Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence
Fault location (FL) is one of the main challenges in Advanced Distribution Automation (ADA) of Active Distribution Networks (ADN). One of the commonly used strategies by utilities to deal with this challenge is the use of Fault Indicators (FIs), which indicate to the operator the path taken by the f...
- Autores:
-
Orozco Henao, C.
Herrera Orozco, A.
Marín Quintero, Juan Guillermo
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13998
- Acceso en línea:
- https://hdl.handle.net/11323/13998
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial neural networks
Fault indicators
Fault location
Microgrids
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
| id |
RCUC2_21bfecb46d214dc3b56399f09167d5f4 |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/13998 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| title |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| spellingShingle |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence Artificial neural networks Fault indicators Fault location Microgrids |
| title_short |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| title_full |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| title_fullStr |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| title_full_unstemmed |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| title_sort |
Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence |
| dc.creator.fl_str_mv |
Orozco Henao, C. Herrera Orozco, A. Marín Quintero, Juan Guillermo |
| dc.contributor.author.none.fl_str_mv |
Orozco Henao, C. Herrera Orozco, A. Marín Quintero, Juan Guillermo |
| dc.subject.proposal.none.fl_str_mv |
Artificial neural networks Fault indicators Fault location Microgrids |
| topic |
Artificial neural networks Fault indicators Fault location Microgrids |
| description |
Fault location (FL) is one of the main challenges in Advanced Distribution Automation (ADA) of Active Distribution Networks (ADN). One of the commonly used strategies by utilities to deal with this challenge is the use of Fault Indicators (FIs), which indicate to the operator the path taken by the fault current. However, a good performance of this scheme depends on the number of installed devices, a high number of them could cause a high cost for the utility investment planning. In this context, this paper presents an artificial intelligence-based fault location strategy that determines the number and location of FI into ADN to maximize performance in fault section estimation. To achieve this objective, the ADN is divided into sections, and the FL problem is modeled as a classification problem to train an Artificial Neural Network (ANN). To determine the number of FIs to be installed and their location, the strategy uses the three-phase current magnitudes measured by the FI as features for an ANN model. Also, the strategy uses a feature selection and tuning scheme based on a multiverse optimization algorithm (MOA) to identify the features that maximize the accuracy of the ANN model. The strategy was validated on the IEEE123-node test feeder. The results showed accuracy close to 99.4 % with a reduction of 40 % of the number of FIs when compared with other method. The strategy shows its simplicity and promising prospects to apply it in the utility's investment planning. |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024-09 |
| dc.date.accessioned.none.fl_str_mv |
2025-02-27T15:56:19Z |
| dc.date.available.none.fl_str_mv |
2025-02-27T15:56:19Z |
| dc.type.none.fl_str_mv |
Artículo de revista |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| status_str |
publishedVersion |
| dc.identifier.citation.none.fl_str_mv |
Marín-Quintero, J., Orozco-Henao, C., Herrera-Orozco, A. Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence, Marín-Quintero, J., Orozco-Henao, C., Herrera-Orozco, A., Electric Power Systems Research, 234, 110701, 2024, 2024/09/01/, 0378-7796, https://doi.org/10.1016/j.epsr.2024.110701 |
