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Author: search.filters.author.Bouzrara, KaisSubject: search.filters.subject.Fault detection and diagnosis (FDD)

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    Wind power converter fault diagnosis using reduced kernel PCA-Based BiLSTM
    (MDPI, 2023) Attouri, Khadija; Mansouri, Majdi; Hajji, Mansour; Kouadri, Abdelmalek; Bouzrara, Kais; Nounou, Hazem
    In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation time and possibly increase the accuracy of fault diagnosis. This strategy involves three significant steps: first, a size reduction procedure is applied to the training dataset, which uses hierarchical K-means clustering and Euclidean distance schemes; second, both significantly reduced training datasets are utilized by the KPCA technique to extract and select the most sensitive and significant features; and finally, in order to distinguish between the diverse WEC system operating modes, the selected features are used to train a bidirectional long-short-term memory classifier (BiLSTM). In this study, various fault scenarios (short-circuit (SC) faults and open-circuit (OC) faults) were injected, and each scenario comprised different cases (simple, multiple, and mixed faults) on different sides and locations (generator-side converter and grid-side converter) to ensure a comprehensive and global evaluation. The obtained results show that the proposed strategy for FDD via both applied dataset size reduction methods not only improves the accuracy but also provides an efficient reduction in computation time and storage space
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    Faults classification in Grid-Connected photovoltaic systems
    (IEEE, 2021) Attouri, Khadija; Hajji, Mansour; Mansouri, Majdi; Nounou, Hazem; Kouadri, Abdelmalek; Bouzrara, Kais
    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