Solar ViT: Vision Transformer for Fault Detection in Solar PV Systems
About this article
Keywords:
Artificial Intelligence; Convolutional Neural Networks; Crack Detection; Vision Transformer; PhotovoltaicAbstract
Clean, renewable, and sustainable energy is vital for advancing social, economic, and environmental well-being, ultimately fostering productivity and long-term development. As solar photovoltaic (PV) systems become a cornerstone in global efforts to combat climate change and promote environmental sustainability, ensuring their reliable operation is increasingly important. Artificial Intelligence (AI) and Deep Learning (DL) have emerged as powerful enablers, offering advanced capabilities in monitoring, fault detection, and predictive maintenance of solar energy systems. These intelligent technologies enhance operational efficiency, minimize downtime, and support sustainable energy management. Blending AI and renewable energy supports key Sustainable Development Goals like clean energy, innovation, and climate action, driving both technological progress and environmental protection. This paper introduces SolarViT, a Vision Transformer-based deep learning model designed for precise fault detection in solar PV panels. By detecting issues like micro cracks and hotspots early, SolarViT helps prevent efficiency loss and reduce maintenance costs. Leveraging transfer learning, data augmentation, and ensemble methods, it delivers robust diagnostics while addressing interpretability, efficiency, and real-time deployment. This supports smarter solar infrastructure and promotes broader adoption of clean energy for sustainable development.
References
Joshua, S. R., Park, S., & Kwon, K. (2024). H2 URESONIC: Design of a Solar-Hydrogen University Renewable Energy System for a New and Innovative Campus. Applied Sciences, 14(4), 1554. https://doi.org/10.3390/app14041554
Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., & Kim, K. H. (2018). Solar energy: Potential and future prospects. Renewable and Sustainable Energy Reviews, 82, 894-900. https://doi.org/10.1016/j.rser.2017.09.094
Sovacool, B. K., Mullard, S., & Ceballos, J. C. (2024). “Made for corruption?” Private sector actors, renewable energy, and corruption risks for wind power in Mexico and solar electricity in Kenya. The Electricity Journal, 37(7-10), 107448. https://doi.org/10.1016/j.tej.2024.107448
Izam, N. S. M. N., Itam, Z., Sing, W. L., & Syamsir, A. (2022). Sustainable development perspectives of solar energy technologies with focus on solar Photovoltaic—A review. Energies, 15(8), 2790. https://doi.org/10.3390/en15082790
Hasan, M. M., Hossain, S., Mofijur, M., Kabir, Z., Badruddin, I. A., Yunus Khan, T. M., & Jassim, E. (2023). Harnessing solar power: a review of photovoltaic innovations, solar thermal systems, and the dawn of energy storage solutions. Energies, 16(18), 6456. https://doi.org/10.3390/en16186456
View more references (43)
Saffari, M., Khodayar, M., & Khodayar, M. E. (2022). Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation. The Electricity Journal, 35(5), 107137. https://doi.org/10.1016/j.tej.2022.107137
Sharif, A., Meo, M. S., Chowdhury, M. A. F., & Sohag, K. (2021). Role of solar energy in reducing ecological footprints: An empirical analysis. Journal of Cleaner Production, 292, 126028. https://doi.org/10.1016/j.jclepro.2021.126028
Duranay, Z. B. (2023). Fault detection in solar energy systems: A deep learning approach. Electronics, 12(21), 4397. https://doi.org/10.3390/electronics12214397
Joshua, S. R., Yeon, A. N., Park, S., & Kwon, K. (2024). A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an A IoT-Based Solar–Hydrogen System in a University Setting. Applied Sciences, 14(18), 8573. https://doi.org/10.3390/app14188573
Joshua, S. R., Junghyun, Y., Park, S., & Kwon, K. (2024). Integrating Deep Learning and Energy Management Standards for Enhanced Solar–Hydrogen Systems: A Study Using MobileNetV2, InceptionV3, and ISO 50001: 2018. Hydrogen, 5(4), 819-850. https://doi.org/10.3390/hydrogen5040043
