Data-driven topology detector for self-healing strategies in Active Distribution Networks
The integration of distributed energy resources requires the implementation of control and automation functionalities in distribution networks, which allow them to operate in a more flexible, efficient, and reliable way. The operation of these functionalities causes topological changes on the networ...
- Autores:
-
Marin-Quintero, J.
Orozco-Henao, C.
Mora-Florez, J.
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12334
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12334
- Palabra clave:
- Distribution System;
State Estimation;
Smart Meters
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Data-driven topology detector for self-healing strategies in Active Distribution Networks |
| title |
Data-driven topology detector for self-healing strategies in Active Distribution Networks |
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Data-driven topology detector for self-healing strategies in Active Distribution Networks Distribution System; State Estimation; Smart Meters LEMB |
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Data-driven topology detector for self-healing strategies in Active Distribution Networks |
| title_full |
Data-driven topology detector for self-healing strategies in Active Distribution Networks |
| title_fullStr |
Data-driven topology detector for self-healing strategies in Active Distribution Networks |
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Data-driven topology detector for self-healing strategies in Active Distribution Networks |
| title_sort |
Data-driven topology detector for self-healing strategies in Active Distribution Networks |
| dc.creator.fl_str_mv |
Marin-Quintero, J. Orozco-Henao, C. Mora-Florez, J. |
| dc.contributor.author.none.fl_str_mv |
Marin-Quintero, J. Orozco-Henao, C. Mora-Florez, J. |
| dc.subject.keywords.spa.fl_str_mv |
Distribution System; State Estimation; Smart Meters |
| topic |
Distribution System; State Estimation; Smart Meters LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
The integration of distributed energy resources requires the implementation of control and automation functionalities in distribution networks, which allow them to operate in a more flexible, efficient, and reliable way. The operation of these functionalities causes topological changes on the network that must be identified since these affect protection, volt/var control, and state estimation, among others. This paper presents a data-driven topology detector for self-healing strategies in Active Distribution Networks (ADN). This approach uses machine learning (ML) techniques to obtain a trained model as a topology detector. The ML models are integrated into the Intelligent Electronic Device (IED) of ADN so that using the voltage and current signals measured locally determine the network's topology. A features selection and tuning technique based on a multiverse optimizer are proposed to improve the ML model accuracy. This approach allows it to be implemented in decentralized architectures since each IED detects the system's topology from local measurements and does not depend on the availability of the communication system. The proposed topology detector was validated on a modified IEEE 123 nodes test feeder considering six topology changes, five DER outages, and five load variations. The results obtained show accuracy values above 96%, which evidences a highlighted potential for real-life applications. © 2023 The Author(s) |
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2023 |
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2023-07-21T16:23:28Z |
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2023-07-21T16:23:28Z |
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2023 |
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2023 |
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Marin-Quintero, J., Orozco-Henao, C., & Mora-Florez, J. (2023). Data-driven topology detector for self-healing strategies in Active Distribution Networks. Energy Reports, 9, 377-385. |
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https://hdl.handle.net/20.500.12585/12334 |
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10.1016/j.egyr.2023.01.005 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Marin-Quintero, J., Orozco-Henao, C., & Mora-Florez, J. (2023). Data-driven topology detector for self-healing strategies in Active Distribution Networks. Energy Reports, 9, 377-385. 10.1016/j.egyr.2023.01.005 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12334 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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8 páginas |
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Marin-Quintero, J.cee25f99-83ee-4fc2-8235-fec1fec18d98Orozco-Henao, C.f3b2ff13-484c-4dac-bcb1-758cc0fd7af0Mora-Florez, J.6e71b42b-271d-42a9-a08b-96bd714914f72023-07-21T16:23:28Z2023-07-21T16:23:28Z20232023Marin-Quintero, J., Orozco-Henao, C., & Mora-Florez, J. (2023). Data-driven topology detector for self-healing strategies in Active Distribution Networks. Energy Reports, 9, 377-385.https://hdl.handle.net/20.500.12585/1233410.1016/j.egyr.2023.01.005Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe integration of distributed energy resources requires the implementation of control and automation functionalities in distribution networks, which allow them to operate in a more flexible, efficient, and reliable way. The operation of these functionalities causes topological changes on the network that must be identified since these affect protection, volt/var control, and state estimation, among others. This paper presents a data-driven topology detector for self-healing strategies in Active Distribution Networks (ADN). This approach uses machine learning (ML) techniques to obtain a trained model as a topology detector. The ML models are integrated into the Intelligent Electronic Device (IED) of ADN so that using the voltage and current signals measured locally determine the network's topology. A features selection and tuning technique based on a multiverse optimizer are proposed to improve the ML model accuracy. This approach allows it to be implemented in decentralized architectures since each IED detects the system's topology from local measurements and does not