Please use this identifier to cite or link to this item: https://dspace.upt.ro/xmlui/handle/123456789/7180
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dc.contributor.authorKandasamy, Kalaiselvi-
dc.contributor.authorPerumal, Renuga-
dc.contributor.authorVelu, Suresh Kumar-
dc.date.accessioned2025-02-17T11:34:47Z-
dc.date.available2025-02-17T11:34:47Z-
dc.date.issued2018-
dc.identifier.citationKandasamy, Kalaiselvi; Perumal,Renuga; Velu,Suresh Kumar. ELM-ANFIS based controller for plug-in electric vehicle to grid integration. Timişoara: Editura Politehnica, 2018.en_US
dc.identifier.issn1582-4594-
dc.identifier.urihttps://dspace.upt.ro/xmlui/handle/123456789/7180-
dc.description.abstractIn this paper, the authors propose adaptive neuro fuzzy inference system (ANFIS) algorithm, based on extreme learning machine (ELM) concepts for designing a controller for electric vehicle to grid (V2G) integration. First, learning speed and accuracy of the proposed algorithm is checked and second the transient response of the ELM-ANFIS (e-ANFIS) based controller is analyzed. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Thus, even with an involvement of a large number of plug-in hybrid electric vehicles (PHEV), a control technique for their charge and discharge pattern can be easily designed. To study the computational performance and transient response of the e-ANFIS based controller, it is compared with conventional ANFIS based controller. To implement the vehicle to grid integration concept, IEEE 33 bus radial distribution system is modelled in MATLAB environment.en_US
dc.language.isoenen_US
dc.publisherTimișoara : Editura Politehnicaen_US
dc.relation.ispartofseriesJournal of Electrical Engineering;Vol 18 No 4-
dc.subjectGrid integrationen_US
dc.subjectElectric vehicleen_US
dc.subjectDistribution systemen_US
dc.subjectExtreme learning machineen_US
dc.subjectANFISen_US
dc.titleELM-ANFIS based controller for plug-in electric vehicle to grid integration [articol]en_US
dc.typeArticleen_US
Appears in Collections:Articole științifice/Scientific articles

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