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    Statistical fault detection in photovoltaic systems

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    Type
    Article
    Authors
    Garoudja, Elyes
    Harrou, Fouzi cc
    Sun, Ying cc
    Kara, Kamel
    Chouder, Aissa
    Silvestre, Santiago
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2017-05-08
    Online Publication Date
    2017-05-08
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/625023
    
    Metadata
    Show full item record
    Abstract
    Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array's maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.
    Citation
    Garoudja E, Harrou F, Sun Y, Kara K, Chouder A, et al. (2017) Statistical fault detection in photovoltaic systems. Solar Energy 150: 485–499. Available: http://dx.doi.org/10.1016/j.solener.2017.04.043.
    Sponsors
    We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality. This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Elyes Garoudja and Kamel Kara) thank the SET Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, Algeria, for continuous support during the study.
    Publisher
    Elsevier BV
    Journal
    Solar Energy
    DOI
    10.1016/j.solener.2017.04.043
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0038092X17303377
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.solener.2017.04.043
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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