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Optimal placement and sizing of dgs in distribution networks using mlpso algorithm

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dc.citedby22
dc.contributor.authorKarunarathne E.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorEkanayake J.en_US
dc.contributor.authorAlmeida D.en_US
dc.contributor.authorid57216633155en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid7003409510en_US
dc.contributor.authorid57211718103en_US
dc.date.accessioned2023-05-29T08:06:44Z
dc.date.available2023-05-29T08:06:44Z
dc.date.issued2020
dc.descriptionParticle size analysis; Particle swarm optimization (PSO); Active power loss minimizations; Comprehensive performance; Exponential increase; Optimal placement and sizings; Optimization algorithms; Optimization techniques; Pre-mature convergences; Unity power factor; Electric power transmission networksen_US
dc.description.abstractIn today�s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs. � 2020 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6185
dc.identifier.doi10.3390/en13236185
dc.identifier.issue23
dc.identifier.scopus2-s2.0-85105643419
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105643419&doi=10.3390%2fen13236185&partnerID=40&md5=e353bceb9f3d76e6b489d81e99a7306d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25089
dc.identifier.volume13
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleOptimal placement and sizing of dgs in distribution networks using mlpso algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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