Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids
Symmetry 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 topol...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7484 |
| _version_ | 1789406752761970688 |
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| author | Guerrero López, Manuel Alejandro Baños Navarro, Raúl Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo |
| author_facet | Guerrero López, Manuel Alejandro Baños Navarro, Raúl Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo |
| author_sort | Guerrero López, Manuel Alejandro |
| collection | DSpace |
| description | Symmetry 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). |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7484 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-74842023-04-12T19:33:51Z Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids Guerrero López, Manuel Alejandro Baños Navarro, Raúl Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo power grids supergrids high-voltage power transmission complex networks community detection modularity evolutionary algorithms generational genetic algorithm modularity and improved genetic algorithm Louvain modularity algorithm Symmetry 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). 2020-01-17T07:50:13Z 2020-01-17T07:50:13Z 2019-12-03 info:eu-repo/semantics/article 2073-8994 http://hdl.handle.net/10835/7484 en https://www.mdpi.com/2073-8994/11/12/1472 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | power grids supergrids high-voltage power transmission complex networks community detection modularity evolutionary algorithms generational genetic algorithm modularity and improved genetic algorithm Louvain modularity algorithm Guerrero López, Manuel Alejandro Baños Navarro, Raúl Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids |
| title | Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids |
| title_full | Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids |
| title_fullStr | Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids |
| title_full_unstemmed | Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids |
| title_short | Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids |
| title_sort | evolutionary algorithms for community detection in continental-scale high-voltage transmission grids |
| topic | power grids supergrids high-voltage power transmission complex networks community detection modularity evolutionary algorithms generational genetic algorithm modularity and improved genetic algorithm Louvain modularity algorithm |
| url | http://hdl.handle.net/10835/7484 |
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