| dc.identifier.issn.none.fl_str_mv |
0378-7796 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/13998 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.epsr.2024.110701 |
| dc.identifier.instname.none.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.none.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Marín-Quintero, J., Orozco-Henao, C., Herrera-Orozco, A. Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence, Marín-Quintero, J., Orozco-Henao, C., Herrera-Orozco, A., Electric Power Systems Research, 234, 110701, 2024, 2024/09/01/, 0378-7796, https://doi.org/10.1016/j.epsr.2024.110701 0378-7796 10.1016/j.epsr.2024.110701 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/13998 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.none.fl_str_mv |
Electric Power Systems Research |
| dc.relation.references.none.fl_str_mv |
International Renewable Energy Agency (IRENA), World Energy Transitions Outlook 2023, 2023 [Online]. Available, https://irena.org/Digital-Report/WorldEnergy-Transitions-Outlook-2022%0Ahttps://irena.org/publications/2021/March /World-Energy-Transitions-Outlook Cisco Systems, Distribution Automation - Secondary Substation Design Guide, 2019 [Online]. Available, https://www.cisco.com/c/en/us/td/docs/solutions/Vertica ls/Distributed-Automation/Secondary-Substation/DG/DA-SS-DG.pdf. A.S. Bretas, C. Orozco-Henao, J. Marín-Quintero, O.D. Montoya, W. Gil-Gonz´ alez, N.G. Bretas, Microgrids physics model-based fault location formulation: analyticbased distributed energy resources effect compensation, Electr. Power Syst. Res. 195 (2021), https://doi.org/10.1016/j.epsr.2021.107178. October 2020. C.G. Arsoniadis, V.C. Nikolaidis, Precise fault location in active distribution systems using unsynchronized source measurements, IEEE Syst. J. 17 (3) (2023) 4114–4125, https://doi.org/10.1109/JSYST.2023.3279232. D. Lu, Y. Liu, Fault location for general AC/DC transmission lines: multi-phase, non-homogeneous, partially mutually coupled and multi-terminal lines, Electr. Power Syst. Res. 222 (2023) 109484, https://doi.org/10.1016/j. epsr.2023.109484. April. Y. Xu, C. Zhao, S. Xie, M. Lu, Novel fault location for high permeability active distribution networks based on improved VMD and S-transform, IEEE Access 9 (2021) 17662–17671, https://doi.org/10.1109/ACCESS.2021.3052349. X. Wang, et al., Fault location based on variable mode decomposition and kurtosis calibration in distribution networks, Int. J. Electr. Power Energy Syst. 154 (2023) 109463, https://doi.org/10.1016/j.ijepes.2023.109463. August. J. Atencia-De la Ossa, C. Orozco-Henao, J. Marín-Quintero, Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids, Int. J. Electr. Power Energy Syst. 148 (2023) 108923, https://doi.org/10.1016/j.ijepes.2022.108923. June 2022. A. Farughian, L. Kumpulainen, K. Kauhaniemi, Review of methodologies for earth fault indication and location in compensated and unearthed MV distribution networks, Electr. Power Syst. Res. 154 (2018) 373–380, https://doi.org/10.1016/j. epsr.2017.09.006. A. Ghaemi, A. Safari, H. Afsharirad, H. Shayeghi, Accuracy enhance of fault classification and location in a smart distribution network based on stacked ensemble learning, Electr. Power Syst. Res. 205 (2022) 107766, https://doi.org/ 10.1016/j.epsr.2021.107766. December 2021. Z. Li, et al., Cognitive knowledge graph generation for grid fault handling based on attention mechanism combined with multi-modal factor fusion, Comput. Electr. Eng. 111 (2023) 108855, https://doi.org/10.1016/j.compeleceng.2023.108855. PA. P. Stefanidou-Voziki, N. Sapountzoglou, B. Raison, J.L. Dominguez-Garcia, A review of fault location and classification methods in distribution grids, Electr. Power Syst. Res. 209 (2022) 108031, https://doi.org/10.1016/j. epsr.2022.108031. April. R.F.G. Sau, V.P. Dardengo, M.C. de Almeida, Allocation of fault indicators in distribution feeders containing distributed generation, Electr. Power Syst. Res. 179 (2020) 