Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models
Electricity generation from renewable energy sources such as solar energy has come to the forefront in the last decade. The solar energy cell is an indispensable part of the solar energy ecosystem of solar panels, and defective cells cause financial losses in energy production. Experienced experts a...
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Sakarya University
2024-08-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3842101 |
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| author | Miktat Aktaş İbrahim Türkoğlu Ferdi Doğan |
| author_facet | Miktat Aktaş İbrahim Türkoğlu Ferdi Doğan |
| author_sort | Miktat Aktaş |
| collection | DOAJ |
| description | Electricity generation from renewable energy sources such as solar energy has come to the forefront in the last decade. The solar energy cell is an indispensable part of the solar energy ecosystem of solar panels, and defective cells cause financial losses in energy production. Experienced experts are needed to detect defects on solar cells. Autonomous systems are important to accelerate the process. Classical image processing techniques are used to manually detect defects on cells. To use these techniques, many parameters are need to be entered into EL imaging software. However, in this study, these processes were carried out automatically without the need for external intervention. False detection/classification may occur during the processes performed by EL imaging devices due to weakness of the operator experience or EL imaging software. It is aimed to use automatic image processing and then deep learning techniques to achieve faster and higher performance than the results obtained from EL imaging devices using classic image processing techniques. AI algorithm and deep learning models can be an important solution. In this study, two AI algorithm and 10 different deep learning models were used to classify solar cells. EL images of defective and normal solar cells with 4 and 5 busbars were used in the study. The dataset, includes 9360 images of solar cells, 4680 of which are defective and 4680 are normal. Performance evaluation of the models made according to the confusion matrix. According to the results, Mobilenet-v2 and VGG-19 achieved the highest validation accuracy rate of 99.68%. According to F1-score, Mobilenetv2 achieved the highest performance of 99.73%. It has been shown that the Mobilenet-v2 is slightly more successful than other models in terms of validation and F1-score. The results show that trained DL models can be used as an inspection method in the production line of solar panels and cells. |
| format | Article |
| id | doaj-art-e738d6d2545b477f81a64a6e9077ceb8 |
| institution | OA Journals |
| issn | 2636-8129 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj-art-e738d6d2545b477f81a64a6e9077ceb82025-08-20T02:27:54ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292024-08-017221722610.35377/saucis...146378828Classification of Solar Cells EL Images with Different Busbars Via Deep Learning ModelsMiktat Aktaş0https://orcid.org/0000-0002-0731-5668İbrahim Türkoğlu1https://orcid.org/0000-0003-4938-4167Ferdi Doğan2https://orcid.org/0000-0002-9203-697XGTC GÜNEŞ SANAYİ VE TİCARET AŞFIRAT ÜNİVERSİTESİADIYAMAN ÜNİVERSİTESİElectricity generation from renewable energy sources such as solar energy has come to the forefront in the last decade. The solar energy cell is an indispensable part of the solar energy ecosystem of solar panels, and defective cells cause financial losses in energy production. Experienced experts are needed to detect defects on solar cells. Autonomous systems are important to accelerate the process. Classical image processing techniques are used to manually detect defects on cells. To use these techniques, many parameters are need to be entered into EL imaging software. However, in this study, these processes were carried out automatically without the need for external intervention. False detection/classification may occur during the processes performed by EL imaging devices due to weakness of the operator experience or EL imaging software. It is aimed to use automatic image processing and then deep learning techniques to achieve faster and higher performance than the results obtained from EL imaging devices using classic image processing techniques. AI algorithm and deep learning models can be an important solution. In this study, two AI algorithm and 10 different deep learning models were used to classify solar cells. EL images of defective and normal solar cells with 4 and 5 busbars were used in the study. The dataset, includes 9360 images of solar cells, 4680 of which are defective and 4680 are normal. Performance evaluation of the models made according to the confusion matrix. According to the results, Mobilenet-v2 and VGG-19 achieved the highest validation accuracy rate of 99.68%. According to F1-score, Mobilenetv2 achieved the highest performance of 99.73%. It has been shown that the Mobilenet-v2 is slightly more successful than other models in terms of validation and F1-score. The results show that trained DL models can be used as an inspection method in the production line of solar panels and cells.https://dergipark.org.tr/en/download/article-file/3842101güneş hücresiderin öğrenmesınıflandırmael görüntülerisolar cellsdeep learningimage processingel imagedefects |
| spellingShingle | Miktat Aktaş İbrahim Türkoğlu Ferdi Doğan Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models Sakarya University Journal of Computer and Information Sciences güneş hücresi derin öğrenme sınıflandırma el görüntüleri solar cells deep learning image processing el image defects |
| title | Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models |
| title_full | Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models |
| title_fullStr | Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models |
| title_full_unstemmed | Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models |
| title_short | Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models |
| title_sort | classification of solar cells el images with different busbars via deep learning models |
| topic | güneş hücresi derin öğrenme sınıflandırma el görüntüleri solar cells deep learning image processing el image defects |
| url | https://dergipark.org.tr/en/download/article-file/3842101 |
| work_keys_str_mv | AT miktataktas classificationofsolarcellselimageswithdifferentbusbarsviadeeplearningmodels AT ibrahimturkoglu classificationofsolarcellselimageswithdifferentbusbarsviadeeplearningmodels AT ferdidogan classificationofsolarcellselimageswithdifferentbusbarsviadeeplearningmodels |