Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

Handle URI:
http://hdl.handle.net/10754/625503
Title:
Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Taghezouit, Bilal; Saidi, Ahmed; Hamlati, Mohamed-Elkarim ( 0000-0003-2687-9538 )
Abstract:
This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array's residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Harrou F, Sun Y, Taghezouit B, Saidi A, Hamlati M-E (2017) Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renewable Energy. Available: http://dx.doi.org/10.1016/j.renene.2017.09.048.
Publisher:
Elsevier BV
Journal:
Renewable Energy
Issue Date:
18-Sep-2017
DOI:
10.1016/j.renene.2017.09.048
Type:
Article
ISSN:
0960-1481
Sponsors:
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. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0960148117309114
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorTaghezouit, Bilalen
dc.contributor.authorSaidi, Ahmeden
dc.contributor.authorHamlati, Mohamed-Elkarimen
dc.date.accessioned2017-09-21T09:25:34Z-
dc.date.available2017-09-21T09:25:34Z-
dc.date.issued2017-09-18en
dc.identifier.citationHarrou F, Sun Y, Taghezouit B, Saidi A, Hamlati M-E (2017) Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renewable Energy. Available: http://dx.doi.org/10.1016/j.renene.2017.09.048.en
dc.identifier.issn0960-1481en
dc.identifier.doi10.1016/j.renene.2017.09.048en
dc.identifier.urihttp://hdl.handle.net/10754/625503-
dc.description.abstractThis study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array's residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.en
dc.description.sponsorshipThis 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. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0960148117309114en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, [, , (2017-09-18)] DOI: 10.1016/j.renene.2017.09.048 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectFault detectionen
dc.subjectPartial shadingen
dc.subjectPhotovoltaic systemsen
dc.subjectOne-diode modelen
dc.subjectStatistical monitoring chartsen
dc.titleReliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approachesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalRenewable Energyen
dc.eprint.versionPost-printen
dc.contributor.institutionCentre de Développement des Energies Renouvelables, CDER, B.P. 62, Route de l’Observatoire, Bouzaréah, Algiers, 16340, Algeriaen
dc.contributor.institutionSmart Grids and Renewable Energie (SGRE) Laboratory, Electrical Engineering Department, Tahri Mohammed University, BP 417 Route de Kenadsa, Béchar, Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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