Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementati...

Full description

Autores:
Marin-Quintero, J.
Orozco-Henao, C.
Bretas, A.S.
Velez, J.C.
Herrada, A.
Barranco-, Carlos A.
Percybrooks, W.S.
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:20.500.12585/12159
Acceso en línea:
https://hdl.handle.net/20.500.12585/12159
Palabra clave:
Overcurrent Protection;
Microgrid;
Fault Current Limiters
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UTB2_be4d0158d218e5cf71db3d92428f4dd3
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/12159
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
title Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
spellingShingle Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
Overcurrent Protection;
Microgrid;
Fault Current Limiters
LEMB
title_short Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
title_full Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
title_fullStr Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
title_full_unstemmed Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
title_sort Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids
dc.creator.fl_str_mv Marin-Quintero, J.
Orozco-Henao, C.
Bretas, A.S.
Velez, J.C.
Herrada, A.
Barranco-, Carlos A.
Percybrooks, W.S.
dc.contributor.author.none.fl_str_mv Marin-Quintero, J.
Orozco-Henao, C.
Bretas, A.S.
Velez, J.C.
Herrada, A.
Barranco-, Carlos A.
Percybrooks, W.S.
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 Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications. © 2013 State Grid Electric Power Research Institute.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-07-19T12:56:17Z
dc.date.available.none.fl_str_mv 2023-07-19T12:56:17Z
dc.date.submitted.none.fl_str_mv 2023
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/draft
dc.type.spa.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
status_str draft
dc.identifier.citation.spa.fl_str_mv J. Marín-Quintero et al., "Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids," in Journal of Modern Power Systems and Clean Energy, vol. 10, no. 6, pp. 1648-1657, November 2022, doi: 10.35833/MPCE.2021.000444.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12159
dc.identifier.doi.none.fl_str_mv 10.35833/MPCE.2021.000444
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv J. Marín-Quintero et al., "Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids," in Journal of Modern Power Systems and Clean Energy, vol. 10, no. 6, pp. 1648-1657, November 2022, doi: 10.35833/MPCE.2021.000444.
10.35833/MPCE.2021.000444
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12159
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 9 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Journal of Modern Power Systems and Clean Energy
institution Universidad Tecnológica de Bolívar
bitstream.url.fl_str_mv https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/2/license_rdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/3/license.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/1/Scopus%20-%20Document%20details%20-%20Adaptive%20Fault%20Detection%20Based%20on%20Neural%20Networks%20and%20Multiple%20Sampling%20Points%20for%20Distribution%20Networks%20and%20Microgrids.pdf
https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/4/Scopus%20-%20Document%20details%20-%20Adaptive%20Fault%20Detection%20Based%20on%20Neural%20Networks%20and%20Multiple%20Sampling%20Points%20for%20Distribution%20Networks%20and%20Microgrids.pdf.txt
https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/5/Scopus%20-%20Document%20details%20-%20Adaptive%20Fault%20Detection%20Based%20on%20Neural%20Networks%20and%20Multiple%20Sampling%20Points%20for%20Distribution%20Networks%20and%20Microgrids.pdf.jpg
bitstream.checksum.fl_str_mv 4460e5956bc1d1639be9ae6146a50347
e20ad307a1c5f3f25af9304a7a7c86b6
a9257b2bc5120140fd40c78d338134a4
20126728cf8cd0de88bd53430b7a2982
785f1cd70635426d25a4eeb1a18d197b
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UTB
repository.mail.fl_str_mv repositorioutb@utb.edu.co
_version_ 1814021690898776064