El-Sawy, A., El-Bakry, H., & Loey, M. (2017). CNN for handwritten arabic digits recognition based on LeNet-5. In Proceedings of the Interna-tional Conference on Advanced Intelligent Systems and Informatics 2016 2 (pp. 566-575). Springer International Publishing. https://doi.org/10.1007/978-3-319-48308-5_54
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural infor-mation processing systems, 25. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
Pasban, S., Mohamadzadeh, S., Zeraatkar‐Moghaddam, J., & Shafiei, A. K. (2020). Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks. IET Image Processing, 14(17), 4756-4765. https://doi.org/10.1049/iet-ipr.2020.0469
Sam, S. M., Kamardin, K., Sjarif, N. N. A., & Mohamed, N. (2019). Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3. Procedia Computer Science, 161, 475-483. https://doi.org/10.1016/j.procs.2019.11.147
Borawar, L., & Kaur, R. (2023, March). ResNet: Solving vanishing gradient in deep networks. In Proceedings of International Conference on Re-cent Trends in Computing: ICRTC 2022 (pp. 235-247). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8825-7_21
Tsivgoulis, M., Papastergiou, T., & Megalooikonomou, V. (2022). An improved SqueezeNet model for the diagnosis of lung cancer in CT scans. Machine Learning with Applications, 10, 100399. https://doi.org/10.1016/j.mlwa.2022.100399
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., ... & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6881-6890). https://doi.org/10.48550/arXiv.2012.15840
Kumar, M., Weissenborn, D., & Kalchbrenner, N. (2021). Colorization transformer. arXiv preprint arXiv:2102.04432. https://doi.org/10.48550/arXiv.2102.04432
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., ... & Gao, W. (2021). Pre-trained image processing transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12299-12310). https://doi.org/10.48550/arXiv.2012.00364
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., & Schmid, C. (2021). Vivit: A video vision transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6836-6846). https://doi.org/10.48550/arXiv.2103.15691
Ramadan, E. A., Moawad, N. M., Abouzalm, B. A., Sakr, A. A., Abouzaid, W. F., & El-Banby, G. M. (2024). An innovative transformer neural network for fault detection and classification for photovoltaic modules. Energy Conversion and Management, 314, 118718. https://doi.org/10.1016/j.enconman.2024.118718
De Luis, A., Tran, M., Hanyu, T., Tran, A., Haitao, L., McCann, R., ... & Le, N. (2024, May). Solarformer: Multi-scale transformer for solar pv pro-filing. In 2024 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA) (pp. 1-8). IEEE. 10.1109/SGSMA58694.2024.10571461
Dwivedi, D., Babu, K. V. S. M., Yemula, P. K., Chakraborty, P., & Pal, M. (2024). Identification of surface defects on solar pv panels and wind turbine blades using attention based deep learning model. Engineering Applications of Artificial Intelligence, 131, 107836. https://doi.org/10.1016/j.engappai.2023.107836Get rights and content
Xie, X., Liu, H., Na, Z., Luo, X., Wang, D., & Leng, B. (2021). DPiT: Detecting defects of photovoltaic solar cells with image transformers. IEEE Access, 9, 154292-154303. 10.1109/ACCESS.2021.3119631
Adhya, D., Chatterjee, S., & Chakraborty, A. K. (2022). Performance assessment of selective machine learning techniques for improved PV array fault diagnosis. Sustainable Energy, Grids and Networks, 29, 100582. https://doi.org/10.1016/j.segan.2021.100582
Pamungkas, R. F., Utama, I. B. K. Y., & Jang, Y. M. (2023). A novel approach for efficient solar panel fault classification using coupled udensenet. Sensors, 23(10), 4918. https://doi.org/10.3390/s23104918
Badr, M. M., Hamad, M. S., Abdel-Khalik, A. S., Hamdy, R. A., Ahmed, S., & Hamdan, E. (2021). Fault identification of photovoltaic array based on machine learning classifiers. IEEE Access, 9, 159113-159132. 10.1109/ACCESS.2021.3130889
Rao, S., Spanias, A., & Tepedelenlioglu, C. (2019, May). Solar array fault detection using neural networks. In 2019 IEEE international conference on industrial cyber physical systems (ICPS) (pp. 196-200). IEEE. 10.1109/ICPHYS.2019.8780208
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
