A Deep Learning Approach for Fault Detection in Photovoltaic Systems Using MobileNetV3

Year 2025, Volume: 12 Issue: 1, 197 - 212, 26.03.2025

Shuhratjon Mansurov , Ziya Çetin , Emrah Aslan , Yıldırım Özüpak

https://doi.org/10.54287/gujsa.1596110

Abstract

This study investigates the use of the MobileNetV3 deep learning architecture for fault detection in Photovoltaic (PV) systems. The research developed a model capable of classifying solar panels under six different conditions: clean, physically damaged, electrically damaged, snow covered, bird droppings covered, and dusty panels. Using a dataset obtained from Kaggle, pre-processed and divided into training (70%) and test (30%) sets, the MobileNetV3 model achieved a validation accuracy of 95%. Confusion matrix analysis showed high classification accuracy, in particular 100% accuracy for snow-covered and bird droppings-covered panels, with F1 scores as high as 98.73% for certain classes. Training and validation curves confirmed stable learning with low loss values. Compared to models such as EfficientB0 + SVM and InceptionV3-Net + U-Net, MobileNetV3 demonstrated competitive accuracy and computational efficiency, making it suitable for resource-constrained devices. This approach improves energy efficiency, reduces manual inspection, and promotes sustainable energy production. Future work will expand the dataset to include different climatic conditions and fault scenarios, improving the robustness and real-world applicability of the model.

Keywords

MobileNetV3, Photovoltaic Systems, Fault Detection, Deep Learning

References

• Bishop, C. M. (2007). Pattern recognition and machine learning (Information Science and Statistics). Springer.
• Boubaker, S., Kamel, S., Ghazouani, N., & Mellit, A. (2023). Assessment of machine and deep learning approaches for fault diagnosis in photovoltaic systems using infrared thermography. Remote Sensing, 15(6), 1686. https://doi.org/10.3390/rs15061686
• Cao, H., Zhang, D., & Yi, S. (2023). Real-time machine learning-based fault detection, classification, and locating in large scale solar energy-based systems: Digital twin simulation. Solar Energy, 251, 77–85. https://doi.org/10.1016/j.solener.2022.12.042
• Eurostat. (2022). Wind and water provide most renewable electricity; solar is the fastest-growing energy source. URL
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
• Hong, Y.Y., & Pula, R. A. (2022). Methods of photovoltaic fault detection and classification: A review. Energy Reports, 8, 5898–5929. https://doi.org/10.1016/j.egyr.2022.04.043
• Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., & Adam, H. (2019). Searching for MobileNetV3. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1314–1324). https://doi.org/10.48550/arXiv.1905.02244
• Huang, J. M., Wai, R. J., & Gao, W. (2019). Newly-designed fault diagnostic method for solar photovoltaic generation system based on IV-Curve measurement. IEEE Access, 7, 70919–70932. https://doi.org/10.1109/ACCESS.2019.2919337
• Johnson, J., Montoya, M., McCalmont, S., Katzir, G., Fuks, F., Earle, J., Fresquez, A., Gonzalez, S., & Granata, J. (2012). Differentiating series and parallel photovoltaic arc-faults. In 2012 38th IEEE Photovoltaic Specialists Conference (pp. 720–726). https://doi.org/10.1109/PVSC.2012.6317708
• Kaggle. (n.d.). Fault detection using ResNet50. Retrieved from URL
• Kumar, U., Mishra, S., & Dash, K. (2023). An IoT and semi-supervised learning-based sensorless technique for panel level solar photovoltaic array fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 72, 1–12. https://doi.org/10.1109/TIM.2023.3287247
• Mustafa, Z., Awad, A. S. A., Azzouz, M., & Azab, A. (2023). Fault identification for photovoltaic systems using a multi-output deep learning approach. Expert Systems with Applications, 211, 118551. https://doi.org/10.1016/j.eswa.2022.118551
• Rudro, R. A. M., Nur, K., Sohan, M. F. A. Al, Mridha, M. F., Alfarhood, S., Safran, M., & Kanagarathinam, K. (2024). SPF-Net: Solar panel fault detection using U-Net based deep learning image classification. Energy Reports, 12, 1580–1594. https://doi.org/10.1016/J.EGYR.2024.07.044
• Sabbaghpur, M., & Hejazi, M. A. (2016). The comprehensive study of electrical faults in PV arrays. Journal of Electrical and Computer Engineering, 2016, 8712960. https://doi.org/10.1155/2016/8712960
• Sepúlveda-Oviedo, E. H., Travé-Massuyès, L., Subias, A., Pavlov, M., & Alonso, C. (2023). Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach. Heliyon, 9(11), e21491. https://doi.org/10.1016/J.HELIYON.2023.E21491
• Voutsinas, S., Mandourarakis, I., Koutroulis, E., Karolidis, D., Voyiatzis, I., & Samarakou, M. (2022). Control and communication for smart photovoltaic arrays. In 26th Pan-Hellenic Conference on Informatics (PCI 2022) (pp. 6). https://doi.org/10.1145/3575879.3575983
• Voutsinas, S., Karolidis, D., Voyiatzis, I., & Samarakou, M. (2023). Development of a machine-learning-based method for early fault detection in photovoltaic systems. Journal of Engineering and Applied Sciences, 70(27). https://doi.org/10.1186/s44147-023-00200-0
• Zhang, D., & Duranay, Z. B. (2023). Fault Detection in Solar Energy Systems: A Deep Learning Approach. Electronics 2023, Vol. 12, Page 4397, 12(21), 4397. https://doi.org/10.3390/ELECTRONICS12214397