Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images

Year 2024, , 363 - 377, 31.08.2024

Sahra Simsek Kaya , Abdülkadir Gümüşçü , Nurettin Beşli

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

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.

Supporting Institution

Harran University Scientific Research Projects Commission

Project Number

22256

Thanks

We would like to thank to Harran University Scientific Research Projects Commission for supporting our study (Project No:22256).

Fotovoltaik Hücre Elektrolüminesans Görüntülerinde Verimli Bara Kayma Kusurlarının Tespiti

Abstract

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.

Keywords

Derin Öğrenme, Bara Kayması, Elektrolüminesans, Güneş Hücreleri Sınıflandırma

Supporting Institution

Harran University Scientific Research Projects Commission

Project Number

22256

Thanks

We would like to thank to Harran University Scientific Research Projects Commission for supporting our study (Project No:22256).

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