106060, https://doi.org/10.1016/j.epsr.2019.106060. October 2019. Y. Jiang, Data-driven fault location of electric power distribution systems with distributed generation, IEEE Trans. Smart Grid 11 (1) (2020) 129–137, https://doi. org/10.1109/TSG.2019.2918195. B. Li, J. Wei, Y. Liang, B. Chen, Optimal placement of fault indicator and sectionalizing switch in distribution networks, IEEE Access 8 (2020) 17619–17631, https://doi.org/10.1109/ACCESS.2020.2968092. T.T. Ku, C.S. Li, C.H. Lin, C.S. Chen, C.T. Hsu, Faulty line-section identification method for distribution systems based on fault indicators, IEEE Trans. Ind. Appl. 57 (2) (2021) 1335–1343, https://doi.org/10.1109/TIA.2020.3045672. G.G. Santos, J.C.M. Vieira, Optimal placement of fault indicators to identify fault zones in distribution systems, IEEE Trans. Power Deliv. 36 (5) (2021) 3282–3285, https://doi.org/10.1109/TPWRD.2021.3101671. Y. Jiang, Outage management of active distribution systems with data fusion from multiple sensors given sensor failures, IEEE Trans. Power Deliv. 38 (3) (2023) 1891–1903, https://doi.org/10.1109/TPWRD.2022.3227184. M. Gholami, I. Ahmadi, M. Pouriani, Optimal placement of fault indicator and remote-controlled switches for predetermined reliability of selected buses, IET Gener. Transm. Distrib. 17 (12) (2023) 2799–2810, https://doi.org/10.1049/ gtd2.12854 J.-U.S. Myong-Soo Kim, Jae-Guk An, Yun-Sik Oh, Seong-Il Lim, Dong-Hee Kwak, A method for fault section identification of distribution networks based on validation of fault indicators using artificial neural network, Energies 16 (2023) 1–14, 10.3390/en16145397 Academic. C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag, Berlin, Heidelberg, 2006. E. Correa-Tapasco, J. Mora-Florez, ´ S. Perez-Londono, ˜ Performance analysis of a learning structured fault locator for distribution systems in the case of polluted inputs, Electr. Power Syst. Res. 166 (2019) 1–8, https://doi.org/10.1016/j. epsr.2018.09.016. August 2018. E. Hosseini, K.Z. Ghafoor, A. Emrouznejad, A.S. Sadiq, D.B. Rawat, Novel metaheuristic based on multiverse theory for optimization problems in emerging systems, Appl. Intell. 51 (6) (Jun. 2021) 3275–3292, https://doi.org/10.1007/ s10489-020-01920-z. J. Marín-Quintero, C. Orozco-Henao, J.C. Velez, A.S. Bretas, Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model, Int. J. Electr. Power Energy Syst. 130 (2021) 106960, https://doi.org/10.1016/j. ijepes.2021.106960. February Y. Bengio. Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade: Second Edition pp. 437–478. Berlin, Heidelberg: Springer Berlin Heidelberg. 10.1007/978-3-642-3 5289-8_26. S. Mirjalili, S.M. Mirjalili, A. Hatamlou, Multi-Verse Optimizer: a nature-inspired algorithm for global optimization, Neural Comput. Appl. 27 (2) (Feb. 2016) 495–513, https://doi.org/10.1007/s00521-015-1870-7. K.P. Schneider, et al., Analytic considerations and design basis for the IEEE distribution test feeders, IEEE Trans. Power Syst. 33 (3) (May 2018) 3181–3188, https://doi.org/10.1109/TPWRS.2017.2760011. W. Kersting, Distribution System Distribution Syste Modeling and Analysis, CRC Press, New Mexico Boca, 2012. W.H. Kersting, Radial distribution test feeders, Trans. Power Syst. 6 (3) (1991) 975–985 [Online]. Available, http://ewh.ieee.org/soc/pes/dsacom/testfeeders.ht ml. Y. Gong, A. Guzm´ an, Distribution feeder fault location using IED and FCI information, in: 2011 64th Annu. Conf. Prot. Relay Eng, 2011, pp. 168–177, https://doi.org/10.1109/CPRE.2011.6035617 |
| dc.relation.citationendpage.none.fl_str_mv |
8 |
| dc.relation.citationstartpage.none.fl_str_mv |
1 |
| dc.relation.citationissue.none.fl_str_mv |
110701 |
| dc.relation.citationvolume.none.fl_str_mv |
234 |
| dc.rights.eng.fl_str_mv |
© 2024 The Author(s) |
| dc.rights.license.none.fl_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) |
| dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) © 2024 The Author(s) https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