Venkatesh, S. N., & Sugumaran, V. (2021). Fault Detection in aerial images of photovoltaic modules based on Deep learning. In IOP Conference Series: Materials Science and Engineering (Vol. 1012, No. 1, p. 012030). IOP Publishing. 10.1088/1757-899X/1012/1/012030
Sridharan, N. V., & Sugumaran, V. (2021). Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning fea-tures. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-17. https://doi.org/10.1080/15567036.2021.2020379
Fazai, R., Abodayeh, K., Mansouri, M., Trabelsi, M., Nounou, H., Nounou, M., & Georghiou, G. E. (2019). Machine learning-based statistical test-ing hypothesis for fault detection in photovoltaic systems. Solar Energy, 190, 405-413. https://doi.org/10.1016/j.solener.2019.08.032
Rudro, R. A. M., Nur, K., Al Sohan, M. F. A., 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
Shihavuddin, A. S. M., Rashid, M. R. A., Maruf, M. H., Hasan, M. A., ul Haq, M. A., Ashique, R. H., & Al Mansur, A. (2021). Image based sur-face damage detection of renewable energy installations using a unified deep learning approach. Energy Reports, 7, 4566-4576. https://doi.org/10.1016/j.egyr.2021.07.045
El-Banby, G. M., Moawad, N. M., Abouzalm, B. A., Abouzaid, W. F., & Ramadan, E. A. (2023). Photovoltaic system fault detection techniques: a review. Neural Computing and Applications, 35(35), 24829-24842. https://doi.org/10.1007/s00521-023-09041-7
Memon, S. A., Javed, Q., Kim, W. G., Mahmood, Z., Khan, U., & Shahzad, M. (2022). A machine-learning-based robust classification method for PV panel faults. Sensors, 22(21), 8515. https://doi.org/10.3390/s22218515
Abubakar, A., Jibril, M. M., Almeida, C. F., Gemignani, M., Yahya, M. N., & Abba, S. I. (2023). A novel hybrid optimization approach for fault detection in photovoltaic arrays and inverters using AI and statistical learning techniques: a focus on sustainable environment. Processes, 11(9), 2549. https://doi.org/10.3390/pr11092549
Eltuhamy, R. A., Rady, M., Almatrafi, E., Mahmoud, H. A., & Ibrahim, K. H. (2023). Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme. Sensors, 23(3), 1280. https://doi.org/10.3390/s23031280
Amaral, T. G., Pires, V. F., & Pires, A. J. (2021). Fault detection in PV tracking systems using an image processing algorithm based on PCA. Ener-gies, 14(21), 7278. https://doi.org/10.3390/en14217278
Chen, L., Li, S., & Wang, X. (2016). Quickest fault detection in photovoltaic systems. IEEE Transactions on Smart Grid, 9(3), 1835-1847. 10.1109/TSG.2016.2601082
Sharir, G., Noy, A., & Zelnik-Manor, L. (2021). An image is worth 16x16 words, what is a video worth?. arXiv preprint arXiv:2103.13915. https://doi.org/10.48550/arXiv.2103.13915
Xie, X., Liu, H., Na, Z., Luo, X., Wang, D., & Leng, B. (2021). DPiT: Detecting defects of photovoltaic solar cells with image transformers. IEEE Access, 9, 154292-154303. https://doi.org/10.1109/ACCESS.2021.3119631
Ramadan, E. A., Moawad, N. M., Abouzalm, B. A., Sakr, A. A., Abouzaid, W. F., & El-Banby, G. M. (2024). An innovative transformer neural network for fault detection and classification for photovoltaic modules. Energy Conversion and Management, 314, 118718. https://doi.org/10.1016/j.enconman.2024.118718
Khalil, I. U., Haq, A. U., & ul Islam, N. (2024). A deep learning-based transformer model for photovoltaic fault forecasting and classification. Elec-tric Power Systems Research, 228, 110063. https://doi.org/10.1016/j.epsr.2023.110063
Mahboob, Z., Khan, M. A., Lodhi, E., Nawaz, T., & Khan, U. S. (2024). Using segFormer for effective semantic cell segmentation for fault detec-tion in photovoltaic arrays. IEEE Journal of Photovoltaics. https://doi.org/10.1109/JPHOTOV.2024.3450009.
Y. Kumar, S. K. Verma, and S. Sharma, “Multi-pose facial expression recognition using hybrid deep learning model with improved variant of gravi-tational search algorithm.,” Int.Arab J. Inf. Technol., vol. 19, no. 2, pp. 281–287, 2022.
Kumar, Y., Verma, S.K. and Sharma, S., 2023. Facial expression recognition of multiple stylised characters using deep convolutional neural net-work. International Journal of Advanced Intelligence Paradigms, 26(3-4), pp.362-391.