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Estimation d'indicateurs de diagnostic pour la surveillance de panneaux photovoltaïques à l'aide de réseaux de neurones artificiels

dc.rights.licenseopenen_US
hal.structure.identifierESTIA - Institute of technology [ESTIA]
dc.contributor.authorALRIFAI, Yehya
hal.structure.identifierESTIA - Institute of technology [ESTIA]
dc.contributor.authorAGUILERA GONZALEZ, Adriana
ORCID: 0000-0003-1166-0648
IDREF: 253127653
hal.structure.identifierESTIA - Institute of technology [ESTIA]
dc.contributor.authorVECHIU, Ionel
ORCID: 0000-0003-4108-3546
IDREF: 102417741
dc.date.accessioned2025-02-13T10:26:14Z
dc.date.available2025-02-13T10:26:14Z
dc.date.conference2024-05-15
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/204841
dc.description.abstractEnSolar energy is widely recognized as one of the primary renewable energy sources. However, the efficiency and reliability of Photovoltaic (PV) systems can be significantly impacted by faults. For these reasons, it is paramount to Continuously monitor the PV health state to ensure optimal performance. In this context, this paper introduces a robust estimation model using an Artificial Neural Network (ANN) model to accurately predict three diagnosis indicators: power (P), current (I), and voltage (V). These indicators play a vital role in monitoring the behavior of PV systems considering different weather conditions. The computational algorithm establishes the mapping from PV electrical coordinates and temperature to the diagnosis indicators, without relying on an irradiation sensor. The performance of the proposed estimation model is evaluated via MATLAB/Simulink®, based on the real meteorological profiles for a typical year in Anglet, France.
dc.language.isoENen_US
dc.publisherIEEEen_US
dc.subject.enANN
dc.subject.enPV panels
dc.subject.enDiagnosis indicators
dc.subject.enEstimation
dc.subject.enTemperature sensors
dc.subject.enPhotovoltaic systems
dc.subject.enRadiation effects
dc.subject.enComputational modeling
dc.subject.enArtificial neural networks
dc.subject.enPredictive models
dc.titleEstimation d'indicateurs de diagnostic pour la surveillance de panneaux photovoltaïques à l'aide de réseaux de neurones artificiels
dc.title.enEstimation of diagnosis indicators for monitoring photovoltaic panels using artificial neural networks
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.1109/iccad60883.2024.10553687en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.page1-6en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.conference.title2024 International Conference on Control, Automation and Diagnosis (ICCAD)en_US
bordeaux.countryfren_US
bordeaux.title.proceeding2024 International Conference on Control, Automation and Diagnosis (ICCAD)en_US
bordeaux.conference.cityParisen_US
bordeaux.import.sourcecrossref
hal.identifierhal-04945385
hal.version1
hal.date.transferred2025-02-13T10:26:16Z
hal.proceedingsouien_US
hal.conference.end2024-05-17
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcecrossref
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.title=Estimation%20d'indicateurs%20de%20diagnostic%20pour%20la%20surveillance%20de%20panneaux%20photovolta%C3%AFques%20%C3%A0%20l'aide%20de%20r%C3%A9seaux%20de%20neurones%20art&rft.atitle=Estimation%20d'indicateurs%20de%20diagnostic%20pour%20la%20surveillance%20de%20panneaux%20photovolta%C3%AFques%20%C3%A0%20l'aide%20de%20r%C3%A9seaux%20de%20neurones%20ar&rft.spage=1-6&rft.epage=1-6&rft.au=ALRIFAI,%20Yehya&AGUILERA%20GONZALEZ,%20Adriana&VECHIU,%20Ionel&rft.genre=unknown


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