Güneş Paneli Kusurlarının Derin Öğrenme Tabanlı Sınıflandırılması

Year 2024, , 140 - 149, 01.07.2024

Sebahattin Yiğit Lermi Tuğba Özge Onur

https://doi.org/10.7240/jeps.1383975

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

References

• “Times of 1500 PV system has come” URL: https://www.mornsun-power.com/html/news-detail/blog-posts/213.html
• “Times of 1500 PV system has come” URL: https://www.mornsun-power.com/html/news-detail/blog-posts/213.html
• Platon, R., Martel, J. T., Woodruff, N., & Chau, T. Y. (2015b). Online fault detection in PV systems. IEEE Transactions on Sustainable Energy, 6(4), 1200–1207. https://doi.org/10.1109/tste.2015.2421447
• Platon, R., Martel, J. T., Woodruff, N., & Chau, T. Y. (2015b). Online fault detection in PV systems. IEEE Transactions on Sustainable Energy, 6(4), 1200–1207. https://doi.org/10.1109/tste.2015.2421447
• Li, B., Delpha, C., Diallo, D., & Migan Dubois, A. (2021). Application of artificial neural networks to photovoltaic fault detection and diagnosis: A review. Renewable & Sustainable Energy Reviews, 138, 110512. https://doi.org/10.1016/j.rser.2020.110512
• Li, B., Delpha, C., Diallo, D., & Migan Dubois, A. (2021). Application of artificial neural networks to photovoltaic fault detection and diagnosis: A review. Renewable & Sustainable Energy Reviews, 138, 110512. https://doi.org/10.1016/j.rser.2020.110512
• Tang, W., Yang, Q., Xiong, K., & Yan, W. (2020). Deep learning based automatic defect identification of photovoltaic module using electroluminescence images. Solar Energy, 201, 453–460. https://doi.org/10.1016/j.solener.2020.03.049
• Tang, W., Yang, Q., Xiong, K., & Yan, W. (2020). Deep learning based automatic defect identification of photovoltaic module using electroluminescence images. Solar Energy, 201, 453–460. https://doi.org/10.1016/j.solener.2020.03.049
• Jordan, D., Kurtz, S., VanSant, K., & Newmiller, J. (2016). Compendium of photovoltaic degradation rates. progress in photovoltaics, 24(7), 978–989. https://doi.org/10.1002/pip.2744
• Jordan, D., Kurtz, S., VanSant, K., & Newmiller, J. (2016). Compendium of photovoltaic degradation rates. progress in photovoltaics, 24(7), 978–989. https://doi.org/10.1002/pip.2744
• Korkmaz, D., & Açıkgöz, H. (2022). An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network. Engineering Applications of Artificial Intelligence, 113, 104959. https://doi.org/10.1016/j.engappai.2022.104959
• Korkmaz, D., & Açıkgöz, H. (2022). An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network. Engineering Applications of Artificial Intelligence, 113, 104959. https://doi.org/10.1016/j.engappai.2022.104959
• Espinosa, A. R., Bressan, M., & Giraldo, L. F. (2020). Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks. Renewable Energy,162,249–256. https://doi.org/10.1016/j.renene.2020.07.154
• Espinosa, A. R., Bressan, M., & Giraldo, L. F. (2020). Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks. Renewable Energy,162,249–256. https://doi.org/10.1016/j.renene.2020.07.154
• Kayci, B., Demir, B. E., & Demir, F. (2022). İHA tarafından elde edilen termal görüntüler kullanılarak fotovoltaik sistemde derin öğrenme tabanlı arıza tespiti ve teşhisi. Politeknik Dergisi, 1, 1. https://doi.org/10.2339/politeknik.1094586
• Kayci, B., Demir, B. E., & Demir, F. (2022). İHA tarafından elde edilen termal görüntüler kullanılarak fotovoltaik sistemde derin öğrenme tabanlı arıza tespiti ve teşhisi. Politeknik Dergisi, 1, 1. https://doi.org/10.2339/politeknik.1094586
• Pierdicca, R., Malinverni, E. S., Piccinini, F., Paolanti, M., Felicetti, A., & Zingaretti, P. (2018). Deep convolutıonal neural network for automatıc detectıon of damaged photovoltaıc cells. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–2, 893–900. https://doi.org/10.5194/isprs-archives-xlii-2-893-2018
• Pierdicca, R., Malinverni, E. S., Piccinini, F., Paolanti, M., Felicetti, A., & Zingaretti, P. (2018). Deep convolutıonal neural network for automatıc detectıon of damaged photovoltaıc cells. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–2, 893–900. https://doi.org/10.5194/isprs-archives-xlii-2-893-2018
• Li, X., Yang, Q., Lou, Z., & Yan, W. (2019). Deep learning based module defect analysis for large-scale photovoltaic farms. IEEE Transactions on Energy Conversion, 34(1), 520–529. https://doi.org/10.1109/tec.2018.2873358
• Li, X., Yang, Q., Lou, Z., & Yan, W. (2019). Deep learning based module defect analysis for large-scale photovoltaic farms. IEEE Transactions on Energy Conversion, 34(1), 520–529. https://doi.org/10.1109/tec.2018.2873358
• S. Wei, X. Li, S. Ding, Q. Yang and W. Yan, (2019). Hotspots Infrared detection of photovoltaic modules based on hough line transformation and faster-rcnn approach, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1266-1271, https://doi.org/10.1109/codit.2019.8820333
• S. Wei, X. Li, S. Ding, Q. Yang and W. Yan, (2019). Hotspots Infrared detection of photovoltaic modules based on hough line transformation and faster-rcnn approach, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1266-1271, https://doi.org/10.1109/codit.2019.8820333
• Herráiz, Á. H., Marugán, A. P., & Márquez, F. P. G. (2020). Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure. Renewable Energy, 153, 334–348. https://doi.org/10.1016/j.renene.2020.01.148
