Model-based fault detection algorithm for photovoltaic system monitoring

Handle URI:
http://hdl.handle.net/10754/627857
Title:
Model-based fault detection algorithm for photovoltaic system monitoring
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Saidi, Ahmed
Abstract:
Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Statistics Program
Citation:
Harrou F, Sun Y, Saidi A (2017) Model-based fault detection algorithm for photovoltaic system monitoring. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2017.8285435.
Publisher:
IEEE
Journal:
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
KAUST Grant Number:
OSR-2015-CRG4-2582
Conference/Event name:
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Issue Date:
12-Feb-2018
DOI:
10.1109/SSCI.2017.8285435
Type:
Conference Paper
Sponsors:
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
Additional Links:
https://ieeexplore.ieee.org/document/8285435/
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Statistics Program

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorSaidi, Ahmeden
dc.date.accessioned2018-05-14T13:37:06Z-
dc.date.available2018-05-14T13:37:06Z-
dc.date.issued2018-02-12en
dc.identifier.citationHarrou F, Sun Y, Saidi A (2017) Model-based fault detection algorithm for photovoltaic system monitoring. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2017.8285435.en
dc.identifier.doi10.1109/SSCI.2017.8285435en
dc.identifier.urihttp://hdl.handle.net/10754/627857-
dc.description.abstractReliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.en
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.en
dc.publisherIEEEen
dc.relation.urlhttps://ieeexplore.ieee.org/document/8285435/en
dc.titleModel-based fault detection algorithm for photovoltaic system monitoringen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentStatistics Programen
dc.identifier.journal2017 IEEE Symposium Series on Computational Intelligence (SSCI)en
dc.conference.date2017-11-27 to 2017-12-01en
dc.conference.name2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017en
dc.conference.locationHonolulu, HI, USAen
dc.contributor.institutionENERGARID Laboratory, Electrical Engineering Department, Tahri Mohammed University, Béchar, , Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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