Publication:
A Multi-Layer Deep Learning System for Fault Detection Solar Cell Electroluminescence Images

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dc.citedby0
dc.contributor.authorAlmashhadani R.en_US
dc.contributor.authorHock G.C.en_US
dc.contributor.authorHani Nordin F.en_US
dc.contributor.authorAbdulrazzak H.N.en_US
dc.contributor.authorAli Abbas Z.en_US
dc.contributor.authorid57223341022en_US
dc.contributor.authorid16021614500en_US
dc.contributor.authorid59558916700en_US
dc.contributor.authorid57210449807en_US
dc.contributor.authorid59558170700en_US
dc.date.accessioned2025-03-03T07:45:12Z
dc.date.available2025-03-03T07:45:12Z
dc.date.issued2024
dc.description.abstractAn automatic solar defect detection with a classification system was proposed using deep learning. This paper focuses on solar defects in photovoltaic systems identified through electroluminescence (EL) images. Convolutional Neural Networks (CNNS), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and a pre-trained VGG16 network for feature extraction were compared in this paper. They adopted two classification strategies: binary classification (defective or non-defective) and multi-class classification the class names are 0%, 33%, 67%, and 100% (here % represents the percentage of defectiveness), which represents the defect likelihood. The effectiveness of the model was evaluated using various metrics, including the recall, precision, accuracy, and F1-score for two and four classes and obtained on, supported by confusion matrices. VGG-16 model outperformed other models and achieved an accuracy of 89% for 2-classes and 93% for 4-classes. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICAST61769.2024.10856497
dc.identifier.scopus2-s2.0-85217832164
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85217832164&doi=10.1109%2fICAST61769.2024.10856497&partnerID=40&md5=a74fa60fa8a616a1b076433d2e906c9c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36852
dc.publisherIEEE Computer Societyen_US
dc.sourceScopus
dc.sourcetitleIEEE International Conference on Adaptive Science and Technology, ICAST
dc.subjectConvolutional neural networks
dc.subjectDeep neural networks
dc.subjectMultilayer neural networks
dc.subjectClassification system
dc.subjectConvolutional neural network
dc.subjectDefect detection
dc.subjectElectroluminescence images
dc.subjectFaults detection
dc.subjectMulti-layers
dc.subjectNeural-networks
dc.subjectPhotovoltaic systems
dc.subjectPhotovoltaics
dc.subjectVGG16
dc.subjectSupport vector machines
dc.titleA Multi-Layer Deep Learning System for Fault Detection Solar Cell Electroluminescence Imagesen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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