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Boulanouar, Saadat, Boualem, Fengal. (1404). Solar panel fault diagnosis based on the intelligent recursive method. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 19(2), 1-14. doi: 10.57647/j.mjee.2025.1902.28
Saadat Boulanouar; Fengal Boualem. "Solar panel fault diagnosis based on the intelligent recursive method". نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 19, 2, 1404, 1-14. doi: 10.57647/j.mjee.2025.1902.28
Boulanouar, Saadat, Boualem, Fengal. (1404). 'Solar panel fault diagnosis based on the intelligent recursive method', نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 19(2), pp. 1-14. doi: 10.57647/j.mjee.2025.1902.28
Boulanouar, Saadat, Boualem, Fengal. Solar panel fault diagnosis based on the intelligent recursive method. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 1404; 19(2): 1-14. doi: 10.57647/j.mjee.2025.1902.28

Solar panel fault diagnosis based on the intelligent recursive method

Majlesi Journal of Electrical Engineering
مقاله 5، دوره 19، شماره 2، شهریور 2025، صفحه 1-14    اصل مقاله (867.57 K)
نوع مقاله: Reseach Article
شناسه دیجیتال (DOI): 10.57647/j.mjee.2025.1902.28
نویسندگان
Saadat Boulanouar* ؛ Fengal Boualem
Faculty of Technology, University of Chlef 02000 DZ, Algeria
چکیده
The solar panel or solar cell is one of the most important components of the solar system that produces electrical energy with high efficiency compatible with electrical loads, but any defect in this cell can cause its efficiency to decrease. The objective of this work is to establish a fault diagnosis method that can be implemented in a real structure. These faults are diagnosed and located by implementing an algorithm based on the measured values of the solar panel using an intelligent recursive least squares approach. Our objective is to contribute to the diagnosis of faults in photovoltaic systems based on fuzzy logic in a recurrent manner. The integration of recursive least squares (RLS) with fuzzy logic are essential to improve system efficiency and reliability. This approach enables rapid identification and resolution of faults, helping to avoid energy losses, reduce downtime, and support proactive maintenance. It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. Quantitatively, this method achieves high diagnostic accuracy (over 90%), reduces error rates by up to 30% under dynamic conditions, and
provides real-time fault detection with minimal latency. The combination of RLS and fuzzy logic improves fault diagnosis by effectively handling uncertainties and handling ambiguous situations better than traditional methods.

کلیدواژه‌ها
Solar panel؛ Fault diagnosis؛ Recursive least squares؛ Fuzzy logic
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