FOTOVOLTAIK PANELLARDA IFLOSLANISH VA NUQSONLARNI CHUQUR O‘RGANISH ASOSIDA AVTOMATIK ANIQLASH

Referat

Fotovoltaik (FV) panellar yuzasining changlanishi hamda mexanik va elektr nuqsonlari quyosh elektr stansiyalarida energiya ishlab chiqarishning pasayishiga olib keluvchi asosiy omillardan biri hisoblanadi. Bu muammo Markaziy Osiyo kabi qurg‘oqchil va changli mintaqalarda ayniqsa keskin namoyon bo‘ladi. Yirik FV massivlarni qo‘lda ko‘zdan kechirish ko‘p mehnat talab qiladi, uzoq vaqt oladi va xatoliklarga moyil. Ushbu maqolada FV panellar yuzasi holatini oltita sinfga — toza, changli, qush axlati bilan ifloslangan, elektr shikastlangan, mexanik shikastlangan va qor bilan qoplangan — avtomatik tasniflovchi yengil hamda natijalarini izohlash mumkin bo‘lgan chuqur o‘rganish tizimi taklif etilgan. ImageNet ma’lumotlar to‘plamida oldindan o‘qitilgan MobileNetV3 - Large neyron tarmog‘i asosida transfer o‘qitish usuli qo‘llanilgan. Sinflar o‘rtasidagi nomutanosiblikni kamaytirish uchun sinflar bo‘yicha vaznlangan yo‘qotish funksiyasi va ma’lumotlarni kengaytirish usullaridan foydalanilgan. Shikastlangan sohalarni aniqlash uchun Grad-CAM usuli integratsiya qilingan. Tizim aniqlangan har bir holat uchun ilmiy adabiyotlarga asoslangan taxminiy quvvat yo‘qotish oralig‘ini belgilaydi va natijalarni o‘zbek tilidagi FastAPI veb - ilovasi orqali foydalanuvchiga taqdim etadi. 3065 ta tasvirdan iborat ochiq ma’lumotlar to‘plamida model 78,81% validatsiya aniqligiga, 0,86 makro- o‘rtacha to‘liqlikka (recall) va 0,79 vaznlangan F 1 ko‘rsatkichiga erishgan. Barcha hisoblashlar grafik tezlatkichsiz, faqat markaziy protsessorda bajarilgan. Kam uchraydigan, biroq ekspluatatsiya nuqtayi nazaridan muhim sinflar — qor bilan qoplangan (0,98), qush axlati bilan ifloslangan (0,90) va elektr shikastlangan (0,90) panellar — yuqori to‘liqlik bilan aniqlangan. Taklif etilgan yondashuv quyosh energetikasi jadal rivojlanayotgan mintaqalarda texnik xizmat ko‘rsatish bo‘yicha qarorlar qabul qilishni qo‘llab -quvvatlovchi arzon va amaliy vosita hisoblanadi.

