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Multi-DGPV Planning Using Artificial Intelligence

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dc.citedby1
dc.contributor.authorAbdullah A.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorOthman M.M.en_US
dc.contributor.authorRahim S.R.A.en_US
dc.contributor.authorSentilkumar A.V.en_US
dc.contributor.authorid57197864035en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid35944613200en_US
dc.contributor.authorid11639107900en_US
dc.contributor.authorid56888921600en_US
dc.date.accessioned2024-10-14T03:18:51Z
dc.date.available2024-10-14T03:18:51Z
dc.date.issued2023
dc.description.abstractThis article investigates the impact of multi-Distributed Generation Photovoltaic (DGPV) installation and their degree of penetration on controlling power loss in the radial distribution system. The Integrated Immune Moth Flame Evolution Programming (IIMFEP), a unique hybrid optimization technique, was utilized to identify the ideal DGPV size and location for base case conditions and under load variations. The IIMFEP approach is compared against Evolutionary Programming (EP), Artificial Immune System (AIS), and Moth Flame Optimization (MFO) and validated using the IEEE 118-Bus Radial Distribution Systems (RDS). Incorporating multi-DGPV into a system reduces the total real and reactive power loss while simultaneously increasing the minimum voltage and decreasing the total voltage deviation. In every instance examined in this study, the IIMFEP method yields optimal solutions superior to those generated by the other three methods. As the number of DGPV units increased to nine, the percentage of power loss reduction became the highest among all DG units examined, and DG penetration reached 94.26 percent. This research provides the power system operator with comprehensive findings demonstrating the impact of installing multi-DGPV in distribution networks on system loss. � 2023, Ismail Saritas. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage391
dc.identifier.issue4s
dc.identifier.scopus2-s2.0-85161668041
dc.identifier.spage377
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161668041&partnerID=40&md5=b9dd6777ee6339f6e73e67cb03e0b0d0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34289
dc.identifier.volume11
dc.pagecount14
dc.publisherIsmail Saritasen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Intelligent Systems and Applications in Engineering
dc.subjectBackward forward sweep
dc.subjectDistributed generation
dc.subjectDistributed generation photovoltaic
dc.subjectEvolutionary programming
dc.subjectLoss minimization
dc.subjectOptimization
dc.subjectTotal voltage deviation
dc.titleMulti-DGPV Planning Using Artificial Intelligenceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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