Fault Detection in Solar PV Panels Using Artificial Intelligence and Embedded Systems
Document Type : Research Article
Authors
Electronic Computer Center, University of Diyala, Diyala, Iraq
Abstract
Solar energy is a sustainable and renewable resource that plays a vital role in mitigating climate change by reducing greenhouse gas emissions. However, its efficiency can be compromised by different operational faults such as dust accumulation, surface cracks, or electrical failures. Detecting these issues early is necessary to maintain optimum performance and avoid costly system failures. In this study, we propose an AI-based approach for automated fault detection in solar panels, built on a lightweight deep learning model adapted from the MobileNetV2 architecture. The model is trained and validated on a publicly available dataset with data balancing and augmentation to improve classification accuracy across different fault categories. To assess practical feasibility, we deployed the system on an embedded Jetson Nano platform. Extensive results and comparisons demonstrate the superior performance of the proposed method, achieving an accuracy of 93.14% and an F1-score of 93.12%, while maintaining a low model size (2.8M parameters) and an inference speed of 44.4 ms per image on the Jetson Nano, which is fast enough to meet real-time inspection requirements in embedded devices. Overall, the findings indicate that our solution provides an effective framework for on-site solar panel monitoring and maintenance without the need for cloud resources.
Keywords
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[36] Gasparyan, H., Agaian, S., & Wu, S. (2025). Efficient lightweight networks for solar panel fault classification using EL and RGB imagery. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3548249.
[2] Saleh, H. M., & Hassan, A. I. (2024). The challenges of sustainable energy transition: A focus on renewable energy. Applied Chemical Engineering, 7(2), 2084. https://doi.org/10.59429/ace.v7i2.2084.
[3] Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50. https://doi.org/10.1016/j.esr.2019.01.006.
[4] Ansari, S., Ayob, A., Lipu, M. S. H., Saad, M. H. M., & Hussain, A. (2021). A review of monitoring technologies for solar PV systems using data processing modules and transmission protocols: Progress, challenges and prospects. Sustainability, 13(15), 8120. https://doi.org/10.3390/su13158120.
[5] Chiteka, K., & Enweremadu, C. (2025). A review on modeling and prediction of soiling on solar photovoltaics and thermal collectors. Journal of Solar Energy Research. https://doi.org/10.22059/jser.2025.392093.1544.
[6] Nooralishahi, P., Ibarra-Castanedo, C., Deane, S., López, F., Pant, S., Genest, M., Avdelidis, N. P., & Maldague, X. P. V. (2021). Drone-based non-destructive inspection of industrial sites: A review and case studies. Drones, 5(4), 106. https://doi.org/10.3390/drones5040106.
[7] Onim, M. S. H., Sakif, Z. M. M., Ahnaf, A., Kabir, A., Azad, A. K., Oo, A. M. T., Afreen, R., Hridy, S. T., Hossain, M., Jabid, T., & Ali, M. S. (2022). Solnet: A convolutional neural network for detecting dust on solar panels. Energies, 16(1), 155. https://doi.org/10.3390/en16010155.
[8] Yousif, A. J., Zaidan, F. K., & Ibrahim, N. J. (2025). A portable AI-driven edge solution for automated plant disease detection. Diyala Journal of Engineering Sciences, 124–135. https://doi.org/10.24237/djes.2025.18308
[9] Ayana, G., Dese, K., & Choe, S. (2021). Transfer learning in breast cancer diagnoses via ultrasound imaging. Cancers, 13(4), 738. https://doi.org/10.3390/cancers13040738.
[10] Yousif, A. J., & Al-Jammas, M. H. (2024). A lightweight visual understanding system for enhanced assistance to the visually impaired using an embedded platform. Diyala Journal of Engineering Sciences, 146–162. https://doi.org/10.24237/djes.2024.17310.
[11] Abro, G. E. M., Ali, A., Memon, S. A., Memon, T. D., & Khan, F. (2024). Strategies and challenges for unmanned aerial vehicle-based continuous inspection and predictive maintenance of solar modules. IEEE Access, 12, 176615–176629. https://doi.org/10.1109/ACCESS.2024.3505754
[12] Masita, K., Hasan, A., Shongwe, T., & Abu Hilal, H. (2025). Deep learning in defects detection of PV modules: A review. Solar Energy Advances, 100090. https://doi.org/10.1016/j.seja.2025.100090.
[13] Polymeropoulos, I., Bezyrgiannidis, S., Vrochidou, E., & Papakostas, G. A. (2024). Enhancing solar plant efficiency: A review of vision-based monitoring and fault detection techniques. Technologies, 12(10), 175. https://doi.org/10.3390/technologies12100175.
[14] Ling, H., Liu, M., & Fang, Y. (2024). Deep edge-based fault detection for solar panels. Sensors, 24(16), 5348. https://doi.org/10.3390/s24165348.
[15] 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.
[16] Kellil, N., Aissat, A., & Mellit, A. (2023). Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions. Energy, 263, 125902. https://doi.org/10.1016/j.energy.2022.125902.
