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dc.contributor.authorGuerrero López, Manuel Alejandro
dc.contributor.authorBaños Navarro, Raúl
dc.contributor.authorGil Montoya, Consolación
dc.contributor.authorGil Montoya, Francisco
dc.contributor.authorAlcayde García, Alfredo
dc.date.accessioned2020-01-17T07:50:13Z
dc.date.available2020-01-17T07:50:13Z
dc.date.issued2019-12-03
dc.identifier.issn2073-8994
dc.identifier.urihttp://hdl.handle.net/10835/7484
dc.description.abstractSymmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectpower gridses_ES
dc.subjectsupergridses_ES
dc.subjecthigh-voltage power transmissiones_ES
dc.subjectcomplex networkses_ES
dc.subjectcommunity detectiones_ES
dc.subjectmodularityes_ES
dc.subjectevolutionary algorithmses_ES
dc.subjectgenerational genetic algorithmes_ES
dc.subjectmodularity and improved genetic algorithmes_ES
dc.subjectLouvain modularity algorithmes_ES
dc.titleEvolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Gridses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2073-8994/11/12/1472es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional