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Supervised evolutionary programming based technique for multi-DG installation in distribution system

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dc.citedby6
dc.contributor.authorShaari M.F.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorNazer M.F.M.en_US
dc.contributor.authorJelani S.en_US
dc.contributor.authorJamaludin F.A.en_US
dc.contributor.authorMansor M.H.en_US
dc.contributor.authorKumar A.V.S.en_US
dc.contributor.authorid57215493564en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid57215489328en_US
dc.contributor.authorid57193388570en_US
dc.contributor.authorid57188962554en_US
dc.contributor.authorid56372667100en_US
dc.contributor.authorid56888921600en_US
dc.date.accessioned2023-05-29T08:13:55Z
dc.date.available2023-05-29T08:13:55Z
dc.date.issued2020
dc.description.abstractInstalling DG in network system, has supported the distribution system to provide the increasing number of consumer demand and load, in order to achieve that this paper presents an efficient and fast converging optimization technique based on a modification of traditional evolutionary programming method for obtain the finest optimal location and power loss in distribution systems. The proposed algorithm that is supervised evolutionary programming is implemented in MATLAB and apply on the 69-bus feeder system in order to minimize the system power loss and obtaining the best optimal location of the distributed generators. � 2020, Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijai.v9.i1.pp11-17
dc.identifier.epage17
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85081034123
dc.identifier.spage11
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081034123&doi=10.11591%2fijai.v9.i1.pp11-17&partnerID=40&md5=e0d6842eb8ae63b7e2df4d991bce4f5f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25759
dc.identifier.volume9
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIAES International Journal of Artificial Intelligence
dc.titleSupervised evolutionary programming based technique for multi-DG installation in distribution systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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