Publication:
TCSC Optimization for Loss Minimization in Power System Using Computational Intelligence Techniques

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dc.citedby0
dc.contributor.authorBalasubramaniam N.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorKamari N.A.M.en_US
dc.contributor.authorIbrahim A.A.en_US
dc.contributor.authorid56695412900en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid36680312000en_US
dc.contributor.authorid7202978991en_US
dc.date.accessioned2025-03-03T07:45:37Z
dc.date.available2025-03-03T07:45:37Z
dc.date.issued2024
dc.description.abstractMinimizing power loss in transmission systems is crucial for achieving energy efficiency, lowering temperature rise and less monetary losses leading to sustainable power system network. Flexible AC Transmission Systems (FACTs) has been vastly adopted in minimizing the power loss in power transmission systems. However, the effectiveness of FACTs devices in achieving these benefits relies heavily on their optimal placement and sizing within the transmission system. Suboptimal solutions on FACTs devices location and sizing results to under-compensation or over-compensation, both of which are undesirable outcomes. Therefore, robust optimization techniques are necessary to attain optimal solutions. This study applies evolutionary programming (EP) and artificial immune system (AIS) as the computational intelligence techniques to examine the effects of thyristor controlled static compensators (TCSC) for loss minimization in power system. This study shows that the installation of TCSC substantially minimizes the power system loss. The IEEE 30-Bus Reliability Test System (RTS) is used to validate the proposed application and compensation scheme. The application of evolutionary programming and artificial immune system techniques provides valuable insights and solutions to power loss reduction ultimately improving the performance of transmission power systems. It was discovered that both techniques are comparable in minimizing the transmission loss in the system. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-97-3851-9_27
dc.identifier.epage315
dc.identifier.scopus2-s2.0-85205104383
dc.identifier.spage301
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85205104383&doi=10.1007%2f978-981-97-3851-9_27&partnerID=40&md5=646304c1f284553cbd03f951028ac348
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36902
dc.identifier.volume1213 LNEE
dc.pagecount14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Electrical Engineering
dc.subjectDC power transmission
dc.subjectFlexible AC transmission systems
dc.subjectArtificial Immune System
dc.subjectEvolutionary programming
dc.subjectFlexible AC transmission
dc.subjectFlexible AC transmission system
dc.subjectLosses minimizations
dc.subjectPower
dc.subjectStatic compensator
dc.subjectThyristor controled static compensator
dc.subjectTransmission systems
dc.subjectThyristors
dc.titleTCSC Optimization for Loss Minimization in Power System Using Computational Intelligence Techniquesen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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