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ELM-ANFIS based controller for plug-in electric vehicle to grid integration [articol]

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dc.contributor.author Kandasamy, Kalaiselvi
dc.contributor.author Perumal, Renuga
dc.contributor.author Velu, Suresh Kumar
dc.date.accessioned 2025-02-17T11:34:47Z
dc.date.available 2025-02-17T11:34:47Z
dc.date.issued 2018
dc.identifier.citation Kandasamy, 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.issn 1582-4594
dc.identifier.uri https://dspace.upt.ro/xmlui/handle/123456789/7180
dc.description.abstract In 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.iso en en_US
dc.publisher Timișoara : Editura Politehnica en_US
dc.relation.ispartofseries Journal of Electrical Engineering;Vol 18 No 4
dc.subject Grid integration en_US
dc.subject Electric vehicle en_US
dc.subject Distribution system en_US
dc.subject Extreme learning machine en_US
dc.subject ANFIS en_US
dc.title ELM-ANFIS based controller for plug-in electric vehicle to grid integration [articol] en_US
dc.type Article en_US


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