Enhanced Detection of Solar Panel Defects Using VGG16-Based Convolutional Neural Networks
DOI:
https://doi.org/10.32628/IJSRST25123136Keywords:
Solar Panel, Machine Learning, Image Classification, VGG16, Deep LearningAbstract
Solar energy is a renewable energy source that is expanding rapidly, and it is essential to ensure that solar panel installations are correctly monitored and maintained to ensure that they are operating at their full potential. Detection of various situations and irregularities in solar panel arrays may be accomplished through automated image classification algorithms, which hold great potential. Within the scope of this research, we offer a technique for categorizing images of solar panels that uses the VGG16 convolutional neural network (CNN) architecture. The dataset was taken from Kaggle, which contained 891 Images. The dataset was divided into six categories: Bird drop, Clean, Dusty, electrical damage, physical damage, and snow-covered. In this research, we implemented solar panel image classification using VGG16, a convolutional neural network and achieved a remarkable accuracy of 97.88%. This research exhibited performance compared to the state-of-the-art methods.
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W. Ahmed, A. Hanif, K. D. Kallu, A. Z. Kouzani, M. U. Ali, and A. Zafar, “Photovoltaic panels classification using isolated and transfer learned deep neural models using infrared thermographic images,” Sensors, vol. 21, no. 16, pp. 1–14, 2021.
M. A. Wani and T. Mujtaba, “Segmentation of Satellite Images of Solar Panels Using Fast Deep Learning Model,” Int. J. Renew. Energy Res., vol. 11, no. 1, pp. 31–45, 2021.
R. H. Fonseca Alves, G. A. de Deus Júnior, E. G. Marra, and R. P. Lemos, “Automatic fault classification in photovoltaic modules using Convolutional Neural Networks,” Renew. Energy, vol. 179, pp. 502–516, 2021.
S. Naveen Venkatesh and V. Sugumaran, “Fault Detection in aerial images of photovoltaic modules based on Deep learning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1012, no. 1, p. 012030, 2021.
X. Wang, W. Yang, and J. Wang, “Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning,” J. Dyn. Monit. Diagnostics, pp. 190–197, 2023.
J. A. Dhanraj et al., “An effective evaluation on fault detection in solar panels,” Energies, vol. 14, no. 22, pp. 1–14, 2021.
M. V. C. V. da Costa et al., “Remote sensing for monitoring photovoltaic solar plants in brazil using deep semantic segmentation,” Energies, vol. 14, no. 10, pp. 1–15, 2021.
B. Kim, R. O. Serfa Juan, D. E. Lee, and Z. Chen, “Importance of image enhancement and CDF for fault assessment of photovoltaic module using IR thermal image,” Appl. Sci., vol. 11, no. 18, 2021.
S. Park, Y. Kim, N. J. Ferrier, S. M. Collis, R. Sankaran, and P. H. Beckman, “Article prediction of solar irradiance and photovoltaic solar energy product based on cloud coverage estimation using machine learning methods,” Atmosphere (Basel)., vol. 12, no. 3, 2021.
F. Sun et al., “Dust Detection Techniques for Photovoltaic Panels from a Machine Vision Perspective: A Review,” Proc. - 2023 8th Asia Conf. Power Electr. Eng. ACPEE 2023, pp. 1413–1418, 2023.
K. C. Liao and J. H. Lu, “Using uav to detect solar module fault conditions of a solar power farm with ir and visual image analysis,” Appl. Sci., vol. 11, no. 4, pp. 1–21, 2021.
D. Dwivedi, K. V. S. M. Babu, P. K. Yemula, P. Chakraborty, and M. Pal, “Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model,” 2022.
X. Wang, W. Yang, B. Qin, K. Wei, Y. Ma, and D. Zhang, “Intelligent monitoring of photovoltaic panels based on infrared detection,” Energy Reports, vol. 8, pp. 5005–5015, 2022.
Y. Wang et al., “Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging,” Energy, vol. 229, 2021.
G. Terzoglou, M. Loufakis, P. Symeonidis, D. Ioannidis and D. Tzovaras, "Employing deep learning framework for improving solar panel defects using drone imagery," 2023 24th International Conference on Digital Signal Processing (DSP), Rhodes (Rodos), Greece, 2023, pp. 1-5, doi: 10.1109/DSP58604.2023.10167960.
Trong, Hieu, Luu., Phan, Nguyen, Ky, Phuc., T., Fai, Lam. (2023). Ensembling techniques in solar panel quality classification. International Journal of Power Electronics and Drive Systems, doi: 10.11591/ijece.v13i5.pp5674-5680
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