Güneş Paneli Kusurlarının Derin Öğrenme Tabanlı Sınıflandırılması

Abstract

Yenilenemez enerji kaynaklarının çevreye ve ekolojiye verdiği zararlar, yenilenebilir enerji kaynaklarına olan ilginin artmasına neden olmaktadır. Fotovoltaik (FV) enerji üretimi, temiz ve sürdürülebilir enerji üretimi için mükemmel enerji alternatiflerinden biridir. Fotovoltaik paneller üzerindeki kar, toz, gölge, kuş pisliği, mekaniksel ve fiziksel arıza gibi etkenler enerji üretimindeki verimi azaltmaktadır ve bu yüzden panel bakımı düzenli olarak yapılmalıdır. Bakımlar manuel olarak yapıldığında hatalar olmakta ve uzun zaman almaktadır. Bu nedenle güneş paneli kusurları son zamanlarda geliştirilen görüntü işleme ve derin öğrenme algoritmaları kullanılarak tespit edilebilmektedir. Bu çalışmada, derin öğrenme tekniği kullanılarak güneş panelleri üzerinde hasar tespiti sınıflandırması yapılmıştır. Çalışma iki aşamadan oluşmaktadır. İlk aşama, ön işleme aşamasıdır ve bu aşamada veri seti yetersiz olması nedeniyle veri çoğaltma teknikleri kullanılarak arttırılmıştır. İkinci aşama olan eğitim aşamasında ise çoğaltılan veri seti önerilen derin öğrenme modeliyle eğitilmiştir. Eğitim sonucunda önerilen modelin 7 farklı kusurun sınıflandırılmasında %96.56 başarı elde ettiği gözlenmiştir.

Keywords

• Derin öğrenme
• Güneş panelleri
• Fotovoltaik
• Veri çoğaltma
• Sınıflandırma

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