model is selected and tested, achieving 70.2% mAP50 (mean average precision with at least 50% localization accuracy). Then, through analyzing an object detection‐based confusion matrix, recognition bias and detection compensation in missed defects that restrain the best model's mAP50 are discovered to be harmless to normal/defective module classification in real production line. Finally, after setting specific screen criteria for different types of defects, normal/defective module classification is conducted on additionally collected 4791 EL images of PV modules on 3 days, and the best model achieves balanced scores of 95.1%, 96.0%, and 97.3%, respectively. As a result, this method surely has a highly promising potential to be adopted in real production line.

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Machine Learning for Photovoltaic Applications

Dwivedi,

Hameiri

2024

Photovoltaic Solar Energy

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CNN based automatic detection of photovoltaic cell defects in electroluminescence images

Cited by 206 publications

References 18 publications

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line

Machine Learning for Photovoltaic Applications

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