Controlling and tracking the maximum active power point in a photovoltaic system connected to the grid using the fuzzy neural controller

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dc.contributor.authorYusupov, Z.
dc.contributor.authorYaghoubi, E.
dc.contributor.authorYaghoubi, E.
dc.date.accessioned2024-09-29T16:20:58Z
dc.date.available2024-09-29T16:20:58Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description14th International Conference on Electrical and Electronics Engineering, ELECO 2023 -- 30 November 2023 through 2 December 2023 -- Virtual, Bursa -- 197135en_US
dc.description.abstractIn contemporary smart distribution microgrids, both AC and DC loads and sources are consistently accessible, often operating at varying voltage levels simultaneously. Consequently, the typical scenario in today's microgrids involves a hybrid microgrid setup, necessitating the integration of inverters to facilitate power sharing between the AC and DC sections. Hence, this paper introduces a solution for active power control within an integrated AC/DC microgrid incorporating decentralized photovoltaic sources. The proposed solution employs a fuzzy neural controller to manage power generation, including the complexities of tracking the maximum power point during partial shading conditions. This approach effectively addresses the challenges posed by the combined microgrid configuration. The simulation results provide clear evidence of the success of the proposed method in controlling the active power managed by the DC microgrid and transferring it to the AC section. © 2023 IEEE.en_US
dc.identifier.doi10.1109/ELECO60389.2023.10416016
dc.identifier.isbn979-835036049-3
dc.identifier.scopus2-s2.0-85185833605en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ELECO60389.2023.10416016
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9457
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectControllersen_US
dc.subjectElectric loadsen_US
dc.subjectElectric power system controlen_US
dc.subjectFuzzy inferenceen_US
dc.subjectFuzzy neural networksen_US
dc.subjectMicrogridsen_US
dc.subjectPower controlen_US
dc.subjectSmart power gridsen_US
dc.subjectAC and DC loadsen_US
dc.subjectActive poweren_US
dc.subjectActive power controlen_US
dc.subjectDC sourcesen_US
dc.subjectFuzzy-neural controllersen_US
dc.subjectMicrogriden_US
dc.subjectPhotovoltaic systemsen_US
dc.subjectPower sharingen_US
dc.subjectPowerpointen_US
dc.subjectVoltage levelsen_US
dc.subjectElectric power distributionen_US
dc.titleControlling and tracking the maximum active power point in a photovoltaic system connected to the grid using the fuzzy neural controlleren_US
dc.typeConference Objecten_US

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