Statistical fault detection in photovoltaic systems

Handle URI:
http://hdl.handle.net/10754/625023
Title:
Statistical fault detection in photovoltaic systems
Authors:
Garoudja, Elyes; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Kara, Kamel; Chouder, Aissa; Silvestre, Santiago
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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.
Publisher:
Elsevier BV
Journal:
Solar Energy
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
8-May-2017
DOI:
10.1016/j.solener.2017.04.043
Type:
Article
ISSN:
0038-092X
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.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0038092X17303377
Appears in Collections:
Articles; 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.accessioned2017-06-14T12:17:34Z-
dc.date.available2017-06-14T12:17:34Z-
dc.date.issued2017-05-08en
dc.identifier.citationGaroudja 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.en
dc.identifier.issn0038-092Xen
dc.identifier.doi10.1016/j.solener.2017.04.043en
dc.identifier.urihttp://hdl.handle.net/10754/625023-
dc.description.abstractFaults 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.en
dc.description.sponsorshipWe 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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0038092X17303377en
dc.subjectFault detectionen
dc.subjectOne-diode modelen
dc.subjectPhotovoltaic systemsen
dc.subjectStatistical monitoring chartsen
dc.subjectTemporary shadingen
dc.titleStatistical fault detection in photovoltaic systemsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSolar Energyen
dc.contributor.institutionSET Laboratory, Electronics Department, Blida 1 University, Blida, BP 270, , Algeriaen
dc.contributor.institutionElectrical Engineering Laboratory (LGE), University Mohamed Boudiaf of M'sila, BP 166, 28000, , Algeriaen
dc.contributor.institutionElectronic Engineering Department, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, Campus Nord UPC, Barcelona, 08034, , 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.