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:20.500.12585/12433
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12433
https://doi.org/10.1016/j.egyr.2023.01.005
- Palabra clave:
- Topology detector
Distributed energy resources
Machine learning tecniques
- 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 |
| spellingShingle |
Data-driven topology detector for self-healing strategies in Active Distribution Networks Topology detector Distributed energy resources Machine learning tecniques |
| title_short |
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 |
| title_full_unstemmed |
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 |
Topology detector Distributed energy resources Machine learning tecniques |
| topic |
Topology detector Distributed energy resources Machine learning tecniques |
| 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. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-25T12:10:58Z |
| dc.date.available.none.fl_str_mv |
2023-07-25T12:10:58Z |
| dc.date.issued.none.fl_str_mv |
2023-01-09 |
| dc.date.submitted.none.fl_str_mv |
2023-06-24 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/draft |
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https://hdl.handle.net/20.500.12585/12433 |
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https://doi.org/10.1016/j.egyr.2023.01.005 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12433 https://doi.org/10.1016/j.egyr.2023.01.005 |
| identifier_str_mv |
Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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9 páginas |
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application/pdf |
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Cartagena de Indias |
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Energy reports |
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Universidad Tecnológica de Bolívar |
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Marin-Quintero, Jcee25f99-83ee-4fc2-8235-fec1fec18d98Orozco-Henao, Cf3b2ff13-484c-4dac-bcb1-758cc0fd7af0Mora-Florez, J6e71b42b-271d-42a9-a08b-96bd714914f72023-07-25T12:10:58Z2023-07-25T12:10:58Z2023-01-092023-06-24https://hdl.handle.net/20.500.12585/12433https://doi.org/10.1016/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.9 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_2df8fbb1http://purl.org/coar/version/c_b1a7d7d4d402bcceTopology detectorDistributed energy resourcesMachine learning tecniquesCartagena de IndiasCampus TecnológicoPúblico generalAkorede MF, Hizam H, Pouresmaeil E. Distributed energy resources and benefits to the environment. Renew Sustain Energy Rev 2010;14:724–34. http://dx.doi.org/10.1016/j.rser.2009.10.025al Shaqsi AZ, Sopian K, Al-Hinai A. Review of energy storage servicesapplications, limitations, and benefits. Energy Rep 2020;6:288–306. http://dx.doi.org/10.1016/j.egyr.2020.07.028.Qadir SA, Al-Motairi H, Tahir F, Al-Fagih L. Incentives and strategies for financing the renewable energy transition: A review. Energy Rep 2021;7:3590–606. http://dx.doi.org/10.1016/j.egyr.2021.06.041.Chowdhury S, Chowdhury S, Crossley P. Microgrids and active distribution networks. 2009.Sakai RT, Almeida CFM, Rosa LHL, Kagan N, Pereira DdeS, Medeiros TS, et al. Architecture deployment for application of advanced distribution automation functionalities in smart grids. J Control Autom Electr Syst 2022;33:219–28. http://dx.doi.org/10.1007/s40313- 021-00799-6.Greer R, Allen W, Schnegg J, Dulmage A. Distribution automation systems with advanced features. In: Papers presented at the annual conference - rural electric power conference. 2011, http://dx.doi.org/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. IEEE Trans Smart Grid 2020;11:1159–70. http://dx.doi.org/10.1109/TSG.2019.2933006.Amoateng DO, Yan R, Mosadeghy M, Saha TK. Topology detection in power distribution networks: A PMU based deep learning approach. IEEE Trans Power Syst 2021;37:2771–82. http://dx.doi.org/10.1109/tpwrs.2021.3128428.Zhang X, Li Y, Yang C, Wang S, Xie W, Ling P. Topology analysis of distribution network based on uPMU and SCADA. In: 2018 international conference on power system technology. POWERCON, 2018.Soltani Z, Ma S, Khorsand M, Vittal V. Simultaneous robust state estimation, topology error processing, and outage detection for unbalanced distribution systems. IEEE Trans Power Syst 2022;1–15. http://dx.doi.org/10.1109/tpwrs.2022.3181118.Zhang J, Wang Y, Weng Y, Zhang N. Topology identification and line parameter estimation for non-PMU distribution network: A numerical method. IEEE Trans Smart Grid 2020;11:4440–53. http://dx.doi.org/10.1109/TSG.2020.2979368.Liao Y, Weng Y, Liu G, Rajagopal R. Urban MV and LV distribution grid topology estimation via group Lasso. IEEE Trans Power Syst 2019;34:12–27. http://dx.doi.org/10.1109/TPWRS.2018.2868877.Marín-Quintero J, Orozco-Henao C, Velez JC, Bretas AS. Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model. Int J Electr Power Energy Syst 2021;130:106960. http://dx.doi.org/10.1016/j.ijepes.2021.106960Marín-Quintero J, Orozco-Henao C, Percybrooks WS, Vélez JC, Montoya OD, Gil-González W. Toward an adaptive protection scheme in active distribution networks: Intelligent approach fault detector. Appl Soft Comput 2020;106839. http://dx.doi.org/10.1016/j.asoc. 2020.106839Mirjalili S, Mirjalili SM, Hatamlou A. Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Comput Appl 2016;27:495–513. http://dx.doi.org/10.1007/s00521-015-1870-7Schneider KP, Mather BA, Pal BC, Ten CW, Shirek GJ, Zhu H, et al. Analytic considerations and design basis for the IEEE distribution test feeders. IEEE Trans Power Syst 2018;33:3181–8. http://dx.doi.org/10.1109/TPWRS.2017.2760011.Kersting WH. Radial distribution test feeders. Trans Power Syst 1991;6:975–85.Distribution system analysis subcommittee. IEEE 123 Node Test Feeder, 2014.0] Marin-Quintero J, Orozco-Henao C, Percybrooks WS, et al. Toward an adaptive protection scheme in active distribution networks: Intelligent approach fault detector. Appl Soft Comput 2021;98:106839.Marín-quintero J, Orozco-henao C, Velez JC, et al. Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model. 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