[17] Alam, S., Kaushik, S., Shaique, S. M., & Rafiuddin, N. (2024). PV fault detection using CNN for enhancing reliability of solar power plants. In Proceedings of the IEEE ICPEICES (pp. 913–917). Delhi, India. https://doi.org/10.1109/ICPEICES62430.2024.10719330.
[18] Jaybhaye, S., Thakur, O., Yardi, R., Raut, V., & Raut, A. (2023). Solar panel damage detection and localization of thermal images. Journal of Failure Analysis and Prevention, 23(5), 1980–1990. https://doi.org/10.1007/s11668-023-01747-z.
[19] Awedat, K., Comert, G., Ayad, M., & Mrebit, A. (2025). Advanced fault detection in photovoltaic panels using enhanced U-Net architectures. Machine Learning with Applications, 20, 100636. https://doi.org/10.1016/j.mlwa.2025.100636.
[20] Meng, Z., Xu, S., Wang, L., Gong, Y., Zhang, X., & Zhao, Y. (2022). Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning. Energy Science & Engineering, 10(3), 800–813. https://doi.org/10.1002/ese3.1056.
[21] Mazen, F. M. A., Seoud, R. A. A., & Shaker, Y. O. (2023). Deep learning for automatic defect detection in PV modules using electroluminescence images. IEEE Access, 11, 57783–57795. https://doi.org/10.1109/ACCESS.2023.3284043.
[22] Wang, J., Bi, L., Sun, P., Jiao, X., Ma, X., Lei, X., & Luo, Y. (2022). Deep-learning-based automatic detection of photovoltaic cell defects in electroluminescence images. Sensors, 23(1), 297. https://doi.org/10.3390/s23010297.
[23] Tella, H., Hussein, A., Rehman, S., Liu, B., Balghonaim, A., & Mohandes, M. (2025). Solar photovoltaic panel cells defects classification using deep learning ensemble methods. Case Studies in Thermal Engineering, 66, 105749. https://doi.org/10.1016/j.csite.2025.105749.
[24] Kilci, O., & Koklu, M. (2025, July). Machine learning-based detection of solar panel surface defects using deep features from InceptionV3. In 4th International Conference on Trends in Advanced Research. Konya, Turkey.
[25] Kazemi, K. (2025). Federated transfer learning for image-based solar panel fault detection. In Proceedings of the ICREDG. https://doi.org/10.1109/ICREDG66184.2025.10966124.
[26] Ledmaoui, Y., El Maghraoui, A., El Aroussi, M., & Saadane, R. (2024). Enhanced fault detection in photovoltaic panels using CNN-based classification with PyQt5 implementation. Sensors, 24(22), 7407. https://doi.org/10.3390/s24227407.
[27] Araji, M. T., Waqas, A., & Ali, R. (2024). Utilizing deep learning towards real-time snow cover detection and energy loss estimation for solar modules. Applied Energy, 375, 124201. https://doi.org/10.1016/j.apenergy.2024.124201.
[28] Alatwi, A. M., Albalawi, H., Wadood, A., Anwar, H., & El-Hageen, H. M. (2024). Deep learning-based dust detection on solar panels: A low-cost sustainable solution for increased solar power generation. Sustainability, 16(19), 8664. https://doi.org/10.3390/su16198664.
[29] Quiles-Cucarella, E., Sánchez-Roca, P., & Agustí-Mercader, I. (2025). Performance optimization of machine-learning algorithms for fault detection and diagnosis in PV systems. Electronics, 14(9), 1709. https://doi.org/10.3390/electronics14091709.
[30] Pujara, D., Ramirez, D., Tepedelenlioglu, C., Srinivasan, D., & Spanias, A. (2024). Real-time PV fault detection using embedded machine learning. In Proceedings of the IEEE ICPS. https://doi.org/10.1109/ICPS59941.2024.10640018.
[31] Kayci, B., Demir, B. E., & Demir, F. (2024). Deep learning-based fault detection and diagnosis in photovoltaic system using thermal images acquired by UAV. Politeknik Dergisi, 27(1), 91–99. https://doi.org/10.2339/politeknik.1094586.
[32] Afroz. (2023). Solar panel images clean and faulty images [Dataset]. Kaggle. https://www.kaggle.com/datasets/pythonafroz/solar-panel-images.
[33] Akinca, R., Firat, H., & Asker, M. E. (2024). Automated fault classification in solar panels using transfer learning with EfficientNet and ResNet models. European Journal of Technique, 14(2), 164–173.
[34] Nunes, M. V., & Ottoni, A. L. (2024). Deep learning for solar panels defect classification using data augmentation strategies. Learning Nonlinear Models – Journal of the Brazilian Society of Computational Intelligence, 22(2), 30–47.
[35] Ghahremani, A., Adams, S. D., Norton, M., Khoo, S. Y., & Kouzani, A. Z. (2025). Detecting defects in solar panels using the YOLO v10 and v11 algorithms. Electronics, 14(2), 344. https://doi.org/10.3390/electronics14020344.
[36] Gasparyan, H., Agaian, S., & Wu, S. (2025). Efficient lightweight networks for solar panel fault classification using EL and RGB imagery. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2025.3548249.
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