Solar panel fault diagnosis based on the intelligentrecursive method
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 diagn...
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| Format: | Article |
| Language: | English |
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OICC Press
2025-06-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/16929 |
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| author | Saadat Boulanouar Fengal Boualem |
| author_facet | Saadat Boulanouar Fengal Boualem |
| author_sort | Saadat Boulanouar |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-b7ca6aca793e4d15995110f3594de381 |
| institution | DOAJ |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-b7ca6aca793e4d15995110f3594de3812025-08-20T03:17:54ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962025-06-01192 (June 2025)10.57647/j.mjee.2025.1902.28Solar panel fault diagnosis based on the intelligentrecursive methodSaadat BoulanouarFengal Boualem 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. https://oiccpress.com/mjee/article/view/16929Solar panelFault diagnosisRecursive least squaresFuzzy logic |
| spellingShingle | Saadat Boulanouar Fengal Boualem Solar panel fault diagnosis based on the intelligentrecursive method Majlesi Journal of Electrical Engineering Solar panel Fault diagnosis Recursive least squares Fuzzy logic |
| title | Solar panel fault diagnosis based on the intelligentrecursive method |
| title_full | Solar panel fault diagnosis based on the intelligentrecursive method |
| title_fullStr | Solar panel fault diagnosis based on the intelligentrecursive method |
| title_full_unstemmed | Solar panel fault diagnosis based on the intelligentrecursive method |
| title_short | Solar panel fault diagnosis based on the intelligentrecursive method |
| title_sort | solar panel fault diagnosis based on the intelligentrecursive method |
| topic | Solar panel Fault diagnosis Recursive least squares Fuzzy logic |
| url | https://oiccpress.com/mjee/article/view/16929 |
| work_keys_str_mv | AT saadatboulanouar solarpanelfaultdiagnosisbasedontheintelligentrecursivemethod AT fengalboualem solarpanelfaultdiagnosisbasedontheintelligentrecursivemethod |