Cover Image

Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification

In this study, an automatic solar defect detection and classification system using deep learning was proposed. This study focuses on solar faults in photovoltaic systems identified through Electroluminescence (EL) images by employing a deep learning framework that utilizes both traditional Convoluti...

Full description

Saved in:
Bibliographic Details
Main Authors: Almashhadani R.A.I., Hock G.C., Nordin F.H.B., Abdulrazzak H.N.
Other Authors: 57223341022
Format: Article
Published: Seventh Sense Research Group 2025
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-36635
record_format dspace
spelling my.uniten.dspace-366352025-03-03T15:43:33Z Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification Almashhadani R.A.I. Hock G.C. Nordin F.H.B. Abdulrazzak H.N. 57223341022 16021614500 25930510500 57210449807 In this study, an automatic solar defect detection and classification system using deep learning was proposed. This study focuses on solar faults in photovoltaic systems identified through Electroluminescence (EL) images by employing a deep learning framework that utilizes both traditional Convolutional Neural Networks (CNNs) and a pre-trained VGG16 and VGG-19 network for feature extraction. This approach was designed to enhance the accuracy and efficiency of solar defect classification. The framework is structured into three main phases: image preprocessing, feature extraction using CNNs, Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN), and classification through a Deep Neural Network (DNN). During preprocessing, images are scaled down to uniform dimensions to ensure consistent learning. They adopted two classification strategies: binary classification (defective or non-defective) and multiclass classification; the class names are 0%, 33%, 67%, and 100% (here, % represents the percentage of defectiveness), which represents the defect likelihood. To refine the model?s performance, a data augmentation technique has been utilized on the dataset. The effectiveness of the model was evaluated using various metrics, including the precision, recall, F1-score, and accuracy for two and four classes and obtained on, supported by confusion matrices. VGG-19 model outperformed other models and achieved precision, recall, F1-score and accuracy of 90% each for two classes respectively and similarly 94% for four classes. This study compares two classification methods to assess the ability of the deep learning framework to detect and classify solar defect images automatically. ? 2024 Seventh Sense Research Group. All rights reserved. Final 2025-03-03T07:43:33Z 2025-03-03T07:43:33Z 2024 Article 10.14445/23488379/IJEEE-V11I5P114 2-s2.0-85195444937 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195444937&doi=10.14445%2f23488379%2fIJEEE-V11I5P114&partnerID=40&md5=173dd2329f3f0e036eb0de55cb8c85f0 https://irepository.uniten.edu.my/handle/123456789/36635 11 5-May 150 160 All Open Access; Hybrid Gold Open Access Seventh Sense Research Group Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description In this study, an automatic solar defect detection and classification system using deep learning was proposed. This study focuses on solar faults in photovoltaic systems identified through Electroluminescence (EL) images by employing a deep learning framework that utilizes both traditional Convolutional Neural Networks (CNNs) and a pre-trained VGG16 and VGG-19 network for feature extraction. This approach was designed to enhance the accuracy and efficiency of solar defect classification. The framework is structured into three main phases: image preprocessing, feature extraction using CNNs, Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN), and classification through a Deep Neural Network (DNN). During preprocessing, images are scaled down to uniform dimensions to ensure consistent learning. They adopted two classification strategies: binary classification (defective or non-defective) and multiclass classification; the class names are 0%, 33%, 67%, and 100% (here, % represents the percentage of defectiveness), which represents the defect likelihood. To refine the model?s performance, a data augmentation technique has been utilized on the dataset. The effectiveness of the model was evaluated using various metrics, including the precision, recall, F1-score, and accuracy for two and four classes and obtained on, supported by confusion matrices. VGG-19 model outperformed other models and achieved precision, recall, F1-score and accuracy of 90% each for two classes respectively and similarly 94% for four classes. This study compares two classification methods to assess the ability of the deep learning framework to detect and classify solar defect images automatically. ? 2024 Seventh Sense Research Group. All rights reserved.
author2 57223341022
author_facet 57223341022
Almashhadani R.A.I.
Hock G.C.
Nordin F.H.B.
Abdulrazzak H.N.
format Article
author Almashhadani R.A.I.
Hock G.C.
Nordin F.H.B.
Abdulrazzak H.N.
spellingShingle Almashhadani R.A.I.
Hock G.C.
Nordin F.H.B.
Abdulrazzak H.N.
Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification
author_sort Almashhadani R.A.I.
title Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification
title_short Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification
title_full Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification
title_fullStr Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification
title_full_unstemmed Electroluminescence Images for Solar Cell Fault Detection Using Deep Learning for Binary and Multiclass Classification
title_sort electroluminescence images for solar cell fault detection using deep learning for binary and multiclass classification
publisher Seventh Sense Research Group
publishDate 2025
_version_ 1825816240242819072
score 13.244413

Similar Items