Online model-based fault detection for grid connected PV systems monitoring

Handle URI:
http://hdl.handle.net/10754/626840
Title:
Online model-based fault detection for grid connected PV systems monitoring
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Saidi, Ahmed
Abstract:
This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical 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; Applied Mathematics and Computational Science Program
Citation:
Harrou F, Sun Y, Saidi A (2017) Online model-based fault detection for grid connected PV systems monitoring. 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B). Available: http://dx.doi.org/10.1109/ICEE-B.2017.8192117.
Publisher:
IEEE
Journal:
2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
14-Dec-2017
DOI:
10.1109/ICEE-B.2017.8192117
Type:
Conference Paper
Sponsors:
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
Additional Links:
http://ieeexplore.ieee.org/document/8192117/
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.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorSaidi, Ahmeden
dc.date.accessioned2018-01-21T07:23:26Z-
dc.date.available2018-01-21T07:23:26Z-
dc.date.issued2017-12-14en
dc.identifier.citationHarrou F, Sun Y, Saidi A (2017) Online model-based fault detection for grid connected PV systems monitoring. 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B). Available: http://dx.doi.org/10.1109/ICEE-B.2017.8192117.en
dc.identifier.doi10.1109/ICEE-B.2017.8192117en
dc.identifier.urihttp://hdl.handle.net/10754/626840-
dc.description.abstractThis paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.en
dc.description.sponsorshipThe research reported in this publication was supported by funding from 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.urlhttp://ieeexplore.ieee.org/document/8192117/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.subjectMathematical modelen
dc.subjectMonitoringen
dc.subjectPower measurementen
dc.titleOnline model-based fault detection for grid connected PV systems monitoringen
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 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)en
dc.eprint.versionPost-printen
dc.contributor.institutionElectrical Engineering Department, Smart grid & renewable energy (SG&RE) Laboratory, TAHRI Mohammed University, Béchar, Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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