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Sharma, Poorva, Yadav, Anamika. (1404). Partial Shading Detection of Solar Panels Using Ensemble Bagged Trees Algorithm. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/joape.2025.15344.2172
Poorva Sharma; Anamika Yadav. "Partial Shading Detection of Solar Panels Using Ensemble Bagged Trees Algorithm". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22098/joape.2025.15344.2172
Sharma, Poorva, Yadav, Anamika. (1404). 'Partial Shading Detection of Solar Panels Using Ensemble Bagged Trees Algorithm', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/joape.2025.15344.2172
Sharma, Poorva, Yadav, Anamika. Partial Shading Detection of Solar Panels Using Ensemble Bagged Trees Algorithm. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22098/joape.2025.15344.2172

Partial Shading Detection of Solar Panels Using Ensemble Bagged Trees Algorithm

Journal of Operation and Automation in Power Engineering
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 06 آبان 1404    اصل مقاله (4.72 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2025.15344.2172
نویسندگان
Poorva Sharma؛ Anamika Yadav*
Department of Electrical Engineering, National Institute of Technology, Raipur, CG, India.
چکیده
The adoption of photovoltaic (PV) energy is growing rapidly, leading to the installation of numerous solar power plants to meet rising electricity demand. Among the various operational challenges, partial shading significantly reduces the power output of PV panels. During routine maintenance, panels are typically cleaned to remove debris such as dust, dirt, or bird droppings—common causes of shading. However, shading severity varies across panels, especially in large PV installations, making uniform cleaning inefficient. To address this, the paper proposes a machine learning-based methodology for detecting and quantifying partial shading at the panel level. By analyzing power output, irradiance, and ambient temperature data, the proposed approach identifies the panels most affected by shading and classifies the severity into low, medium, and high categories. This enables smart maintenance prioritization and early fault prediction, preventing issues such as hotspots and module degradation while reducing operational costs. In a comparative study of eleven Machine Learning models, the ensemble bagged trees algorithm achieved the best performance 90% accuracy in identifying the most affected panels and 93.5% accuracy in classifying shading levels. The proposed solution is well-suited for real-time deployment in solar parks, offering an effective tool for predictive maintenance and optimized plant operations.
کلیدواژه‌ها
Partial shading؛ photovoltaic system؛ machine learning technique؛ modeling؛ simulation
مراجع
  1. S. Behzadi, N. Osali, A. Younesi, and A. Bagheri, “A costeffective trade-off in distribution system expansion planning between construction of conventional/renewable distributed energy sources in long term,” J. Oper. Autom. Power Eng., vol. 13, no. 3, pp. 196–205, 2025.
  2. Energy World, “India now has 70,000 mw of solar power generation capacity; rajasthan leading the pack.” https://energy.economictimes.indiatimes.com/news/renewable /india-now-has-70000-mw-of-solar-power-generation-capaci ty-rajasthan-leading-the-pack/102577162, 2023. Accessed: 2025-09-01.
  3. M. Dhimish and A. M. Tyrrell, “Photovoltaic bypass diode fault detection using artificial neural networks,” IEEE Trans. Instrum. Meas., vol. 72, 2023.
  4. IEA, “Snapshot 2021.” https://iea-pvps.org/snapshot-reports/ snapshot-2021. Online report.
  5. M. S. Syed, C. V. Suresh, and S. Sivanagaraju, “Impact of renewable sources on electrical power system,” J. Oper. Autom. Power Eng., vol. 12, no. 3, pp. 261–268, 2024.
  6. M. Gupta, “Explained: Mitigation techniques for dust and soil effects on solar panel performance.” https:
    //solarquarter.com/2023/05/26/explained-mitigation-techniques-for-dust-and-soil-effects-on-solar-panel-performance/, 2023. Solarquarter.
  7. 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.
  8. G. Abdelaziz, H. Hichem, B. R. Chiheb, and G. Rached, “Shading effect on the performance of a photovoltaic panel,” in 2021 IEEE 2nd Int. Conf. Signal, Control Commun., pp. 208–213, IEEE, 2021.
  9. N. Khadka, A. Bista, B. Adhikari, A. Shrestha, D. Bista, and B. Adhikary, “Current practices of solar photovoltaic panel cleaning system and future prospects of machine learning implementation,” IEEE Access, vol. 8, pp. 135948–135962, 2020.
  10. L. B. Bosman, W. D. Leon-Salas, W. Hutzel, and E. A. Soto, “PV system predictive maintenance: Challenges, current approaches, and opportunities,” Energies, vol. 13, no. 6, p. 1398, 2020.
