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| Titre: | Faults classification in Grid-Connected photovoltaic systems |
| Auteur(s): | Attouri, Khadija
Hajji, Mansour
Mansouri, Majdi
Nounou, Hazem
Kouadri, Abdelmalek
Bouzrara, Kais |
| Mots-clés: | Fault detection and diagnosis (FDD)
Grid-Connected photovoltaic systems (GCPV)
Kullback-Leibler Divergence (KLD)
Principal Component Analysis (PCA) |
| Date de publication: | 2021 |
| Editeur: | IEEE |
| Collection/Numéro: | 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)/ (2021);pp. 1431-1437 |
| Résumé: | Fault detection and diagnosis (FDD) for Grid-Connected Photovoltaic (GCPV) systems have been received an important measure for improving the operation of these systems. Therefore, in this paper, an enhanced FDD approach, so-called principal component analysis (PCA)-based on a Kullback-Leibler Divergence (KLD), aims to provide the reliability and safety of the overall GCPV system is proposed. The developed approach merges the benefits of PCA model and KLD metric. Firstly, the GCPV features are extracted using PCA model. Secondly, the extracted features are fed to KLD metric for classification purposes. The obtained results confirm the high accuracy of the developed technique. The proposed approach showed superior effectiveness and robustness in process fault diagnosis |
| URI/URL: | https://ieeexplore.ieee.org/document/9429312
DOI: 10.1109/SSD52085.2021.9429312
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7050 |
| ISBN: | 978-166541493-7 |
| Collection(s) : | Communications Internationales
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