8 páginas |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier B.V. |
| dc.publisher.place.none.fl_str_mv |
Netherlands |
| publisher.none.fl_str_mv |
Elsevier B.V. |
| dc.source.none.fl_str_mv |
https://www.sciencedirect.com/science/article/pii/S037877962400587X?pes=vor&utm_source=scopus&getft_integrator=scopus |
| institution |
Corporación Universidad de la Costa |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/59ad5ea2-ce46-4131-9e7e-d792112b80e2/download https://repositorio.cuc.edu.co/bitstreams/5e9d2c72-359c-4914-be63-623b8a5d0f99/download https://repositorio.cuc.edu.co/bitstreams/fe6b1063-5de3-4a8b-8800-6472c5f00944/download https://repositorio.cuc.edu.co/bitstreams/5ce7a413-590f-40ba-8b71-7574a57ccb6a/download |
| bitstream.checksum.fl_str_mv |
a60d199b1d3129a9ee6b41675f2d2d8d 73a5432e0b76442b22b026844140d683 d0a495a462a0f79a48fe672eba8263ae 9d740abad2ea2ae0cc7aec2d67aef5bc |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1828166790475153408 |
| spelling |
Atribución 4.0 Internacional (CC BY 4.0)© 2024 The Author(s)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Orozco Henao, C.Herrera Orozco, A.Marín Quintero, Juan Guillermovirtual::949-12025-02-27T15:56:19Z2025-02-27T15:56:19Z2024-09Marín-Quintero, J., Orozco-Henao, C., Herrera-Orozco, A. Fault indicators allocation to maximize the performance of a fault locator based on artificial intelligence, Marín-Quintero, J., Orozco-Henao, C., Herrera-Orozco, A., Electric Power Systems Research, 234, 110701, 2024, 2024/09/01/, 0378-7796, https://doi.org/10.1016/j.epsr.2024.1107010378-7796https://hdl.handle.net/11323/1399810.1016/j.epsr.2024.110701Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Fault location (FL) is one of the main challenges in Advanced Distribution Automation (ADA) of Active Distribution Networks (ADN). One of the commonly used strategies by utilities to deal with this challenge is the use of Fault Indicators (FIs), which indicate to the operator the path taken by the fault current. However, a good performance of this scheme depends on the number of installed devices, a high number of them could cause a high cost for the utility investment planning. In this context, this paper presents an artificial intelligence-based fault location strategy that determines the number and location of FI into ADN to maximize performance in fault section estimation. To achieve this objective, the ADN is divided into sections, and the FL problem is modeled as a classification problem to train an Artificial Neural Network (ANN). To determine the number of FIs to be installed and their location, the strategy uses the three-phase current magnitudes measured by the FI as features for an ANN model. Also, the strategy uses a feature selection and tuning scheme based on a multiverse optimization algorithm (MOA) to identify the features that maximize the accuracy of the ANN model. The strategy was validated on the IEEE123-node test feeder. The results showed accuracy close to 99.4 % with a reduction of 40 % of the number of FIs when compared with other method. The strategy shows its simplicity and promising prospects to apply it in the utility's investment planning.8 páginasapplication/pdfengElsevier B.V.Netherlandshttps://www.sciencedirect.com/science/article/pii/S037877962400587X?pes=vor&utm_source=scopus&getft_integrator=scopusFault indicators allocation to maximize the performance of a fault locator based on artificial intelligenceArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Electric Power Systems ResearchInternational Renewable Energy Agency (IRENA), World Energy Transitions Outlook 2023, 2023 [Online]. Available, https://irena.org/Digital-Report/WorldEnergy-Transitions-Outlook-2022%0Ahttps://irena.org/publications/2021/March /World-Energy-Transitions-OutlookCisco Systems, Distribution Automation - Secondary Substation Design Guide, 2019 [Online]. Available, https://www.cisco.com/c/en/us/td/docs/solutions/Vertica