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dc.contributor.authorFaris Amiri, Ahmed
dc.contributor.authorKichou, Sofiane
dc.contributor.authorOudira, Houcine
dc.contributor.authorChouder, Aissa
dc.contributor.authorSilvestre Bergés, Santiago
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2024-01-30T07:25:58Z
dc.date.available2024-01-30T07:25:58Z
dc.date.issued2024-01-24
dc.identifier.citationFaris, A. [et al.]. Fault detection and diagnosis of a photovoltaic system based on deep learning using the combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). "Sustainability (Switzerland)", 24 Gener 2024, vol. 16, núm. 3, article 1012.
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/2117/400474
dc.description.abstractThe meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Microelectrònica
dc.subjectÀrees temàtiques de la UPC::Energies::Energia solar fotovoltaica::Captadors solars
dc.subject.lcshMicroelectronics
dc.subject.lcshPhotovoltaic power generation
dc.subject.otherPhotovoltaic (PV) system
dc.subject.otherFault detection
dc.subject.otherFault classification
dc.subject.otherDeep learning
dc.subject.otherConvolutional Neural Network (CNN)
dc.subject.otherBidirectional Gated Recurrent Unit (Bi-GRU)
dc.subject.otherPV modeling
dc.titleFault detection and diagnosis of a photovoltaic system based on deep learning using the combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)
dc.typeArticle
dc.subject.lemacMicroelectrònica
dc.subject.lemacEnergia solar fotovoltaica
dc.contributor.groupUniversitat Politècnica de Catalunya. MNT-Solar - Grup de Micro i Nano Tecnologies per Energia Solar
dc.identifier.doi10.3390/su16031012
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2071-1050/16/3/1012
dc.rights.accessOpen Access
local.identifier.drac37906709
dc.description.versionPostprint (published version)
local.citation.authorFaris, A.; Kichou, S.; Oudira, H.; Chouder, A.; Silvestre, S.
local.citation.publicationNameSustainability (Switzerland)
local.citation.volume16
local.citation.number3, article 1012


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