  11. K. Abdulmawjood, S. Alsadi, S. S. Refaat, and W. G. Morsi, “Characteristic study of solar photovoltaic array under different partial shading conditions,” IEEE Access, vol. 10, pp. 6856–6866, 2022.
  12. F. Ramezani and M. Mirhosseini, “Shading impact modeling on photovoltaic panel performance,” Renew. Sustain. Energy Rev., vol. 212, p. 115432, 2025.
  13. M. Khan, M. Ferdous, and S. Chowdhury, “Sustainable development of solar power through the investigation of partial shading effect of solar module in terms of experimental setup and MATLAB simulation,” Procedia Comput. Sci., vol. 252, pp. 702–707, 2025.
  14. S. M. Abdallah, M. N. Abdullah, I. Musirin, and A. M. Elshamy, “Intelligent solar panel monitoring system and shading detection using artificial neural networks,” Energy Rep., vol. 9, pp. 324–334, 2023.
  15. A. K. Tripathi, M. Aruna, and C. S. N. Murthy, “Performance of a PV panel under different shading strengths,” Int. J. Ambient Energy, vol. 40, no. 3, pp. 248–253, 2019.
  16. S. Ansari, A. Ayob, M. S. H. Lipu, M. H. M. Saad, and A. Hussain, “A review of monitoring technologies for solar PV systems using data processing modules and transmission protocols: Progress, challenges and prospects,” Sustainability, vol. 13, no. 15, 2021.
  17. T. Kappler, A. S. Starosta, N. Munzke, B. Schwarz, and M. Hiller, “Detection of shading for solar power forecasting using machine learning techniques,” in Proc. Eur. PV Solar Energy Conf. Exhib., 2023.
  18. E. A. Setiawan, M. Fathurrahman, R. F. Pamungkas, and S. Ma’Arif, “Fast partial shading detection on pv modules for precise power loss ratio estimation using digital image processing,” J. Electr. Comput. Eng., vol. 2024, p. 9385602, 2024.
  19. F. Belhachat and C. Larbes, “Photovoltaic array reconfiguration strategies for mitigating partial shading effects: Recent advances and perspectives,” Energy Convers. Manage., vol. 313, p. 118547, 2024.
  20. S. Ganesan, P. W. David, P. K. Balachandran, and I. Colak, “Power enhancement in pv arrays under partial shaded conditions with different array configuration,” Heliyon, vol. 10, no. 2, 2024.
  21. R. Verma, S. Gupta, and A. Yadav, “Performance evaluation of different photovoltaic array configurations under partial shading,” Renew. Energy, vol. 237, p. 121796, 2024.
  22. S. Saravanan, R. S. Kumar, and P. Balakumar, “Binary firefly algorithm based reconfiguration for maximum power extraction under partial shading and machine learning approach for fault detection in solar pv arrays,” Appl. Soft Comput., vol. 154, p. 111318, 2024.
  23. Y. Wang and B. Yang, “Optimal pv array reconfiguration under partial shading condition through dynamic leader based collective intelligence,” Prot. Control Mod. Power Syst., vol. 8, no. 1, 2023.
  24. V. Jain, R. Singh, R. Yadav, V. K. Yadav, V. Kumar, and S. Garg, “Multi-step optimization for reconfiguration of solar pv array for optimal shade dispersion,” Electr. Eng., 2024.
  25. P. R. Satpathy, B. Aljafari, S. B. Thanikanti, N. Nwulu, and R. Sharma, “A multi-string differential power processing based voltage equalizer for partial shading detection and mitigation in pv arrays,” Alex. Eng. J., vol. 104, pp. 12–30, 2024.
  26. B. Liu et al., “A novel data-driven state evaluation approach for photovoltaic arrays in uncertain shading scenarios,” Energy, vol. 312, p. 133533, 2024.
  27. V. Sarkar, V. K. Kolakaluri, and S. Anantha, “Enhancing the maximum or flexible power point tracking control of a photovoltaic array with a non-invasive and computationally robust model-based method for partial shading detection,” Electr. Power Syst. Res., vol. 238, p. 111096, 2025.
  28. U. Kumar, S. Mishra, and K. Dash, “An iot and semisupervised learning-based sensorless technique for panel level solar photovoltaic array fault diagnosis,” IEEE Trans. Instrum. Meas., vol. 72, 2023.
  29. J. Zhang et al., “An i–v characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings,” Energy, vol. 300, p. 131480, 2024.
