Multi-objective grounding system optimisation using NSGA-II

dc.contributor.authorLucero Tenorio, Miriameng
dc.contributor.authorValcárcel Rojas, Angel C.eng
dc.date.accessioned2024-12-24 00:00:00
dc.date.available2024-12-24 00:00:00
dc.date.issued2024-12-24
dc.description.abstractThis study investigates the optimisation of grounding infrastructure in substations by implementing the philosophy of the multi-objective algorithm NSGA-II Elite. A complete description of the operating scheme and the characteristic mechanisms that support the behaviour and development of optimal Pareto solutions is provided. A detailed comparison was made with the optimisation method used in the GMAT program of Aplicaciones Tecnológicas, based on a semi-optimization process derived from the correlation of semi-precision optimisation solutions. The results show that multi-objective optimisation using NSGA-II results in a significant cost reduction compared to the semi-optimization method, although the computational time required to reach the final solution increases significantly. This approach allows a more adequate understanding of optimising the terrestrial substation grid. It highlights its ability to generate more cost-effective and performance-efficient solutions by carefully considering the computing time required.eng
dc.format.mimetypeapplication/pdfeng
dc.identifier.doi10.32397/tesea.vol5.n2.616
dc.identifier.eissn2745-0120
dc.identifier.urlhttps://doi.org/10.32397/tesea.vol5.n2.616
dc.language.isoengeng
dc.publisherUniversidad Tecnológica de Bolívareng
dc.relation.bitstreamhttps://revistas.utb.edu.co/tesea/article/download/616/421
dc.relation.citationeditionNúm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applicationseng
dc.relation.citationendpage14
dc.relation.citationissue2eng
dc.relation.citationstartpage1
dc.relation.citationvolume5eng
dc.relation.ispartofjournalTransactions on Energy Systems and Engineering Applicationseng
dc.relation.referencesRamón Alfonso Gallego, Antonio Escobar, and Rubén Romero. Técnicas de optimización combinatorial. Universidad Tecnológica de Pereira, pages 19–77, 2006. [2] E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999. [3] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002. [4] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T Meyarivan. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, page 849–858. Springer Berlin Heidelberg, 2000. [5] L.E. Schrage. Optimization Modeling with LINDO. Duxbury Press, 1997.eng
dc.rightsMiriam Lucero Tenorio, Angel C. Valcárcel Rojas - 2024eng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2eng
dc.rights.creativecommonsThis work is licensed under a Creative Commons Attribution 4.0 International License.eng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0eng
dc.sourcehttps://revistas.utb.edu.co/tesea/article/view/616eng
dc.subjectStep voltageeng
dc.subjectTouch voltageeng
dc.subjectAlgorithm II (NSGA-II)eng
dc.subjectGrounding systemeng
dc.subjectnon-dominated Sorting Geneticeng
dc.subjectOptimizationeng
dc.titleMulti-objective grounding system optimisation using NSGA-IIspa
dc.title.translatedMulti-objective grounding system optimisation using NSGA-IIspa
dc.typeArtículo de revistaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501eng
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85eng
dc.type.contentTexteng
dc.type.driverinfo:eu-repo/semantics/articleeng
dc.type.localJournal articleeng
dc.type.versioninfo:eu-repo/semantics/publishedVersioneng

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