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    Improved $k$ NN-Based Monitoring Schemes for Detecting Faults in PV Systems

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    Type
    Article
    Authors
    Harrou, Fouzi cc
    Taghezouit, Bilal
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2019-03-18
    Online Publication Date
    2019-03-18
    Print Publication Date
    2019-05
    Permanent link to this record
    http://hdl.handle.net/10754/653087
    
    Metadata
    Show full item record
    Abstract
    This paper presents a model-based anomaly detection method for supervising the direct current (dc) side of photovotaic (PV) systems. Toward this end, a framework combining the benefits of k-nearest neighbors (kNN) with univariate monitoring approaches has been proposed. Specifically, kNN-based Shewhart and exponentially weighted moving average (EWMA) schemes with parametric and nonparametric thresholds have been introduced to suitably detect faults in PV systems. The choice of kNN method to separate normal and abnormal features is motivated by its capacity to handle nonlinear features and do not make assumptions on the underlying data distribution. In addition, because the EWMA approach is sensitive in detecting small changes. First, a simulation model for the inspected PV array is constructed. Then, residuals generated from this model are employed as the input for kNN-based schemes for anomaly detection. Parametric and nonparametric thresholds using kernel density estimation have been used to detect faults. The effectiveness of the kNN-based procedures is verified using actual measurements from a 9.54-kWp grid-connected system in Algeria. Results proclaim the efficiency of the proposed strategy to supervise the dc side of PV systems.
    Citation
    Harrou F, Taghezouit B, Sun Y (2019) Improved $k$NN-Based Monitoring Schemes for Detecting Faults in PV Systems. IEEE Journal of Photovoltaics 9: 811–821. Available: http://dx.doi.org/10.1109/JPHOTOV.2019.2896652.
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award OSR-2015-CRG4-2582.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Journal of Photovoltaics
    DOI
    10.1109/JPHOTOV.2019.2896652
    Additional Links
    https://ieeexplore.ieee.org/document/8657711
    ae974a485f413a2113503eed53cd6c53
    10.1109/JPHOTOV.2019.2896652
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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