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Deep learning based automatic defect identification of photovoltaic module using electroluminescence images
摘要： The maintenance of large-scale photovoltaic (PV) power plants is considered as an outstanding challenge for years. This paper presented a deep learning-based defect detection of PV modules using electroluminescence images through addressing two technical challenges: (1) providing a large number of high-quality Electroluminescence (EL) image generation method for the limit of EL image samples; and (2) an efficient model for automatic defect classification with the generated EL image. The EL image generation approach combines traditional image processing technology and GAN characteristics. It can produce a large number of EL image samples with high resolution using a limited number of samples. Then, a convolution neural network (CNN) based model for the automatic classification of defects in an EL image is presented. CNN is used to extract the deep feature of the EL image. It can greatly increase the accuracy and efficiency of PV modules inspection and health management in comparison with the other solutions. The proposed solution is assessed through extensive experiments by using the existing machine learning models, VGG16, ResNet50, Inception V3 and MobileNet, as the comparison benchmarks. The numerical results confirm that the proposed deep learning-based solution can carry out efficient and accurate defect detection automatically using the electroluminescence images.
关键词： Automatic defect classification,Electroluminescence Images,Generative adversarial network,Convolution neural network
更新于2025-09-23 15:19:57
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Photovoltaic defect classification through thermal infrared imaging using a machine learning approach
摘要： This study examines a deep learning and feature-based approach for the purpose of detecting and classifying defective photovoltaic modules using thermal infrared images in a South African setting. The VGG-16 and MobileNet models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with a random forest classifier, is used to identify defective photovoltaic modules. The implementation of this approach has potential for cost reduction in defect classification over current methods.
关键词： photovoltaic,SIFT,machine learning,defect classification,random forest,deep learning,support vector machine,defect detection,infrared thermography
更新于2025-09-12 10:27:22
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Intelligent classification of silicon photovoltaic cell defects based on eddy current thermography and convolution neural network
摘要： Defects the production process of silicon photovoltaic (Si-PV) cells are urgently needed to be detected due to their serious impact on the normal generation of PV system. In view of the shortcomings such as low defect efficiency, few detection data and high detection error rate in the existing industrial production line, the main research purpose of this study is to complete an intelligent classification method for efficient and innovative defect detection for Si-PV cells and modules. The purpose is to improve the detection efficiency of Si-PV cell, to ensure the safety and reliability of Si-PV cell production process, to achieve large number of Si-PV cell defects detection and classification. Firstly, the Eddy Current Thermography (ECT) system of Si-PV cells was established. Secondly, Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF) algorithms are compared for thermography sequences processing. Thirdly, LeNet-5, VGG-16 and GoogleNet models are compared for Si-PV cell defects classification. Finally, the results showed that the proposed method have successful application in Si-PV cell defects detection and classification.
关键词： Nondestructive testing & evaluation,Defect feature extraction,Defect classification,Convolution neural network,Silicon photovoltaic cell,Eddy current thermography
更新于2025-09-11 14:15:04