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Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation

dc.contributor.authorHussien, Ahmed M.
dc.contributor.authorKim, Jonghoom
dc.contributor.authorAlkuhayli, Abdulaziz
dc.contributor.authorAlharbi, Mohammed
dc.contributor.authorHasanien, Hany M.
dc.contributor.authorTostado-Véliz, Marcos
dc.contributor.authorTurky, Rania A.
dc.contributor.authorJurado-Melguizo, Francisco
dc.date.accessioned2024-12-04T13:11:21Z
dc.date.available2024-12-04T13:11:21Z
dc.date.issued2022-11-11
dc.description.abstractThe present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.es_ES
dc.identifier.citationHussien, A.M.; Kim, J.; Alkuhayli, A.; Alharbi, M.; Hasanien, H.M.; Tostado-Véliz, M.; Turky, R.A.; Jurado, F. Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation. Sustainability 2022, 14, 14928. https://doi.org/10.3390/su142214928es_ES
dc.identifier.issn2071-1050es_ES
dc.identifier.other10.3390/su142214928es_ES
dc.identifier.urihttps://www.mdpi.com/2071-1050/14/22/14928es_ES
dc.identifier.urihttps://hdl.handle.net/10953/3456
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.ispartofSustainability [2022]; [14]: [14928]es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectAdaptive controles_ES
dc.subjectEnhanced block-sparse adaptive Bayesian algorithmes_ES
dc.subjectMicrogrides_ES
dc.subjectResponse surface methodologyes_ES
dc.titleAdaptive PI Control Strategy for Optimal Microgrid Autonomous Operationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES

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