depend on the availability of the communication system. The proposed topology detector was validated on a modified IEEE 123 nodes test feeder considering six topology changes, five DER outages, and five load variations. The results obtained show accuracy values above 96%, which evidences a highlighted potential for real-life applications. © 2023 The Author(s)8 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_abf2Energy ReportsData-driven topology detector for self-healing strategies in Active Distribution Networksinfo: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_2df8fbb1Distribution System;State Estimation;Smart MetersLEMBCartagena de IndiasAkorede, M.F., Hizam, H., Pouresmaeil, E. Distributed energy resources and benefits to the environment (2010) Renewable and Sustainable Energy Reviews, 14 (2), pp. 724-734. Cited 469 times. doi: 10.1016/j.rser.2009.10.025AL Shaqsi, A.Z., Sopian, K., Al-Hinai, A. Review of energy storage services, applications, limitations, and benefits (2020) Energy Reports, 6, pp. 288-306. Cited 243 times. http://www.journals.elsevier.com/energy-reports/ doi: 10.1016/j.egyr.2020.07.028Qadir, S.A., Al-Motairi, H., Tahir, F., Al-Fagih, L. Incentives and strategies for financing the renewable energy transition: A review (2021) Energy Reports, 7, pp. 3590-3606. Cited 106 times. http://www.journals.elsevier.com/energy-reports/ doi: 10.1016/j.egyr.2021.06.041Chowdhury, S., Chowdhury, S.P., Crossley, P. Microgrids and active distribution networks (2009) Microgrids and Active Distribution Networks, pp. 1-298. Cited 584 times. http://dx.doi.org/10.1049/PBRN006E ISBN: 978-184919102-9; 978-184919014-5Sakai, R.T., Almeida, C.F.M., Rosa, L.H.L., Kagan, N., Pereira, D.S., Medeiros, T.S., Kagan, H., (...), Brito, J.A.S. Architecture Deployment for Application of Advanced Distribution Automation Functionalities in Smart Grids (2022) Journal of Control, Automation and Electrical Systems, 33 (1), pp. 219-228. Cited 2 times. http://rd.springer.com/journal/40313 doi: 10.1007/s40313-021-00799-6Greer, R., Allen, W., Schnegg, J., Dulmage, A. Distribution automation systems with advanced features (2011) Papers Presented at the Annual Conference - Rural Electric Power Conference, art. no. 5756721. Cited 16 times. ISBN: 978-161284056-7 doi: 10.1109/REPCON.2011.5756721Cisco Systems Distribution automation - secondary substation design guide (2019)Farajollahi, M., Shahsavari, A., Mohsenian-Rad, H. Topology Identification in Distribution Systems Using Line Current Sensors: An MILP Approach (Open Access) (2020) IEEE Transactions on Smart Grid, 11 (2), art. no. 8787584, pp. 1159-1170. Cited 51 times. https://ieeexplore.ieee.org/servlet/opac?punumber=5165411 doi: 10.1109/TSG.2019.2933006Amoateng, D.O., Yan, R., Mosadeghy, M., Saha, T.K. Topology Detection in Power Distribution Networks: A PMU Based Deep Learning Approach (2022) IEEE Transactions on Power Systems, 37 (4), pp. 2771-2782. Cited 2 times. https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=59 doi: 10.1109/TPWRS.2021.3128428Zhang, X., Li, Y., Yang, C., Wang, S., Xie, W., Ling, P. Topology Analysis of Distribution Network Based on uPMU and SCADA (2018) 2018 international conference on power system technology, POWERCON.Soltani, Z., Ma, S., Khorsand, M., Vittal, V. Simultaneous Robust State Estimation, Topology Error Processing, and Outage Detection for Unbalanced Distribution Systems (2023) IEEE Transactions on Power Systems, 38 (3), pp. 2018-2034. https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=59 doi: 10.1109/TPWRS.2022.3181118Zhang, J., Wang, Y., Weng, Y., Zhang, N. Topology Identification and Line Parameter Estimation for Non-PMU Distribution Network: A Numerical Method (2020) IEEE Transactions on Smart Grid, 11 (5), art. no. 9027950, pp. 4440-4453. Cited 90 times. https://ieeexplore.ieee.org/servlet/opac?punumber=5165411 doi: 10.1109/TSG.2020.2979368Liao, Y., Weng, Y., Liu, G., Rajagopal, R. Urban MV and LV distribution grid topology estimation via group Lasso (Open Access) (2019) IEEE Transactions on Power Systems, 34 (1), art. no. 8456535, pp. 12-27. Cited 69 times. https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=59 doi: 10.1109/TPWRS.2018.2868877Marín-Quintero, J., Orozco-Henao, C., Velez, J.C., Bretas, A.S. Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model (Open Access) (2021) International Journal of Electrical Power and Energy Systems, 130, art. no. 106960. Cited 11 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2021.106960Marín-Quintero, J., Orozco-Henao, C., Percybrooks, W.S., Vélez, J.C., Montoya, O.D., Gil-González, W. Toward an adaptive protection scheme in active distribution networks: Intelligent approach fault detector (Open Access) (2021) Applied Soft Computing, 98, art. no. 106839. Cited 13 times. http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description doi: 10.1016/j.asoc.2020.106839Mirjalili, S., Mirjalili, S.M., Hatamlou, A. Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (Open Access) (2016) Neural Computing and Applications, 27 (2), pp. 495-513. Cited 1640 times. http://link.springer.com/journal/521 doi: 10.1007/s00521-015-1870-7Schneider, K.P., Mather, B.A., Pal, B.C., Ten, C.-W., Shirek, G.J., Zhu, H., Fuller, J.C., (...), Kersting, W. Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders (2018) IEEE Transactions on Power Systems, 33 (3), pp. 3181-3188. Cited 316 times. doi: 10.1109/TPWRS.2017.2760011Kerting, W.H. Radial distribution test feeders IEEE distribution planning working group report (Open Access) (1991) IEEE Transactions on Power Systems, 6 (3), pp. 975-985. Cited 917 times. doi: 10.1109/59.119237Distribution system analysis subcommittee (2014) IEEE 123 Node Test FeederMarín-Quintero, J., Orozco-Henao, C., Percybrooks, W.S., Vélez, J.C., Montoya, O.D., Gil-González, W. Toward an adaptive protection scheme in active distribution networks: Intelligent approach fault detector (2021) Applied Soft Computing, 98, art. no. 106839. Cited 13 times. http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description doi: 10.1016/j.asoc.2020.106839Marín-Quintero, J., Orozco-Henao, C., Velez, J.C., Bretas, A.S. Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model (2021) International Journal of Electrical Power and Energy Systems, 130, art. no. 106960. 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