Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images

Sahra Simsek Kaya; Abdülkadir Gümüşçü; Nurettin Beşli

doi:10.54365/adyumbd.1494765

EN TR

Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images

Abstract

PV panel quality control is crucial for their efficient and long-lasting operation. Detecting defects in PV panels during production is essential. Electroluminescence imaging is a commonly used method for fault detection in PV panels. This study focuses on detecting busbar slippage, a specific PV panel malfunction. Automatic error detection was researched using machine learning methods on a dataset of 500 EL images taken from the production line. Feature extraction was performed using two pre-trained deep learning architectures: ResNet and SqueezeNet. Additionally, the study aimed to observe the impact of combining features from different deep learning architectures on success parameters. The highest accuracy rate of 0.9920 was achieved using deep features extracted by Relu34 and Relu25+Conv10 layers.

Keywords

Deep Learning , Busbar slip , Defect Detection , Electroluminescence , Solar cell classification.

Fotovoltaik Hücre Elektrolüminesans Görüntülerinde Verimli Bara Kayma Kusurlarının Tespiti

Öz

PV panellerin kalite kontrolü, verimli ve uzun ömürlü çalışmaları için çok önemlidir. PV panellerdeki kusurların üretim sırasında tespit edilmesi de ayrıca önem arz etmektedir. Elektrolüminesans görüntüleme, PV panellerde arıza tespiti için yaygın olarak kullanılan bir yöntem olarak karşımıza çıkmaktadır. Bu çalışma, spesifik bir PV panel arızası olan bara kaymasının tespitine odaklanmaktadır. Üretim hattından alınan 500 EL görüntüsünden oluşan bir veri seti üzerinde makine öğrenmesi yöntemleri kullanılarak otomatik hata tespiti araştırılmıştır. Özellik çıkarma, önceden eğitilmiş ResNet ve SqueezeNet derin öğrenme mimarileri kullanılarak gerçekleştirilmiştir. Ayrıca çalışmada, farklı derin öğrenme mimarilerindeki özellikleri birleştirmenin başarı parametreleri üzerindeki etkisini gözlemlenmiştir. En yüksek doğruluk oranı olan 0,9920, Relu34 ve Relu25+Conv10 katmanları tarafından çıkarılan derin özellikler kullanılarak elde edilmiştir.

Anahtar Kelimeler

Derin Öğrenme , Bara Kayması , Elektrolüminesans , Güneş Hücreleri Sınıflandırma

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Görüntü İşleme

Bölüm

Araştırma Makalesi

Yazarlar

Sahra Simsek Kaya
0009-0008-3797-8324
Türkiye

Abdülkadir Gümüşçü ^*
0000-0002-5948-595X
Türkiye

Nurettin Beşli
0000-0003-3657-1393
Türkiye

Yayımlanma Tarihi

31 Ağustos 2024

Gönderilme Tarihi

3 Haziran 2024

Kabul Tarihi

20 Ağustos 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 11 Sayı: 23

DOI

https://doi.org/10.54365/adyumbd.1494765

IZ

https://izlik.org/JA25MC34CD

APA

Simsek Kaya, S., Gümüşçü, A., & Beşli, N. (2024). Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 11(23), 363-377. https://doi.org/10.54365/adyumbd.1494765

AMA

1.Simsek Kaya S, Gümüşçü A, Beşli N. Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2024;11(23):363-377. doi:10.54365/adyumbd.1494765

Chicago

Simsek Kaya, Sahra, Abdülkadir Gümüşçü, ve Nurettin Beşli. 2024. “Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 11 (23): 363-77. https://doi.org/10.54365/adyumbd.1494765.

EndNote

Simsek Kaya S, Gümüşçü A, Beşli N (01 Ağustos 2024) Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 11 23 363–377.

IEEE

[1]S. Simsek Kaya, A. Gümüşçü, ve N. Beşli, “Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 11, sy 23, ss. 363–377, Ağu. 2024, doi: 10.54365/adyumbd.1494765.

ISNAD

Simsek Kaya, Sahra - Gümüşçü, Abdülkadir - Beşli, Nurettin. “Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 11/23 (01 Ağustos 2024): 363-377. https://doi.org/10.54365/adyumbd.1494765.

JAMA

1.Simsek Kaya S, Gümüşçü A, Beşli N. Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2024;11:363–377.

MLA

Simsek Kaya, Sahra, vd. “Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 11, sy 23, Ağustos 2024, ss. 363-77, doi:10.54365/adyumbd.1494765.

Vancouver

1.Sahra Simsek Kaya, Abdülkadir Gümüşçü, Nurettin Beşli. Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 01 Ağustos 2024;11(23):363-77. doi:10.54365/adyumbd.1494765

Cited By

From Indoor to Daylight Electroluminescence Imaging for PV Module Diagnostics: A Comprehensive Review of Techniques, Challenges, and AI-Driven Advancements

Micromachines

https://doi.org/10.3390/mi16040437

Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images

Abstract

Keywords

Fotovoltaik Hücre Elektrolüminesans Görüntülerinde Verimli Bara Kayma Kusurlarının Tespiti

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

From Indoor to Daylight Electroluminescence Imaging for PV Module Diagnostics: A Comprehensive Review of Techniques, Challenges, and AI-Driven Advancements