ls/Distributed-Automation/Secondary-Substation/DG/DA-SS-DG.pdf.A.S. Bretas, C. Orozco-Henao, J. Marín-Quintero, O.D. Montoya, W. Gil-Gonz´ alez, N.G. Bretas, Microgrids physics model-based fault location formulation: analyticbased distributed energy resources effect compensation, Electr. Power Syst. Res. 195 (2021), https://doi.org/10.1016/j.epsr.2021.107178. October 2020.C.G. Arsoniadis, V.C. Nikolaidis, Precise fault location in active distribution systems using unsynchronized source measurements, IEEE Syst. J. 17 (3) (2023) 4114–4125, https://doi.org/10.1109/JSYST.2023.3279232.D. Lu, Y. Liu, Fault location for general AC/DC transmission lines: multi-phase, non-homogeneous, partially mutually coupled and multi-terminal lines, Electr. Power Syst. Res. 222 (2023) 109484, https://doi.org/10.1016/j. epsr.2023.109484. April.Y. Xu, C. Zhao, S. Xie, M. Lu, Novel fault location for high permeability active distribution networks based on improved VMD and S-transform, IEEE Access 9 (2021) 17662–17671, https://doi.org/10.1109/ACCESS.2021.3052349.X. Wang, et al., Fault location based on variable mode decomposition and kurtosis calibration in distribution networks, Int. J. Electr. Power Energy Syst. 154 (2023) 109463, https://doi.org/10.1016/j.ijepes.2023.109463. August.J. Atencia-De la Ossa, C. Orozco-Henao, J. Marín-Quintero, Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids, Int. J. Electr. Power Energy Syst. 148 (2023) 108923, https://doi.org/10.1016/j.ijepes.2022.108923. June 2022.A. Farughian, L. Kumpulainen, K. Kauhaniemi, Review of methodologies for earth fault indication and location in compensated and unearthed MV distribution networks, Electr. Power Syst. Res. 154 (2018) 373–380, https://doi.org/10.1016/j. epsr.2017.09.006.A. Ghaemi, A. Safari, H. Afsharirad, H. Shayeghi, Accuracy enhance of fault classification and location in a smart distribution network based on stacked ensemble learning, Electr. Power Syst. Res. 205 (2022) 107766, https://doi.org/ 10.1016/j.epsr.2021.107766. December 2021.Z. Li, et al., Cognitive knowledge graph generation for grid fault handling based on attention mechanism combined with multi-modal factor fusion, Comput. Electr. Eng. 111 (2023) 108855, https://doi.org/10.1016/j.compeleceng.2023.108855. PA.P. Stefanidou-Voziki, N. Sapountzoglou, B. Raison, J.L. Dominguez-Garcia, A review of fault location and classification methods in distribution grids, Electr. Power Syst. Res. 209 (2022) 108031, https://doi.org/10.1016/j. epsr.2022.108031. April.R.F.G. Sau, V.P. Dardengo, M.C. de Almeida, Allocation of fault indicators in distribution feeders containing distributed generation, Electr. Power Syst. Res. 179 (2020) 106060, https://doi.org/10.1016/j.epsr.2019.106060. October 2019.Y. Jiang, Data-driven fault location of electric power distribution systems with distributed generation, IEEE Trans. Smart Grid 11 (1) (2020) 129–137, https://doi. org/10.1109/TSG.2019.2918195.B. Li, J. Wei, Y. Liang, B. Chen, Optimal placement of fault indicator and sectionalizing switch in distribution networks, IEEE Access 8 (2020) 17619–17631, https://doi.org/10.1109/ACCESS.2020.2968092.T.T. Ku, C.S. Li, C.H. Lin, C.S. Chen, C.T. Hsu, Faulty line-section identification method for distribution systems based on fault indicators, IEEE Trans. Ind. Appl. 57 (2) (2021) 1335–1343, https://doi.org/10.1109/TIA.2020.3045672.G.G. Santos, J.C.M. Vieira, Optimal placement of fault indicators to identify fault zones in distribution systems, IEEE Trans. Power Deliv. 36 (5) (2021) 3282–3285, https://doi.org/10.1109/TPWRD.2021.3101671.Y. Jiang, Outage management of active distribution systems with data fusion from multiple sensors given sensor failures, IEEE Trans. Power Deliv. 38 (3) (2023) 1891–1903, https://doi.org/10.1109/TPWRD.2022.3227184.M. Gholami, I. Ahmadi, M. Pouriani, Optimal placement of fault indicator and remote-controlled switches for predetermined reliability of selected buses, IET Gener. Transm. Distrib. 17 (12) (2023) 2799–2810, https://doi.org/10.1049/ gtd2.12854J.