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Embedded adaptive mutation evolutionary programming for distributed generation management

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dc.citedby1
dc.contributor.authorZulkefli M.F.M.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorJelani S.en_US
dc.contributor.authorMansor M.H.en_US
dc.contributor.authorHonnoon N.M.S.en_US
dc.contributor.authorid57212927977en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid57193388570en_US
dc.contributor.authorid56372667100en_US
dc.contributor.authorid57210749614en_US
dc.date.accessioned2023-05-29T07:23:07Z
dc.date.available2023-05-29T07:23:07Z
dc.date.issued2019
dc.description.abstractDistribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced. � 2019 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v16.i1.pp364-370
dc.identifier.epage370
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85077489022
dc.identifier.spage364
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85077489022&doi=10.11591%2fijeecs.v16.i1.pp364-370&partnerID=40&md5=d94d9d496984a241d7b93569ffc22308
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24380
dc.identifier.volume16
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Hybrid Gold
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleEmbedded adaptive mutation evolutionary programming for distributed generation managementen_US
dc.typeArticleen_US
dspace.entity.typePublication
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