Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation

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dc.contributor.authorYalçin, Tolga
dc.contributor.authorParadell Solà, Pol
dc.contributor.authorStefanidou-Voziki, Paschalia
dc.contributor.authorDomínguez-García, Jose Luis
dc.contributor.authorDemirdelen, Tugce
dc.date.accessioned2025-01-06T17:30:25Z
dc.date.available2025-01-06T17:30:25Z
dc.date.issued2023
dc.description.abstractThe rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions. © 2023 by the authors.
dc.identifier.doi10.3390/en16135044
dc.identifier.issn1996-1073
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85164835439
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/en16135044
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1596
dc.identifier.volume16
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofEnergies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectDigital Twin
dc.subjectmachine learning
dc.subjectO&M systems
dc.subjectPV system
dc.subjectsolar plant
dc.titleExploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation
dc.typeArticle

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