Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques
ABSTRACT The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper intr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Applied AI Letters |
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| Online Access: | https://doi.org/10.1002/ail2.115 |
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| _version_ | 1849330322735366144 |
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| author | Ghalia Nassreddine Amal El Arid Mohamad Nassereddine Obada Al Khatib |
| author_facet | Ghalia Nassreddine Amal El Arid Mohamad Nassereddine Obada Al Khatib |
| author_sort | Ghalia Nassreddine |
| collection | DOAJ |
| description | ABSTRACT The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a potential strategy for fault identification and classification through the utilization of machine learning (ML) techniques. The study aimed to use ML algorithms to identify and classify normal operations, seven different types of faults, in two operational modes (maximum power point tracking and intermediate power point tracking). Four machine learning algorithms and ensemble methods (decision trees, k‐nearest neighbors, random forest, and extreme gradient boosting) were employed, followed by hyperparameter tuning and cross‐validation to determine the best configuration. The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. The TPR values show a high sensitivity of 0.999, with some achieving a perfect score of 1.000. The FPR shows very low values, with the majority of metrics indicating FPRs at or close to 0%. This performance is crucial in the solar energy context, as failing to detect faults can result in significant energy loss and increased maintenance costs. |
| format | Article |
| id | doaj-art-ae2eebdcb50a430ba2a919e61480f8c4 |
| institution | Kabale University |
| issn | 2689-5595 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied AI Letters |
| spelling | doaj-art-ae2eebdcb50a430ba2a919e61480f8c42025-08-20T03:46:58ZengWileyApplied AI Letters2689-55952025-04-0162n/an/a10.1002/ail2.115Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning TechniquesGhalia Nassreddine0Amal El Arid1Mohamad Nassereddine2Obada Al Khatib3Rafik Hariri University Meshref LebanonRafik Hariri University Meshref LebanonUniversity of Wollongong in Dubai Dubai UAEUniversity of Wollongong in Dubai Dubai UAEABSTRACT The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a potential strategy for fault identification and classification through the utilization of machine learning (ML) techniques. The study aimed to use ML algorithms to identify and classify normal operations, seven different types of faults, in two operational modes (maximum power point tracking and intermediate power point tracking). Four machine learning algorithms and ensemble methods (decision trees, k‐nearest neighbors, random forest, and extreme gradient boosting) were employed, followed by hyperparameter tuning and cross‐validation to determine the best configuration. The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. The TPR values show a high sensitivity of 0.999, with some achieving a perfect score of 1.000. The FPR shows very low values, with the majority of metrics indicating FPRs at or close to 0%. This performance is crucial in the solar energy context, as failing to detect faults can result in significant energy loss and increased maintenance costs.https://doi.org/10.1002/ail2.115cross‐validationensemble learningfault classificationfault detectionmachine learningphotovoltaic panel system |
| spellingShingle | Ghalia Nassreddine Amal El Arid Mohamad Nassereddine Obada Al Khatib Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques Applied AI Letters cross‐validation ensemble learning fault classification fault detection machine learning photovoltaic panel system |
| title | Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques |
| title_full | Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques |
| title_fullStr | Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques |
| title_full_unstemmed | Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques |
| title_short | Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques |
| title_sort | fault detection and classification for photovoltaic panel system using machine learning techniques |
| topic | cross‐validation ensemble learning fault classification fault detection machine learning photovoltaic panel system |
| url | https://doi.org/10.1002/ail2.115 |
| work_keys_str_mv | AT ghalianassreddine faultdetectionandclassificationforphotovoltaicpanelsystemusingmachinelearningtechniques AT amalelarid faultdetectionandclassificationforphotovoltaicpanelsystemusingmachinelearningtechniques AT mohamadnassereddine faultdetectionandclassificationforphotovoltaicpanelsystemusingmachinelearningtechniques AT obadaalkhatib faultdetectionandclassificationforphotovoltaicpanelsystemusingmachinelearningtechniques |