Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning
Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7684 |
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| author | Mohand Djeziri Ndricim Ferko Marc Bendahan Hiba Al Sheikh Nazih Moubayed |
| author_facet | Mohand Djeziri Ndricim Ferko Marc Bendahan Hiba Al Sheikh Nazih Moubayed |
| author_sort | Mohand Djeziri |
| collection | DOAJ |
| description | Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes. |
| format | Article |
| id | doaj-art-a735e0737b214e39be42bbdc0c7d86d6 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a735e0737b214e39be42bbdc0c7d86d62025-08-20T03:32:12ZengMDPI AGApplied Sciences2076-34172025-07-011514768410.3390/app15147684Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep LearningMohand Djeziri0Ndricim Ferko1Marc Bendahan2Hiba Al Sheikh3Nazih Moubayed4Aix-Marseille University, Université de Toulon, CNRS, IM2NP, 13397 Marseille, FranceAix-Marseille University, Université de Toulon, CNRS, IM2NP, 13397 Marseille, FranceAix-Marseille University, Université de Toulon, CNRS, IM2NP, 13397 Marseille, FranceFaculty of Engineering, City University, Tripoli 1300, LebanonLaRGES-CRSI, Faculty of Engineering, Lebanese University, Tripoli 1300, LebanonPhotovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes.https://www.mdpi.com/2076-3417/15/14/7684solar cellfault detection and identificationsimulationmonitoring |
| spellingShingle | Mohand Djeziri Ndricim Ferko Marc Bendahan Hiba Al Sheikh Nazih Moubayed Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning Applied Sciences solar cell fault detection and identification simulation monitoring |
| title | Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning |
| title_full | Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning |
| title_fullStr | Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning |
| title_full_unstemmed | Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning |
| title_short | Detection and Identification of Degradation Root Causes in a Photovoltaic Cell Based on Physical Modeling and Deep Learning |
| title_sort | detection and identification of degradation root causes in a photovoltaic cell based on physical modeling and deep learning |
| topic | solar cell fault detection and identification simulation monitoring |
| url | https://www.mdpi.com/2076-3417/15/14/7684 |
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