spelling Marin-Quintero, J.cee25f99-83ee-4fc2-8235-fec1fec18d98Orozco-Henao, C.f3b2ff13-484c-4dac-bcb1-758cc0fd7af0Bretas, A.S.91053dea-1312-457c-ab6d-0e84a4504e71Velez, J.C.d23f6ec7-7faa-468e-9366-0916c0f485f5Herrada, A.e95df0d4-7119-470c-a658-1ad742e34d81Barranco-, Carlos A.b40f260a-6c43-426c-9b8f-6bc5d0c17509Percybrooks, W.S.27c01209-2900-4575-9fdc-93278dc75e172023-07-19T12:56:17Z2023-07-19T12:56:17Z20222023J. Marín-Quintero et al., "Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids," in Journal of Modern Power Systems and Clean Energy, vol. 10, no. 6, pp. 1648-1657, November 2022, doi: 10.35833/MPCE.2021.000444.https://hdl.handle.net/20.500.12585/1215910.35833/MPCE.2021.000444Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSmart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications. © 2013 State Grid Electric Power Research Institute.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_abf2Journal of Modern Power Systems and Clean EnergyAdaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgridsinfo: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 IndiasChoi, M.-G., Ahn, S.-J., Choi, J.-H., Cho, S.-M., Yun, S.-Y. Adaptive Protection Method of Distribution Networks Using the Sensitivity Analysis for Changed Network Topologies Based on Base Network Topology (2020) IEEE Access, 8, art. no. 9163368, pp. 148169-148180. Cited 11 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2020.3015517Chowdhury, 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; 978-184919014-5Hatziargyriou, N. Microgrids: Architectures and Control (2013) Microgrids: Architectures and Control, pp. 1-317. Cited 550 times. http://onlinelibrary.wiley.com/book/10.1002/9781118720677 ISBN: 978-111872067-7; 978-111872068-4 doi: 10.1002/9781118720677Mariam, L., Basu, M., Conlon, M.F. Microgrid: Architecture, policy and future trends (2016) Renewable and Sustainable Energy Reviews, 64, pp. 477-489. Cited 169 times. https://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews doi: 10.1016/j.rser.2016.06.037Monti, A., Milano, F., Bompard, E., Guillaud, X. Converter-Based Dynamics and Control of Modern Power Systems (2020) Converter-Based Dynamics and Control of Modern Power Systems, pp. 1-361. Cited 8 times. https://www.elsevier.com/books/converter-based-dynamics-and-control-of-modern-power-systems/monti/978-0-12-818491-2 ISBN: 978-012818491-2 doi: 10.1016/B978-0-12-818491-2.09991-0Gopalan, S.A., Sreeram, V., Iu, H.H.C. A review of coordination strategies and protection schemes for microgrids (2014) Renewable and Sustainable Energy Reviews, 32, pp. 222-228. Cited 119 times. doi: 10.1016/j.rser.2014.01.037Ustun, T.S., Ozansoy, C., Zayegh, A. Modeling of a centralized microgrid protection system and distributed energy resources according to IEC 61850-7-420 (2012) IEEE Transactions on Power Systems, 27 (3), art. no. 6153416, pp. 1560-1567. Cited 263 times. doi: 10.1109/TPWRS.2012.2185072Memon, A.A., Kauhaniemi, K. An adaptive protection for radial AC microgrid using IEC 61850 communication standard: Algorithm proposal using offline simulations (2020) Energies, 13 (20), art. no. 5316. Cited 16 times. https://www.mdpi.com/1996-1073/13/20/5316 doi: 10.3390/en13205316Senarathna, T.S.S., Udayanga Hemapala, K.T.M. Review of adaptive protection methods for microgrids (Open Access) (2019) AIMS Energy, 7 (5), pp. 557-578. Cited 25 times. https://www.aimspress.com/fileOther/PDF/energy/energy-07-05-557.pdf doi: 10.3934/energy.2019.5.557Mahat, P., Chen, Z., Bak-Jensen, B., Bak, C.L. A simple adaptive overcurrent protection of distribution systems with distributed generation (2011) IEEE Transactions on Smart Grid, 2 (3), art. no. 5871328, pp. 428-437. Cited 380 times. doi: 10.1109/TSG.2011.2149550Yu, J.J.Q., Hou, Y., Lam, A.Y.S., Li, V.O.K. Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks (2019) IEEE Transactions on Smart Grid, 10 (2), art. no. 8118194, pp. 1694-1703. Cited 218 times. doi: 10.1109/TSG.2017.2776310Fahim, S.R., Sarker, S.K., Muyeen, S.M., Das, S.K., Kamwa, I. A deep learning based intelligent approach in detection and classification of transmission line faults (2021) International Journal of Electrical Power and Energy Systems, 133, art. no. 107102. Cited 38 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2021.107102Draz, A., Elkholy, M.M., El-Fergany, A.A. Soft Computing Methods for Attaining the Protective Device Coordination Including Renewable Energies: Review and Prospective (2021) Archives of Computational Methods in Engineering, 28 (7), pp. 4383-4404. Cited 10 times. http://www.springerlink.com/content/1134-3060 doi: 10.1007/s11831-021-09534-5Marín-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. Cited 11 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2021.106960Cepeda, C., Orozco-Henao, C., Percybrooks, W., Pulgarín-Rivera, J.D., Montoya, O.D., Gil-González, W., Vélez, J.C. Intelligent