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A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

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dc.rights.license CC BY eng
dc.contributor.author Alhasnawi, Bilal Naji cze
dc.contributor.author Jasim, Basil H cze
dc.contributor.author Alhasnawi, Arshad Naji cze
dc.contributor.author Sedhom, Bishoy E cze
dc.contributor.author Jasim, Ali M cze
dc.contributor.author Khalili, Azam cze
dc.contributor.author Bureš, Vladimír cze
dc.contributor.author Burgio, Alessandro cze
dc.contributor.author Siano, Pierluigi cze
dc.date.accessioned 2025-12-05T11:56:44Z
dc.date.available 2025-12-05T11:56:44Z
dc.date.issued 2022 eng
dc.identifier.issn 1996-1073 eng
dc.identifier.uri http://hdl.handle.net/20.500.12603/1733
dc.description.abstract In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm's capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness. eng
dc.format p. "Article Number: 8480" eng
dc.language.iso eng eng
dc.publisher MDPI eng
dc.relation.ispartof ENERGIES, volume 15, issue: 22 eng
dc.subject MPPT eng
dc.subject SCADA eng
dc.subject solar system eng
dc.subject Internet of Things eng
dc.title A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA eng
dc.type article eng
dc.identifier.obd 43879843 eng
dc.identifier.wos 000887191900001 eng
dc.identifier.doi 10.3390/en15228480 eng
dc.publicationstatus postprint eng
dc.peerreviewed yes eng
dc.source.url https://www.mdpi.com/1996-1073/15/22/8480 cze
dc.relation.publisherversion https://www.mdpi.com/1996-1073/15/22/8480 eng
dc.rights.access Open Access eng


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