Kalit so'zlar

Adabiyotlar

1. IRENA, “Renewable Capacity Statistics 2025,” Abu Dhabi, 2025.
2. T. Sarver, A. Al- Qaraghuli, and L. L. Kazmerski, “A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches,” Renewable and Sustainable Energy Reviews, vol. 22, pp. 698 – 733, 2013, doi: 10.1016/j.rser.2012.12.065.
3. A. Sayyah, M. N. Horenstein, and M. K. Mazumder, “Energy yield loss caused by dust deposition on photovoltaic panels,” Solar Energy, vol. 107, pp. 576 – 604, 2014, doi: 10.1016/j.solener.2014.05.030.
4. M. R. Maghami, H. Hizam, C. Gomes, M. A. Radzi, M. I. Rezadad, and S. Hajighorbani, “Power loss due to soiling on solar panel: A review,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 1307 – 1316, 2016, doi: 10.1016/j.rser.2016.01.044.
5. K. Ilse et al., “Techno -economic assessment of soiling losses and mitigation strategies for solar power generation,” Joule, vol. 3, no. 10, pp. 2303 – 2321, 2019, doi: 10.1016/j.joule.2019.08.019.
6. D. S. Pillai and N. Rajasekar, “A comprehensive review on protection challenges and fault diagnosis in PV systems,” Renewable and Sustainable Energy Reviews, vol. 91, pp. 18 – 40, 2018, doi: 10.1016/j.rser.2018.03.082.
7. A. Mellit and S. A. Kalogirou, “Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems,” Renewable and Sustainable Energy Reviews, vol. 143, p. 110889, 2021, doi: 10.1016/j.rser.2021.110889.
8. Y. Zefri, A. ElKettani, I. Sebari, and S. Ait Lamallam, “Thermal infrared and visual inspection of photovoltaic installations by UAV photogrammetry: Application case Morocco,” Remote Sens. (Basel)., vol. 10, no. 12, p. 1874, 2018, doi: 10.3390/rs10121874.
9. S. Mehta, A. P. Azad, S. A. Chemmengath, V. Raykar, and S. Kalyanaraman, “DeepSolarEye: Power loss prediction and weakly supervised soiling localization via fully convolutional networks for INNOVATSION TEXNOLOGIYALAR INNOVATIVE TECHNOLOGIES ИННОВАЦИОННЫЕ ТЕХ НОЛОГИИ 2026 - yil 3 (63) - son 2026 volume 63, number 3 Том 63 No 3, 202 6 ISSN 2181 - 4732 E - ISSN 2181 - 4 015 123 solar panels,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 333 – 342. doi: 10.1109/WACV.2018.00043.
10. M. Yang, J. Ji, and B. Guo, “Soiling quantification using an image -based method: Effects of imaging conditions,” IEEE J. Photovolt., vol. 10, no. 6, pp. 1780 – 1787, 2020, doi: 10.1109/JPHOTOV.2020.3018493.
11. S. Fan, Y. Wang, S. Cao, B. Zhao, T. Sun, and P. Liu, “A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels,” Energy, vol. 239, p. 122302, 2022, doi: 10.1016/j.energy.2021.122302.
12. A. Et- Taleby, Y. Chaibi, M. Boussetta, and M. Benslimane, “Application of deep learning based detector YOLOv5 for soiling recognition in photovoltaic modules,” in Proceedings of the 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Mohammedia, Morocco, 2023, pp. 1 – 6. doi: 10.1109/IRASET57153.2023.10152990.
13. M. W. Akram, G. Li, Y. Jin, X. Chen, C. Zhu, and A. Ahmad, “Automatic dete ction of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning,” Solar Energy, vol. 198, pp. 175 – 186, 2020, doi: 10.1016/j.solener.2020.01.055.
14. Á. H. Herraiz, A. P. Marugán, and F. P. G. Márquez, “Photov oltaic plant condition monitoring using thermal images analysis by convolutional neural network- based structure,” Renew. Energy, vol. 153, pp. 334 – 348, 2020, doi: 10.1016/j.renene.2020.01.148.
15. S. Deitsch et al., “Automatic classification of defective photovoltaic module cells in electroluminescence images,” Solar Energy, vol. 185, pp. 455 – 468, 2019, doi: 10.1016/j.solener.2019.02.067.
16. J. Wang et al., “Deep -learning-based automatic detection of photovoltaic cell defects in electroluminescence image s,” Sensors, vol. 23, no. 1, p. 297, 2023, doi: 10.3390/s23010297.
17. N. Ledmaoui, A. El Maghraoui, M. El Aroussi, and R. Saadane, “Enhanced fault detection in photovoltaic panels using CNN- based classification with PyQt5 implementation,” Sensors, vol. 24, no. 22, p. 7407, 2024, doi: 10.3390/s24227407.
18. M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,” Neural Networks, vol. 106, pp. 249 – 259, 2018, doi: 10.1016/j.neunet.2018.07.011.
19. Kaggle, “Solar Panel Images: Clean and Faulty.” [Online]. Available: https://www.kaggle.com/datasets
20. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei- Fei, “ImageNet: A large -scale hierarchical image database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 248 – 255. doi: 10.1109/CVPR.2009.5206848.
21. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” in Advances in Neural Information Processing Systems (NeurIPS), 2014, pp. 3320 – 3328.
22. M. Tan and Q. V Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR, 2019, pp. 6105 – 6114.
23. Y. Cui, M. Jia, T.- Y. Lin, Y. Song, and S. Belongie, “Class -balanced loss based on effective number of samples,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9268 – 9277. doi: 10.1109/CVPR.2019.00949.
24. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad -CAM: Visual explanations from deep networks via gradient- based localization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618 – 626. doi: 10.1109/ICCV.2017.74.
25. T.- Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980 – 2988. doi: 10.1109/ICCV.2017.324.