İHA Tarafından Elde Edilen Termal Görüntüler Kullanılarak Fotovoltaik Sistemde Derin Öğrenme Tabanlı Arıza Tespiti ve Teşhisi

Öz

Güneş enerjisi, dünyanın en büyük yenilenebilir enerji kaynaklarından biridir. Fotovoltaik sistemler ile güneşin olduğu her yerde elektrik enerjisi üretilebilir. Fotovoltaik sistemlerde verim kayıplarını önlemek için bu sistemlerin düzenli aralıklarla test edilmesi gerekmektedir. Bu çalışmada güneş panellerinden elde edilen termal görüntüler kullanılarak panellerdeki hücre, modül ve panel arızalarının tespiti ele alınmıştır. Çalışma kapsamında dört rotorlu bir insansız hava aracı (drone) tasarlamış ve araca termal bir kamera yerleştirilmiştir. Böylelikle Karabük Üniversitesi binalarının çatısında bulunan güneş panellerinin termal görüntüleri alınmıştır. Elde edilen termal görüntüler kullanılarak hücre hatası, modül hatası ve panel hatasını içeren bir termal veri seti oluşturulmuştur. YOLOv3 derin öğrenme tabanlı evrişimsel sinir ağı, oluşturulan veri seti ile eğitilmiştir. Bu eğitim, gömülü bir yapay zeka bilgi işlem cihazı olan Nvidia Jetson TX2 üzerinde gerçekleştirilmiştir. YOLOv3 ağının eğitiminin tamamlanmasının ardından testlerde bahsedilen arızaların başarıyla tespit edildiği sonucuna ulaşılmıştır.

Anahtar Kelimeler

• Fotovoltaik sistem
• termografi
• derin öğrenme
• arıza tespiti
• insansız hava araçları

Proje Numarası

FYL-2019-2131

Deep Learning Based Fault Detection and Diagnosis in Photovoltaic System Using Thermal Images Acquired by UAV

Öz

Solar power is one of the largest renewable energy sources in the world. With photovoltaic systems, electrical energy can be generated wherever the sun is located. To prevent efficiency losses in photovoltaic systems, these systems should be tested at regular intervals. In this study, it is discussed to detect cell, module and panel faults in panels using thermal images obtained from solar panels. Within the scope of the study, a four-rotor unmanned aerial vehicle (drone) was designed and a thermal camera was placed on the vehicle. Thus, thermal images of the solar panels on the roof of Karabuk University buildings were taken. A thermal data set with cell fault, module fault and panel fault were created using the resulting thermal images. The YOLOv3 deep learning-based convolutional neural network was trained with the created dataset. This training was conducted on Nvidia Jetson TX2, an embedded AI (Artificial Intelligence) computing device. After the completion of the training of the YOLOv3 network, it was concluded that the faults mentioned in the tests were successfully detected.  

Anahtar Kelimeler

• photovoltaic system
• thermography
• deep learning
• fault detection
• unmanned aerial vehicles

Destekleyen Kurum

Karabuk University Scientific Research Projects

Proje Numarası

FYL-2019-2131

Teşekkür

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Karabuk University within the scope of Scientific Research Projects with FYL-2019-2131 code.

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