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    Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes

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    JPV-2020-12-0571-R-R2.pdf
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Harrou, Fouzi cc
    Saidi, Ahmed
    Sun, Ying cc
    Khadraoui, Sofiane
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2019-CRG7-3800
    Date
    2021
    Submitted Date
    2020-12-15
    Permanent link to this record
    http://hdl.handle.net/10754/667699
    
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    Abstract
    Data-based procedures for monitoring the operating performance of a PV system are proposed in this article. The only information required to apply the procedures is the availability of system measurements, which are routinely on-line collected via sensors. Here, kernel-based machine learning methods, including support vector regression (SVR) and Gaussian process regression (GPR), are used to model multivariate data from the PV system for fault detection because of their flexibility and capability to nonlinear approximation. Essentially, the SVR and GPR models are adopted to obtain residuals to detect and identify occurred faults. Then, residuals are passed through an exponential smoothing filter to reduce noise and improve data quality. In this work, a monitoring scheme based on kernel density estimation is used to sense faults by examining the generated residuals. Several different scenarios of faults were considered in this study, including PV string fault, partial shading, PV modules short-circuited, module degradation, and line–line faults on the PV array. Using data from a 20 MWp grid-connected PV system, the considered faults were successfully traced using the developed procedures. Also, it has been demonstrated that GPR-based monitoring procedures achieve better detection performance over SVRs to monitor PV systems.
    Citation
    Harrou, F., Saidi, A., Sun, Y., & Khadraoui, S. (2021). Monitoring of Photovoltaic Systems Using Improved Kernel-Based Learning Schemes. IEEE Journal of Photovoltaics, 1–13. doi:10.1109/jphotov.2021.3057169
    Sponsors
    This work was supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award OSR-2019-CRG7-3800.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Journal of Photovoltaics
    DOI
    10.1109/JPHOTOV.2021.3057169
    Additional Links
    https://ieeexplore.ieee.org/document/9363277/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9363277
    ae974a485f413a2113503eed53cd6c53
    10.1109/JPHOTOV.2021.3057169
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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