• Herráiz, Á. H., Marugán, A. P., & Márquez, F. P. G. (2020). Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure. Renewable Energy, 153, 334–348. https://doi.org/10.1016/j.renene.2020.01.148
• Venkatesh, S., & Sugumaran, V. (2021). Fault detection in aerial images of photovoltaic modules based on deep learning. IOP Conference Series, 1012(1), 012030. https://doi.org/10.1088/1757-899x/1012/1/012030
• Venkatesh, S., & Sugumaran, V. (2021). Fault detection in aerial images of photovoltaic modules based on deep learning. IOP Conference Series, 1012(1), 012030. https://doi.org/10.1088/1757-899x/1012/1/012030
• Xie, X., Wei, X., Wang, X., Guo, X., Ju, L., & Cheng, Z. (2020). Photovoltaic panel anomaly detection system based on unmanned aerial vehicle platform. IOP Conference Series, 768(7), 072061. https://doi.org/10.1088/1757-899x/768/7/072061
• Xie, X., Wei, X., Wang, X., Guo, X., Ju, L., & Cheng, Z. (2020). Photovoltaic panel anomaly detection system based on unmanned aerial vehicle platform. IOP Conference Series, 768(7), 072061. https://doi.org/10.1088/1757-899x/768/7/072061
• Díaz, J. J. V., Vlaminck, M., Lefkaditis, D., Vargas, S. a. O., & Luong, H. (2020). Solar panel detection within complex backgrounds using thermal images acquired by UAVs. Sensors, 20(21), 6219. https://doi.org/10.3390/s20216219
• Díaz, J. J. V., Vlaminck, M., Lefkaditis, D., Vargas, S. a. O., & Luong, H. (2020). Solar panel detection within complex backgrounds using thermal images acquired by UAVs. Sensors, 20(21), 6219. https://doi.org/10.3390/s20216219
• Akram, M. W., Li, G., Jin, Y., Xiao, C., Zhu, C., & Ahmad, A. (2020). Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning. solar energy, 198, 175–186. https://doi.org/10.1016/j.solener.2020.01.055
• Akram, M. W., Li, G., Jin, Y., Xiao, C., Zhu, C., & Ahmad, A. (2020). Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning. solar energy, 198, 175–186. https://doi.org/10.1016/j.solener.2020.01.055
• Kurukuru, V. S. B., Haque, A., Khan, M. A., & Tripathy, A. K. (2019). Fault classification for photovoltaic modules using thermography and machine learning techniques, 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1-6, https://doi.org/10.1109/iccisci.2019.8716442
• Kurukuru, V. S. B., Haque, A., Khan, M. A., & Tripathy, A. K. (2019). Fault classification for photovoltaic modules using thermography and machine learning techniques, 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1-6, https://doi.org/10.1109/iccisci.2019.8716442
• Zaki, S. A., Zhu, H., Fakih, M. A., Sayed, A. R., & Yao, J. (2021). Deep learning–based method for faults classification of PV system. Iet Renewable Power Generation, 15(1), 193–205. https://doi.org/10.1049/rpg2.12016
• Zaki, S. A., Zhu, H., Fakih, M. A., Sayed, A. R., & Yao, J. (2021). Deep learning–based method for faults classification of PV system. Iet Renewable Power Generation, 15(1), 193–205. https://doi.org/10.1049/rpg2.12016
• Deitsch, S., Christlein, V., Berger, S., Buerhop Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2019). Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, 185, 455–468. https://doi.org/10.1016/j.solener.2019.02.067
• Deitsch, S., Christlein, V., Berger, S., Buerhop Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2019). Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, 185, 455–468. https://doi.org/10.1016/j.solener.2019.02.067
• Tang, W., Yang, Q., Xiong, K., & Yan, W. (2020b). Deep learning based automatic defect identification of photovoltaic module using electroluminescence images. Solar Energy, 201, 453–460. https://doi.org/10.1016/j.solener.2020.03.049
• Tang, W., Yang, Q., Xiong, K., & Yan, W. (2020b). Deep learning based automatic defect identification of photovoltaic module using electroluminescence images. Solar Energy, 201, 453–460. https://doi.org/10.1016/j.solener.2020.03.049
• Solar panel clean and faulty images. (2023, May 16).Kaggle. https://www.kaggle.com/datasets/pythonafroz/solar-panel-clean-and-faulty-images
• Solar panel clean and faulty images. (2023, May 16).Kaggle. https://www.kaggle.com/datasets/pythonafroz/solar-panel-clean-and-faulty-images
• LeCun, Y., Bengio, Y., & Hinton, G. E. (2015b). Deep learning. nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
• LeCun, Y., Bengio, Y., & Hinton, G. E. (2015b). Deep learning. nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
• İnik, Ö. & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri, Gaziosmanpaşa Bilimsel Araştırma Dergisi, c. 6, sayı. 3, ss. 85-104. http://dergipark.gov.tr/download/article-file/380999
• İnik, Ö. & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri, Gaziosmanpaşa Bilimsel Araştırma Dergisi, c. 6, sayı. 3, ss. 85-104. http://dergipark.gov.tr/download/article-file/380999
• Scherer, D., Müller, A., & Behnke, S. (2010). Evaluation of pooling operations in convolutional architectures for object recognition. ın proceedings of the International Greece, Springer: Berlin/Heidelberg, Germany, 2010; pp. 92–101.
• Scherer, D., Müller, A., & Behnke, S. (2010). Evaluation of pooling operations in convolutional architectures for object recognition. ın proceedings of the International Greece, Springer: Berlin/Heidelberg, Germany, 2010; pp. 92–101.