Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections
Inclusion of distributed generation and topological changes in a network originate several operating scenarios. For this reason, techniques that adjust the configuration of overcurrent relays have been developed in order to provide protection coordination strategies capable of operating in different...
- Autores:
-
Barranco, Carlos, A.
Orozco Henao, C.
Marín Quintero, J.
Mora Flórez, J.
Herrera Orozco, A.
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12353
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12353
- Palabra clave:
- Overcurrent Protection;
Microgrid;
Fault Current Limiters
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
| title |
Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
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Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections Overcurrent Protection; Microgrid; Fault Current Limiters LEMB |
| title_short |
Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
| title_full |
Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
| title_fullStr |
Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
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Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
| title_sort |
Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections |
| dc.creator.fl_str_mv |
Barranco, Carlos, A. Orozco Henao, C. Marín Quintero, J. Mora Flórez, J. Herrera Orozco, A. |
| dc.contributor.author.none.fl_str_mv |
Barranco, Carlos, A. Orozco Henao, C. Marín Quintero, J. Mora Flórez, J. Herrera Orozco, A. |
| dc.subject.keywords.spa.fl_str_mv |
Overcurrent Protection; Microgrid; Fault Current Limiters |
| topic |
Overcurrent Protection; Microgrid; Fault Current Limiters LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Inclusion of distributed generation and topological changes in a network originate several operating scenarios. For this reason, techniques that adjust the configuration of overcurrent relays have been developed in order to provide protection coordination strategies capable of operating in different schemes. However, the adjustments allowed by these devices are limited. Thus, scenario grouping techniques are proposed to reduce the number of required configurations. This paper aims to evaluate the performance of different grouping techniques with input parameters for coordination strategies of electrical overcurrent protections, where it is required to associate the different modes of operation of a distribution network. For the clustering process, unsupervised learning techniques such as K-means, K-medoids and Agglomerative Hierarchical Clustering were employed. Additionally, for the input characteristics, fault currents, nominal currents and other parameters obtained from the electrical system were taken into account. From the results obtained when evaluating different combinations of techniques and inputs, it is important to mention that the characteristics that describe the different modes of operation necessary for the grouping are decisive for the coordination strategies of electrical protections and that it is not possible to establish a significant difference between the clustering techniques evaluated. Lastly, the combination that presents the best performance was K-means: Manhattan and maximum short-circuit phase currents per relay with a sum of operation time of 428.72s and zero restriction violation. © 2022 IEEE. |
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2022 |
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2022 |
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2023-07-21T16:26:30Z |
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2023-07-21T16:26:30Z |
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2023 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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http://purl.org/coar/resource_type/c_6501 |
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Carlos, A. B., Henao, C. O., Quintero, J. M., Flórez, J. M., & Orozco, A. H. (2022, November). Clustering techniques performance for the coordination of adaptive overcurrent protections. In 2022 IEEE ANDESCON (pp. 1-6). IEEE. |
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https://hdl.handle.net/20.500.12585/12353 |
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10.1109/ANDESCON56260.2022.9989786 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Carlos, A. B., Henao, C. O., Quintero, J. M., Flórez, J. M., & Orozco, A. H. (2022, November). Clustering techniques performance for the coordination of adaptive overcurrent protections. In 2022 IEEE ANDESCON (pp. 1-6). IEEE. 10.1109/ANDESCON56260.2022.9989786 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12353 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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6 páginas |
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Cartagena de Indias |
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2022 IEEE ANDESCON: Technology and Innovation for Andean Industry |
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Universidad Tecnológica de Bolívar |
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Barranco, Carlos, A.b40f260a-6c43-426c-9b8f-6bc5d0c17509Orozco Henao, C.f3b2ff13-484c-4dac-bcb1-758cc0fd7af0Marín Quintero, J.1eb91321-8dee-42b2-99f8-3f2421582accMora Flórez, J.14954436-c2b6-4ab2-9194-071ad9e7aaeeHerrera Orozco, A.4f70fbde-ddd5-40d5-aa46-ab06c5dd3aea2023-07-21T16:26:30Z2023-07-21T16:26:30Z20222023Carlos, A. B., Henao, C. O., Quintero, J. M., Flórez, J. M., & Orozco, A. H. (2022, November). Clustering techniques performance for the coordination of adaptive overcurrent protections. In 2022 IEEE ANDESCON (pp. 1-6). IEEE.https://hdl.handle.net/20.500.12585/1235310.1109/ANDESCON56260.2022.9989786Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarInclusion of distributed generation and topological changes in a network originate several operating scenarios. For this reason, techniques that adjust the configuration of overcurrent relays have been developed in order to provide protection