  30. A. Ahmadpour, A. Dejamkhooy, and H. Shayeghi, “Optimization and modelling of linear fresnel reflector solar concentrator using various methods based on monte carlo ray–trace,” Sol. Energy, vol. 245, pp. 67–79, 2022.
  31. B. Nomeir, S. Lakhouil, S. Boukheir, M. A. Ali, and S. Naamane, “Recent progress on transparent and selfcleaning surfaces by superhydrophobic coatings deposition to optimize the cleaning process of solar panels,” Sol. Energy Mater. Sol. Cells, vol. 257, p. 112347, 2023.
  32. Y. Wu et al., “A review of self-cleaning technology to reduce dust and ice accumulation in photovoltaic power generation using superhydrophobic coating,” Renew. Energy, vol. 185, pp. 1034–1061, 2022.
  33. V. P. Madhanmohan, M. Nandakumar, and A. Saleem, “Enhanced performance of partially shaded photovoltaic arrays using diagonally dispersed total cross tied configuration,” Energy Sources A: Recovery Util. Environ. Eff., 2020.
  34. D. Craciunescu and L. Fara, “Investigation of the partial shading effect of photovoltaic panels and optimization of their performance based on high-efficiency flc algorithm,” Energies, vol. 16, no. 3, 2023.
  35. S. Selvi, M. Mohanraj, P. Duraipandy, S. Kaliappan, L. Natrayan, and N. Vinayagam, “Optimization of solar panel orientation for maximum energy efficiency,” in 2023 4th Int. Conf. Smart Electron. Commun., pp. 159–162, 2023.
  36. A. K. Gupta, T. Maity, A. H, and Y. K. Chauhan, “An electromagnetic strategy to improve the performance of pv panel under partial shading,” Comput. Electr. Eng., vol. 90, p. 106896, 2021.
  37. V. M. R. Tatabhatla, A. Agarwal, and T. Kanumuri, “A chaos map based reconfiguration of solar array to mitigate the effects of partial shading,” IEEE Trans. Energy Convers., vol. 37, pp. 811–823, 2022.
  38. H. Oufettoul, N. Lamdihine, S. Motahhir, N. Lamrini, I. A. Abdelmoula, and G. Aniba, “Comparative performance analysis of pv module positions in a solar pv array under partial shading conditions,” IEEE Access, vol. 11, pp. 12176–12194, 2023.
  39. M. Baghoolizadeh, A. A. Nadooshan, A. Raisi, and E. H. Malekshah, “The effect of photovoltaic shading with ideal tilt angle on the energy cost optimization of a building model in european cities,” Energy Sustainable Dev., vol. 71, pp. 505–516, 2022.
  40. L. El Iysaouy et al., “Performance enhancements and modelling of photovoltaic panel configurations during partial shading conditions,” Energy Syst., 2023.
  41. M. A. Raza et al., “Mitigating the impact of partial shading conditions on photovoltaic arrays through modified bridge-linked configuration,” Sustainability, vol. 17, no. 3, 2025.
  42. P. Venkata, V. Pandya, and A. V. Sant, “Data mining and svm based fault diagnostic analysis in modern power system using time and frequency series parameters calculated from full-cycle moving window,” J. Oper. Autom. Power Eng., vol. 12, no. 3, pp. 206–214, 2024.
  43. V. Singh, A. Yadav, S. Gupta, and A. Y. Abdelaziz, “Switch fault identification scheme based on machine learning algorithms for pv-fed three-phase neutral point clamped inverter,” e-Prime Adv. Electr. Eng. Electron. Energy, vol. 8, 2024.
  44. A. Nagpal, “Decision tree ensembles- bagging and boosting.” https://medium.com/towards-data-science/decision -tree-ensembles-bagging-and-boosting-266a8ba60fd9, 2020.
  45. A. Raj and R. P. Praveen, “Highly efficient dc-dc boost converter implemented with improved mppt algorithm for utility level photovoltaic applications,” Ain Shams Eng. J., vol. 13, no. 3, 2022.
  46. S. M. Pizer and J. S. Marron, “Object statistics on curved manifolds,” in Stat. Shape Deform. Anal.: Methods Implementation Appl., pp. 137–164, 2017.
  47. N. Acharya, “Understanding precision, recall, f1score, and support in machine learning evaluation.”
    https://medium.com/@nirajan.acharya777/understandingprecision-recall-f1-score-and-support-in-machine-learningevaluation-7ec935e8512e, 2023.
  48. Keylabs, “Understanding the f1 score and auc-roc curve.” https://keylabs.ai/blog/understanding-the-f1-score-and -auc-roc-curve/, 2023.
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