Management of hybrid electric microgrid using fuzzy logic and adaptive neural network

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dc.contributor.authorAljanabi, Ihsan Basil
dc.contributor.authorCansever, Galip
dc.date.accessioned2022-08-05T13:57:15Z
dc.date.available2022-08-05T13:57:15Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe Energy most countries installed a distributed generation micro-grid which incorporates renewable resources (solar and wind energy) With respect to this system, in this work two topics are addressed; the calculation of the range in which the demand can move and the effect that the demand management signals have on the forecast. For the first topic, fuzzy intervals will be used to determine, based on historical data, the dynamic range. This range provides the limits to the optimizer for the load displacement factor, on which the signals that are sent to the consumers depend.en_US
dc.identifier.citationAljanabi, I. B., Cansever, G. (2022). Management of hybrid electric microgrid using fuzzy logic and adaptive neural network. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE.en_US
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133978817
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2779
dc.indekslendigikaynakScopus
dc.institutionauthorAljanabi, Ihsan Basil
dc.institutionauthorCansever, Galip
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.isversionof10.1109/HORA55278.2022.9799920en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEMSen_US
dc.subjectFuzzy Logicen_US
dc.subjectMGen_US
dc.subjectSGen_US
dc.titleManagement of hybrid electric microgrid using fuzzy logic and adaptive neural network
dc.typeConference Object

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