Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation
Abstract Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84365-5 |
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| author | Chouaib Labiod Redha Meneceur Ali Bebboukha Abdelmoumene Hechifa Kamel Srairi Adel Ghanem Ievgen Zaitsev Mohit Bajaj |
| author_facet | Chouaib Labiod Redha Meneceur Ali Bebboukha Abdelmoumene Hechifa Kamel Srairi Adel Ghanem Ievgen Zaitsev Mohit Bajaj |
| author_sort | Chouaib Labiod |
| collection | DOAJ |
| description | Abstract Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions. This paper introduces a diagnostic methodology for photovoltaic panels using I-V curves, enhanced by new techniques combining optimization and classification-based artificial intelligence. The research is organized into two key sections. The first section outlines the implementation of a DC/DC buck-boost converter, which is designed to extract and display real-time data from the PV system based on actual (I-V) measurements. The second section focuses on the comprehensive processing of the experimental dataset, where the Harris Hawks Optimization (HHO) algorithm is combined with machine learning methods to identify the most critical features. The HHO algorithm is combined with an advanced machine learning model, XGBoost, to accurately detect faults within the PV system. The proposed HHO-XGBoost algorithm achieves an impressive accuracy of 99.49%, outperforming other classification-based artificial intelligence methods in fault detection. In validation and comparison with previous approaches, the HHO-XGBoost model consistently outperforms established methods such as GADF-ANN, PCA-SVM, PNN, and Fuzzy Logic, achieving an overall accuracy of 98.48%. This outstanding performance confirms the model’s effectiveness in accurately diagnosing PV system conditions, further validating its robustness and reliability in fault detection and classification. |
| format | Article |
| id | doaj-art-91c1de625b6b43f39fcbe2dae885e209 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-91c1de625b6b43f39fcbe2dae885e2092025-01-05T12:17:50ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-024-84365-5Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementationChouaib Labiod0Redha Meneceur1Ali Bebboukha2Abdelmoumene Hechifa3Kamel Srairi4Adel Ghanem5Ievgen Zaitsev6Mohit Bajaj7Department of Mechanical Engineering, University of El OuedUDERZA Unit, Faculty of Technology, University of El OuedUDERZA Unit, Faculty of Technology, University of El OuedLGMM Laboratory, Faculty of Technology, University of 20 August 1955Laboratory of Energy Systems Modeling (LMSE), Department of Electrical Engineering, University of BiskraDepartment of Mechanical Engineering, University of El OuedDepartment of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of UkraineDepartment of Electrical Engineering, Graphic Era (Deemed to be University)Abstract Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions. This paper introduces a diagnostic methodology for photovoltaic panels using I-V curves, enhanced by new techniques combining optimization and classification-based artificial intelligence. The research is organized into two key sections. The first section outlines the implementation of a DC/DC buck-boost converter, which is designed to extract and display real-time data from the PV system based on actual (I-V) measurements. The second section focuses on the comprehensive processing of the experimental dataset, where the Harris Hawks Optimization (HHO) algorithm is combined with machine learning methods to identify the most critical features. The HHO algorithm is combined with an advanced machine learning model, XGBoost, to accurately detect faults within the PV system. The proposed HHO-XGBoost algorithm achieves an impressive accuracy of 99.49%, outperforming other classification-based artificial intelligence methods in fault detection. In validation and comparison with previous approaches, the HHO-XGBoost model consistently outperforms established methods such as GADF-ANN, PCA-SVM, PNN, and Fuzzy Logic, achieving an overall accuracy of 98.48%. This outstanding performance confirms the model’s effectiveness in accurately diagnosing PV system conditions, further validating its robustness and reliability in fault detection and classification.https://doi.org/10.1038/s41598-024-84365-5Photovoltaic SystemReal-Time Data MonitoringFault DetectionDC/DC Buck-Boost ConverterI-V characteristicsArtificial Intelligence |
| spellingShingle | Chouaib Labiod Redha Meneceur Ali Bebboukha Abdelmoumene Hechifa Kamel Srairi Adel Ghanem Ievgen Zaitsev Mohit Bajaj Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation Scientific Reports Photovoltaic System Real-Time Data Monitoring Fault Detection DC/DC Buck-Boost Converter I-V characteristics Artificial Intelligence |
| title | Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation |
| title_full | Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation |
| title_fullStr | Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation |
| title_full_unstemmed | Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation |
| title_short | Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation |
| title_sort | enhanced photovoltaic panel diagnostics through ai integration with experimental dc to dc buck boost converter implementation |
| topic | Photovoltaic System Real-Time Data Monitoring Fault Detection DC/DC Buck-Boost Converter I-V characteristics Artificial Intelligence |
| url | https://doi.org/10.1038/s41598-024-84365-5 |
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