coordination strategies capable of operating in different schemes. However, the adjustments allowed by these devices are limited. Thus, scenario grouping techniques are proposed to reduce the number of required configurations. This paper aims to evaluate the performance of different grouping techniques with input parameters for coordination strategies of electrical overcurrent protections, where it is required to associate the different modes of operation of a distribution network. For the clustering process, unsupervised learning techniques such as K-means, K-medoids and Agglomerative Hierarchical Clustering were employed. Additionally, for the input characteristics, fault currents, nominal currents and other parameters obtained from the electrical system were taken into account. From the results obtained when evaluating different combinations of techniques and inputs, it is important to mention that the characteristics that describe the different modes of operation necessary for the grouping are decisive for the coordination strategies of electrical protections and that it is not possible to establish a significant difference between the clustering techniques evaluated. Lastly, the combination that presents the best performance was K-means: Manhattan and maximum short-circuit phase currents per relay with a sum of operation time of 428.72s and zero restriction violation. © 2022 IEEE.6 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf22022 IEEE ANDESCON: Technology and Innovation for Andean IndustryClustering Techniques Performance for the Coordination of Adaptive Overcurrent Protectionsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Overcurrent Protection;Microgrid;Fault Current LimitersLEMBCartagena de IndiasBritish Petroleum: 2020 Edition https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/energyoutlook/bp-energy-outlook-2020.pdfTon, D.T., Smith, M.A. The U.S. Department of Energy's Microgrid Initiative (2012) Electricity Journal, 25 (8), pp. 84-94. Cited 417 times. doi: 10.1016/j.tej.2012.09.013Theo, W.L., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T. Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods (2017) Renewable and Sustainable Energy Reviews, 67, pp. 531-573. Cited 211 times. https://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews doi: 10.1016/j.rser.2016.09.063Brearley, B.J., Prabu, R.R. A review on issues and approaches for microgrid protection (2017) Renewable and Sustainable Energy Reviews, 67, pp. 988-997. Cited 176 times. https://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews doi: 10.1016/j.rser.2016.09.047Souza, F.C., Souza, B.A. Adaptive overcurrent adjustment settings: A case study using RTDS® (2013) 2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013, art. no. 6554469. Cited 13 times. ISBN: 978-146735274-1 doi: 10.1109/ISGT-LA.2013.6554469Zhang, G., Guo, B., Liang, Y. A classification method for adaptive relay protection setting system based on clustering analysis (2011) APAP 2011 - Proceedings: 2011 International Conference on Advanced Power System Automation and Protection, 3, art. no. 6180633, pp. 1819-1823. Cited 3 times. ISBN: 978-142449619-8 doi: 10.1109/APAP.2011.6180633Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y. An efficient k-means clustering algorithms: Analysis and implementation (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (7), pp. 881-892. Cited 4154 times. doi: 10.1109/TPAMI.2002.1017616Dong, J., Qi, M. K-means optimization algorithm for solving clustering problem (2009) Proceedings - 2009 2nd International Workshop on Knowledge Discovery and Data Mining, WKKD 2009, art. no. 4771876, pp. 52-55. Cited 16 times. ISBN: 978-076953543-2 doi: 10.1109/WKDD.2009.85Kapil, S., Chawla, M. Performance evaluation of K-means clustering algorithm with various distance metrics (2016) 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016, art. no. 7853264. Cited 35 times. ISBN: 978-146738587-9 doi: 10.1109/ICPEICES.2016.7853264Ojaghi, M., Mohammadi, V. Use of Clustering to Reduce the Number of Different Setting Groups for Adaptive Coordination of Overcurrent Relays (2018) IEEE Transactions on Power Delivery, 33 (3), pp. 1204-1212. Cited 72 times. doi: 10.1109/TPWRD.2017.2749321Samadi, A., Mohammadi Chabanloo, R. Adaptive coordination of overcurrent relays in active distribution networks based on independent change of relays’ setting groups (2020) International Journal of Electrical Power and Energy Systems, 120, art. no. 106026. Cited 35 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2020.106026Ghadiri, S.M.E., Mazlumi, K. Adaptive protection scheme for microgrids based on SOM clustering technique (2020) Applied Soft Computing Journal, 88, art. no. 106062. Cited 39 times. http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description doi: 10.1016/j.asoc.2020.106062Saldarriaga-Zuluaga, S.D., Zuluaga, C.D., Muñoz-Galeano, N., López-Lezama, J.M. Optimal coordination of overcurrent relays in microgrids using a metaheuristic approach (2020) International Journal of Engineering Research and Technology, 13 (9), pp. 2213-2218. http://www.irphouse.com/ijert20/ijertv13n9_15.pdfGers, J.M., Holmes, E.J. (2011) Institution of Electrical Engineers: Protection of Electricity Distribution Networks. Energy Engineering Institution of Engineering and Technology, ISBN 9781849192231Overcurrent and Feeder Protection-SIPROTEC 7SJ82-Siemens Ag siemens.com Global Website https://cache.industry.siemens.comYang, H., Liu, X., Zhang, D., Chen, T., Li, C., Huang, W. 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ISBN: 978-142445585-0 doi: 10.1109/ICCAE.2010.5452085Nazari, Z., Kang, D., Asharif, M.R., Sung, Y., Ogawa, S. A new hierarchical clustering algorithm (2015) ICIIBMS 2015 - International Conference on Intelligent Informatics and Biomedical Sciences, art. no. 7439517, pp. 148-152. Cited 45 times. ISBN: 978-147998562-3 doi: 10.1109/ICIIBMS.2015.7439517Construct Agglomerative Clusters from Data-MATLAB Clusterdata https://la.mathworks.com/help/stats/clusterdata.html(2001) IEEE 34 Node Test Feeder. Cited 5 times. 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