Solar photovoltaic panel cells defects classification using deep learning ensemble methods
Solar photovoltaic (PV) systems are essential for sustainable energy production; however, their reliability may be undermined by unfavorable weather conditions, resulting in defects in the individual cells. Conventional manual inspection techniques are labor-intensive and susceptible to human error....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25000097 |
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| author | H. Tella A. Hussein S. Rehman B. Liu A. Balghonaim M. Mohandes |
| author_facet | H. Tella A. Hussein S. Rehman B. Liu A. Balghonaim M. Mohandes |
| author_sort | H. Tella |
| collection | DOAJ |
| description | Solar photovoltaic (PV) systems are essential for sustainable energy production; however, their reliability may be undermined by unfavorable weather conditions, resulting in defects in the individual cells. Conventional manual inspection techniques are labor-intensive and susceptible to human error. This study utilizes drone-acquired electroluminescence (EL) images to identify and categorize solar cell defects through an ensemble-based deep learning framework. Eight advanced models—AlexNet, SENet, GoogleNet (Inception V1), Xception, Vision Transformer (ViT), Darknet53, ResNet18, and SqueezeNet—were fine-tuned on the 2624-sample ELPV benchmark dataset. Experimental findings indicate that the proposed voting and bagging ensembles attain accuracies of 68.36 % and 68.31 %, respectively, exceeding the previously documented accuracy of a hybrid model at 61.15 %. Significantly, the ResNet18 model achieves an accuracy of 73.02 % in a straightforward binary classification task, highlighting that individual models can surpass ensembles in particular circumstances. This study emphasizes the efficacy of integrating various deep learning architectures to augment defect detection precision in photovoltaic systems, enhancing operational reliability and enabling prompt maintenance under challenging environmental conditions. |
| format | Article |
| id | doaj-art-4f4207069daf4fdbbc227fab52bbd502 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-4f4207069daf4fdbbc227fab52bbd5022025-02-02T05:27:21ZengElsevierCase Studies in Thermal Engineering2214-157X2025-02-0166105749Solar photovoltaic panel cells defects classification using deep learning ensemble methodsH. Tella0A. Hussein1S. Rehman2B. Liu3A. Balghonaim4M. Mohandes5Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaInterdisciplinary Research Center for Sustainable Energy Systems, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia; Corresponding author. Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia; Interdisciplinary Research Center for Sustainable Energy Systems, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia; Corresponding author. Electrical Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia.Solar photovoltaic (PV) systems are essential for sustainable energy production; however, their reliability may be undermined by unfavorable weather conditions, resulting in defects in the individual cells. Conventional manual inspection techniques are labor-intensive and susceptible to human error. This study utilizes drone-acquired electroluminescence (EL) images to identify and categorize solar cell defects through an ensemble-based deep learning framework. Eight advanced models—AlexNet, SENet, GoogleNet (Inception V1), Xception, Vision Transformer (ViT), Darknet53, ResNet18, and SqueezeNet—were fine-tuned on the 2624-sample ELPV benchmark dataset. Experimental findings indicate that the proposed voting and bagging ensembles attain accuracies of 68.36 % and 68.31 %, respectively, exceeding the previously documented accuracy of a hybrid model at 61.15 %. Significantly, the ResNet18 model achieves an accuracy of 73.02 % in a straightforward binary classification task, highlighting that individual models can surpass ensembles in particular circumstances. This study emphasizes the efficacy of integrating various deep learning architectures to augment defect detection precision in photovoltaic systems, enhancing operational reliability and enabling prompt maintenance under challenging environmental conditions.http://www.sciencedirect.com/science/article/pii/S2214157X25000097Solar panelsPV cellsDefect detectionDeep learningEnsemble models |
| spellingShingle | H. Tella A. Hussein S. Rehman B. Liu A. Balghonaim M. Mohandes Solar photovoltaic panel cells defects classification using deep learning ensemble methods Case Studies in Thermal Engineering Solar panels PV cells Defect detection Deep learning Ensemble models |
| title | Solar photovoltaic panel cells defects classification using deep learning ensemble methods |
| title_full | Solar photovoltaic panel cells defects classification using deep learning ensemble methods |
| title_fullStr | Solar photovoltaic panel cells defects classification using deep learning ensemble methods |
| title_full_unstemmed | Solar photovoltaic panel cells defects classification using deep learning ensemble methods |
| title_short | Solar photovoltaic panel cells defects classification using deep learning ensemble methods |
| title_sort | solar photovoltaic panel cells defects classification using deep learning ensemble methods |
| topic | Solar panels PV cells Defect detection Deep learning Ensemble models |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25000097 |
| work_keys_str_mv | AT htella solarphotovoltaicpanelcellsdefectsclassificationusingdeeplearningensemblemethods AT ahussein solarphotovoltaicpanelcellsdefectsclassificationusingdeeplearningensemblemethods AT srehman solarphotovoltaicpanelcellsdefectsclassificationusingdeeplearningensemblemethods AT bliu solarphotovoltaicpanelcellsdefectsclassificationusingdeeplearningensemblemethods AT abalghonaim solarphotovoltaicpanelcellsdefectsclassificationusingdeeplearningensemblemethods AT mmohandes solarphotovoltaicpanelcellsdefectsclassificationusingdeeplearningensemblemethods |