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Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

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dc.citedby1
dc.contributor.authorAbas N.A.S.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.authorOthman M.M.en_US
dc.contributor.authorid57210749079en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid57193388570en_US
dc.contributor.authorid56372667100en_US
dc.contributor.authorid57210749614en_US
dc.contributor.authorid35944613200en_US
dc.date.accessioned2023-05-29T07:23:55Z
dc.date.available2023-05-29T07:23:55Z
dc.date.issued2019
dc.description.abstractThis paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system. � 2019 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/eei.v8i3.1631
dc.identifier.epage984
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85071394911
dc.identifier.spage978
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071394911&doi=10.11591%2feei.v8i3.1631&partnerID=40&md5=a6376ad443583b89c0189ba58ce183d7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24484
dc.identifier.volume8
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Bronze, Green
dc.sourceScopus
dc.sourcetitleBulletin of Electrical Engineering and Informatics
dc.titleIntegrated monte carlo-evolutionary programming technique for distributed generation studies in distribution systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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