Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper propose...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/3/1241 |
| _version_ | 1797405951141085184 |
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| author | Sergio D. Saldarriaga-Zuluaga Jesús M. López-Lezama Nicolás Muñoz-Galeano |
| author_facet | Sergio D. Saldarriaga-Zuluaga Jesús M. López-Lezama Nicolás Muñoz-Galeano |
| author_sort | Sergio D. Saldarriaga-Zuluaga |
| collection | DOAJ |
| description | Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach. |
| first_indexed | 2024-03-09T03:18:39Z |
| format | Article |
| id | doaj.art-2ce87d7a306e4e60b3734803b234e58b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:18:39Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2ce87d7a306e4e60b3734803b234e58b2023-12-03T15:13:36ZengMDPI AGApplied Sciences2076-34172021-01-01113124110.3390/app11031241Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning TechniquesSergio D. Saldarriaga-Zuluaga0Jesús M. López-Lezama1Nicolás Muñoz-Galeano2Departamento de Eléctrica, Facultad de Ingenieria, Institución Universitaria Pascual Bravo, Calle 73 No. 73A-226, Medellín 050036, ColombiaGrupo en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellín 050010, ColombiaGrupo en Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Calle 70 No. 52-21, Medellín 050010, ColombiaMicrogrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach.https://www.mdpi.com/2076-3417/11/3/1241distributed generationdistribution networksmicrogridspower system protectionover-current relay coordinationunsupervised learning techniques |
| spellingShingle | Sergio D. Saldarriaga-Zuluaga Jesús M. López-Lezama Nicolás Muñoz-Galeano Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques Applied Sciences distributed generation distribution networks microgrids power system protection over-current relay coordination unsupervised learning techniques |
| title | Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques |
| title_full | Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques |
| title_fullStr | Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques |
| title_full_unstemmed | Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques |
| title_short | Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques |
| title_sort | optimal coordination of over current relays in microgrids using unsupervised learning techniques |
| topic | distributed generation distribution networks microgrids power system protection over-current relay coordination unsupervised learning techniques |
| url | https://www.mdpi.com/2076-3417/11/3/1241 |
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