-U.S. Myong-Soo Kim, Jae-Guk An, Yun-Sik Oh, Seong-Il Lim, Dong-Hee Kwak, A method for fault section identification of distribution networks based on validation of fault indicators using artificial neural network, Energies 16 (2023) 1–14, 10.3390/en16145397 Academic.C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag, Berlin, Heidelberg, 2006.E. Correa-Tapasco, J. Mora-Florez, ´ S. Perez-Londono, ˜ Performance analysis of a learning structured fault locator for distribution systems in the case of polluted inputs, Electr. Power Syst. Res. 166 (2019) 1–8, https://doi.org/10.1016/j. epsr.2018.09.016. August 2018.E. Hosseini, K.Z. Ghafoor, A. Emrouznejad, A.S. Sadiq, D.B. Rawat, Novel metaheuristic based on multiverse theory for optimization problems in emerging systems, Appl. Intell. 51 (6) (Jun. 2021) 3275–3292, https://doi.org/10.1007/ s10489-020-01920-z.J. Marín-Quintero, C. Orozco-Henao, J.C. Velez, A.S. Bretas, Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model, Int. J. Electr. Power Energy Syst. 130 (2021) 106960, https://doi.org/10.1016/j. ijepes.2021.106960. FebruaryY. Bengio. Practical recommendations for gradient-based training of deep architectures. In Neural Networks: Tricks of the Trade: Second Edition pp. 437–478. Berlin, Heidelberg: Springer Berlin Heidelberg. 10.1007/978-3-642-3 5289-8_26.S. Mirjalili, S.M. Mirjalili, A. Hatamlou, Multi-Verse Optimizer: a nature-inspired algorithm for global optimization, Neural Comput. Appl. 27 (2) (Feb. 2016) 495–513, https://doi.org/10.1007/s00521-015-1870-7.K.P. Schneider, et al., Analytic considerations and design basis for the IEEE distribution test feeders, IEEE Trans. Power Syst. 33 (3) (May 2018) 3181–3188, https://doi.org/10.1109/TPWRS.2017.2760011.W. Kersting, Distribution System Distribution Syste Modeling and Analysis, CRC Press, New Mexico Boca, 2012.W.H. Kersting, Radial distribution test feeders, Trans. Power Syst. 6 (3) (1991) 975–985 [Online]. Available, http://ewh.ieee.org/soc/pes/dsacom/testfeeders.ht ml.Y. Gong, A. Guzm´ an, Distribution feeder fault location using IED and FCI information, in: 2011 64th Annu. Conf. Prot. Relay Eng, 2011, pp. 168–177, https://doi.org/10.1109/CPRE.2011.603561781110701234Artificial neural networksFault indicatorsFault locationMicrogridsPublication7d591bf6-cbc3-46ff-8937-50220a3b1ad7virtual::949-17d591bf6-cbc3-46ff-8937-50220a3b1ad7virtual::949-1https://scholar.google.com.co/citations?user=6uHcEpEAAAAJ&hl=esvirtual::949-10000-0002-9848-0094virtual::949-1ORIGINALFault indicators allocation to maximize the performance of a fault locator based on artificial intelligence.pdfFault indicators allocation to maximize the performance of a fault locator based on artificial intelligence.pdfapplication/pdf3040663https://repositorio.cuc.edu.co/bitstreams/59ad5ea2-ce46-4131-9e7e-d792112b80e2/downloada60d199b1d3129a9ee6b41675f2d2d8dMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/5e9d2c72-359c-4914-be63-623b8a5d0f99/download73a5432e0b76442b22b026844140d683MD52TEXTFault indicators allocation to maximize the performance of a fault locator based on artificial intelligence.pdf.txtFault indicators allocation to maximize the performance of a fault locator based on artificial intelligence.pdf.txtExtracted texttext/plain43239https://repositorio.cuc.edu.co/bitstreams/fe6b1063-5de3-4a8b-8800-6472c5f00944/downloadd0a495a462a0f79a48fe672eba8263aeMD53THUMBNAILFault indicators allocation to maximize the performance of a fault locator based on artificial intelligence.pdf.jpgFault indicators allocation to maximize the performance of a fault locator based on artificial intelligence.pdf.jpgGenerated Thumbnailimage/jpeg14806https://repositorio.cuc.edu.co/bitstreams/5ce7a413-590f-40ba-8b71-7574a57ccb6a/download9d740abad2ea2ae0cc7aec2d67aef5bcMD5411323/13998oai:repositorio.cuc.edu.co:11323/139982025-02-28 04:02:03.624https://creativecommons.org/licenses/by/4.0/© 2024 The Author(s)open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |