A statistical-based approach for fault detection and diagnosis in a photovoltaic system

Handle URI:
http://hdl.handle.net/10754/625672
Title:
A statistical-based approach for fault detection and diagnosis in a photovoltaic system
Authors:
Garoudja, Elyes; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Kara, Kamel; Chouder, Aissa; Silvestre, Santiago
Abstract:
This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program
Citation:
Garoudja E, Harrou F, Sun Y, Kara K, Chouder A, et al. (2017) A statistical-based approach for fault detection and diagnosis in a photovoltaic system. 2017 6th International Conference on Systems and Control (ICSC). Available: http://dx.doi.org/10.1109/ICoSC.2017.7958710.
Publisher:
IEEE
Journal:
2017 6th International Conference on Systems and Control (ICSC)
KAUST Grant Number:
OSR-2015-CRG4-2582
Conference/Event name:
6th International Conference on Systems and Control, ICSC 2017
Issue Date:
10-Jul-2017
DOI:
10.1109/ICoSC.2017.7958710
Type:
Conference Paper
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. 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.
Additional Links:
http://ieeexplore.ieee.org/document/7958710/
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGaroudja, Elyesen
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorKara, Kamelen
dc.contributor.authorChouder, Aissaen
dc.contributor.authorSilvestre, Santiagoen
dc.date.accessioned2017-10-03T12:49:33Z-
dc.date.available2017-10-03T12:49:33Z-
dc.date.issued2017-07-10en
dc.identifier.citationGaroudja E, Harrou F, Sun Y, Kara K, Chouder A, et al. (2017) A statistical-based approach for fault detection and diagnosis in a photovoltaic system. 2017 6th International Conference on Systems and Control (ICSC). Available: http://dx.doi.org/10.1109/ICoSC.2017.7958710.en
dc.identifier.doi10.1109/ICoSC.2017.7958710en
dc.identifier.urihttp://hdl.handle.net/10754/625672-
dc.description.abstractThis paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short-circuit faults; open-circuit faults; and partial shading faults). Towards this end, we apply exponentially-weighted moving average (EWMA) control chart on the residuals obtained from the one-diode model. Such a choice is motivated by the greater sensitivity of EWMA chart to incipient faults and its low-computational cost making it easy to implement in real time. Practical data from a 3.2 KWp photovoltaic plant located within an Algerian research center is used to validate the proposed approach. Results show clearly the efficiency of the developed method in monitoring PV system status.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. 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.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7958710/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectCircuit faultsen
dc.subjectComputational modelingen
dc.subjectControl chartsen
dc.subjectFault detectionen
dc.subjectIntegrated circuit modelingen
dc.subjectMathematical modelen
dc.subjectMonitoringen
dc.titleA statistical-based approach for fault detection and diagnosis in a photovoltaic systemen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journal2017 6th International Conference on Systems and Control (ICSC)en
dc.conference.date2017-05-07 to 2017-05-09en
dc.conference.name6th International Conference on Systems and Control, ICSC 2017en
dc.conference.locationBatna, DZAen
dc.eprint.versionPost-printen
dc.contributor.institutionElectronic Department, Blida 1 University Blida, Algeriaen
dc.contributor.institutionElectrical Engineering Department University of m’sila Ichbilia Street, Algeriaen
dc.contributor.institutionElectronic Engineering Departmen Univesitat Politécnica de Catalunya Barcelona, Spainen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.