Deep Learning Architectures for Advanced Anomaly Detection in Solar Photovoltaic Systems
DOI:
https://doi.org/10.13052/spee1048-5236.45310Keywords:
Sustainability, deep learning, solar photovoltaic, anomaly detection, RGB imaging, EfficientNetV2B0, ResNet18, VGG16, DenseNetAbstract
Solar photovoltaic (PV) system operation and maintenance are important for achieving global sustainability objectives. Also, the reliability of PV is one thing that has always prevented it from reaching the full product guarantee due to hotspot, diode degradation, shading, and cracks issues. Traditional inspection methods are time-consuming, expensive, and can contain errors, which justifies the development of automation systems. This paper proposes a deep learning-based framework for anomaly detection using high-resolution RGB images, which overcomes the drawbacks of low-resolution grayscale datasets. To improve robustness, a dataset consisting of 20,000 PV module images with eleven fault categories and normal modules was systematically preprocessed by resizing, augmentation, and quality assurance. Six models, namely, CNN model, AlexNet, VGG16, ResNet18, DenseNet, and EfficientNetV2B0, were compared with each other through the evaluation metrics of accuracy, precision, recall, and F1 score. The experimental results show that the RGB transform can greatly benefit feature learning and model generalization. Overall, ResNet18 had the highest accuracy (91.1%) while EfficientNetV2B0 had balanced performance overall metrics. The results highlight Deep-Learning applications with fine quality datasets (i.e., achieving high performances of Anomaly Detection, Predictive Maintenance, and Sustainable Energy Generation).
Downloads
References
Ibrahim, M., Alsheikh, A., Awaysheh, F.M., Alshehri, M. (2021). Machine learning schemes for anomaly detection in solar power plants. Energies (MDPI), Preprint. https://doi.org/10.20944/preprints202112.0481.v1.
Pierdicca, R., Paolanti, M., Felicetti, A., Piccinini, F., Zingaretti, P. (2020). Automatic faults detection of photovoltaic farms: solAIr, a deep learning-based system for thermal images. Energies, MDPI. https://doi.org/10.3390/en13246496.
Voutsinas, S., Karolidis, D., Voyiatzis, I. (2022). Multi-output neural network for fault detection in PV Systems. Energy Reports, Elsevier BV. https://doi.org/10.1016/j.egyr.2022.06.107.
Bendary, A.F., Abdelaziz, A.Y., Ismail, M.M., Mahmoud, K., Lehtonen, M., Darwish, M.M.F. (2021). ANFIS Based Approach for Fault Tracking in PV System. Sensors, MDPI. https://doi.org/10.3390/s21072269.
Boubaker, S., Kamel, S., Ghazouani, N., Mellit, A. (2023). Machine and Deep Learning for Fault Diagnosis in PV Systems Using IR Thermography. Remote Sensing, MDPI. https://doi.org/10.3390/rs15061686.
Ahmad, M.W., Mourshed, M., Rezgui, Y. (2018). Tree-based ensemble methods for PV power generation prediction. Energy, Elsevier BV. https://doi.org/10.1016/j.energy.2018.08.207.
Bermejo, J.F., Fernández, J.F.G., Polo, F.A.O., Márquez, A.C. (2019). ANN Models for Energy and Reliability Prediction: PV, Hydraulic and Wind. Applied Sciences, MDPI. https://doi.org/10.3390/app9091844.
Rojek, I., Mikołajewski, D., Mróziński, A., Macko, M. (2023). AI Prediction for Smart Energy Systems with PV and Batteries. Energies, MDPI. https://doi.org/10.3390/en16186613.
Fan, Z., Yan, Z., Wen, S. (2023). Deep Learning and AI in Sustainability: A Review of SDGs, Renewable Energy, and Health. Sustainability, MDPI. https://doi.org/10.3390/su151813493.
Hijjawi, U., Lakshminarayana, S., Xu, T., Fierro, G.P.M., Rahman, M. (2024). Automated defect detection in solar photovoltaic systems using deep learning: Recent advances and challenges. Solar Energy, Elsevier. https://doi.org/10.1016/j.solener.2023.112186.
El-Banby, G.M., Moawad, N.M., Abouzalm, B.A., Abouzaid, W.F., Ramadan, E.A. (2024). Deep learning-based photovoltaic system fault detection techniques: A comprehensive review. Neural Computing and Applications, Springer. https://doi.org/10.1007/s00521-023-09041-7.
Boubaker, S., Kamel, S., Ghazouani, N., Mellit, A. (2024). Machine and deep learning techniques for photovoltaic fault diagnosis using infrared thermography. Remote Sensing, MDPI. https://doi.org/10.3390/rs15061686.
Ledmaoui, Y., El Maghraoui, A., El Aroussi, M., Saadane, R. (2024). CNN-based fault detection and classification of photovoltaic panels using RGB images. Sensors, MDPI. https://doi.org/10.3390/s24197407.
Sabati, A., Bensmail, A., Hachicha, W. (2025). Photovoltaic panel fault detection using convolutional neural networks and optimization techniques. International Journal of Computational Intelligence Systems, Springer. https://doi.org/10.1007/s44196-025-00984-4.
Wang, X., Wang, H., Bhandari, B., Cheng, L. (2023). AI-Empowered Methods for Smart Energy Consumption. International Journal of Precision Engineering and Manufacturing-Green Technology, Springer. https://doi.org/10.1007/s40684-023-005370.
Ahmed, C.M., Mathur, A. (2020). Challenges in Machine Learning-Based Approaches for Real-Time Anomaly Detection in ICS. ACM Digital Library. https://doi.org/10.1145/3384941.3409588.
Alnuaimi, K., Al-Sumaiti, A. S., Alansari, M., Wang, H., Al Hosani, K. H. (2025). Deep Learning-Based Health Monitoring for Photovoltaic Systems. IEEE Journal of Photovoltaics, vol. 15, no. 4, pp. 577–591. https://doi.org/10.1109/JPHOTOV.2025.3563887.
Malarkodi, K., Sivabalan, R., Jayalakshmi, D., Rajaram, G., Rubavathy, S. J., Nelson, L. (2024). Smart Anomaly Detection in Solar Power Plants with Auto Encoder and LSTM Integration. Proceedings of the Ninth International Conference on Communication and Electronics Systems (ICCES). https://doi.org/10.1109/ICCES63552.2024.10860097.
Zhang, Y., Liu, X., Chen, J., Wang, H. (2025). Advanced Deep Learning Solutions for Automated Diagnosis of Solar Panel Issues. Solar Energy, Elsevier. https://doi.org/10.1016/j.solener.2025.xxxxxx.
Ansari, S., Ayob, A., Lipu, M.S.H., Saad, M.H.M., Hussain, A. (2021). Review of Monitoring Technologies for PV Systems. Sustainability, MDPI. https://doi.org/10.3390/su13158120.
https://aeeeuropeenergy.com/
