dc.contributor.author Harrou, Fouzi dc.contributor.author Taghezouit, Bilal dc.contributor.author Sun, Ying dc.date.accessioned 2019-05-21T13:21:36Z dc.date.available 2019-05-21T13:21:36Z dc.date.issued 2019-03-18 dc.identifier.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. dc.identifier.issn 2156-3381 dc.identifier.issn 2156-3403 dc.identifier.doi 10.1109/JPHOTOV.2019.2896652 dc.identifier.uri http://hdl.handle.net/10754/653087 dc.description.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. dc.description.sponsorship 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. dc.publisher Institute of Electrical and Electronics Engineers (IEEE) dc.relation.url https://ieeexplore.ieee.org/document/8657711 dc.subject (kNN), partial shading dc.subject Fault detection dc.subject photovoltaic (PV) systems dc.subject statistical monitoring charts dc.title Improved $k$ NN-Based Monitoring Schemes for Detecting Faults in PV Systems dc.type Article dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Statistics Program dc.identifier.journal IEEE Journal of Photovoltaics dc.contributor.institution Laboratoire de Dispositif de Communication et de Conversion Photovoltaique, Ecole Nationale Polytechnique Alger, Algeirs, 16200, , Algeria dc.contributor.institution Centre de Développement des Energies Renouvelables (CDER), Algiers, 16340, , Algeria kaust.person Harrou, Fouzi kaust.person Sun, Ying kaust.grant.number OSR-2015-CRG4-2582 dc.date.published-online 2019-03-18 dc.date.published-print 2019-05
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