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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
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