fault detection system for microgrids (2020) Energies, 13 (5), art. no. 1223. Cited 27 times. https://www.mdpi.com/1996-1073/13/5/1223 doi: 10.3390/en13051223Kar, S., Samantaray, S.R., Zadeh, M.D. Data-Mining Model Based Intelligent Differential Microgrid Protection Scheme (2017) IEEE Systems Journal, 11 (2), pp. 1161-1169. Cited 186 times. http://www.ieee.org/products/onlinepubs/news/0806_01.html doi: 10.1109/JSYST.2014.2380432Saleh, K., Ayad, A. Fault zone identification and phase selection for microgrids using decision trees ensemble (2021) International Journal of Electrical Power and Energy Systems, 132, art. no. 107178. Cited 9 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2021.107178Mishra, D.P., Samantaray, S.R., Joos, G. A combined wavelet and data-mining based intelligent protection scheme for microgrid (2016) IEEE Transactions on Smart Grid, 7 (5), art. no. 7307183, pp. 2295-2304. Cited 259 times. doi: 10.1109/TSG.2015.2487501Patnaik, B., Mishra, M., Bansal, R.C., Jena, R.K. MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid (Open Access) (2021) Applied Energy, 285, art. no. 116457. Cited 39 times. https://www.journals.elsevier.com/applied-energy doi: 10.1016/j.apenergy.2021.116457NaitMalek, Y., Najib, M., Bakhouya, M., Essaaidi, M. Embedded Real-time Battery State-of-Charge Forecasting in Micro-Grid Systems (Open Access) (2021) Ecological Complexity, 45, art. no. 100903. Cited 5 times. http://www.elsevier.com/wps/find/journaldescription.cws_home/701873/description#description doi: 10.1016/j.ecocom.2020.100903Principi, E., Rossetti, D., Squartini, S., Piazza, F. Unsupervised electric motor fault detection by using deep autoencoders (Open Access) (2019) IEEE/CAA Journal of Automatica Sinica, 6 (2), art. no. 8651897, pp. 441-451. Cited 114 times. https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER284-EPC doi: 10.1109/JAS.2019.1911393Li, H., Hu, G., Li, J., Zhou, M. Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests (2022) IEEE Transactions on Automation Science and Engineering, 19 (2), pp. 1109-1119. Cited 27 times. http://www.ieee.org/t-ase doi: 10.1109/TASE.2020.3048056Coffele, F., Booth, C., Dyśko, A., Burt, G. Quantitative analysis of network protection blinding for systems incorporating distributed generation (Open Access) (2012) IET Generation, Transmission and Distribution, 6 (12), pp. 1218-1224. Cited 34 times. doi: 10.1049/iet-gtd.2012.0381Hosseini, S.A., Abyaneh, H.A., Sadeghi, S.H.H., Razavi, F., Nasiri, A. An overview of microgrid protection methods and the factors involved (2016) Renewable and Sustainable Energy Reviews, 64, pp. 174-186. Cited 133 times. https://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews doi: 10.1016/j.rser.2016.05.089Zheng, K.-H., Xia, M.-C. Impacts of microgrid on protection of distribution networks and protection strategy of microgrid (2011) APAP 2011 - Proceedings: 2011 International Conference on Advanced Power System Automation and Protection, 1, art. no. 6180426, pp. 356-359. Cited 19 times. ISBN: 978-142449619-8 doi: 10.1109/APAP.2011.6180426Mumtaz, F., Bayram, I.S. Planning, Operation, and Protection of Microgrids: An Overview (2017) Energy Procedia, 107, pp. 94-100. Cited 58 times. http://www.sciencedirect.com/science/journal/18766102 doi: 10.1016/j.egypro.2016.12.137Zhai, H.F., Yang, M., Chen, B., Kang, N. Dynamic reconfiguration of three-phase unbalanced distribution networks (2018) International Journal of Electrical Power and Energy Systems, 99, pp. 1-10. Cited 68 times. doi: 10.1016/j.ijepes.2017.12.027Bhattacharya, S.K., Goswami, S.K. Distribution network reconfiguration considering protection coordination constraints (2008) Electric Power Components and Systems, 36 (11), pp. 1150-1165. Cited 20 times. doi: 10.1080/15325000802084463Aminifar, F., Teimourzadeh, S., Shahsavari, A., Savaghebi, M., Golsorkhi, M.S. Machine learning for protection of distribution networks and power electronics-interfaced systems (2021) Electricity Journal, 34 (1), art. no. 106886. Cited 14 times. http://www.electricity-online.com doi: 10.1016/j.tej.2020.106886Khalid, H., Shobole, A. Existing Developments in Adaptive Smart Grid Protection: A Review (2021) Electric Power Systems Research, 191, art. no. 106901. Cited 24 times. https://www.journals.elsevier.com/electric-power-systems-research doi: 10.1016/j.epsr.2020.106901Alonso, M., Amaris, H., Alcala, D., Florez, D.M.R. Smart sensors for smart grid reliability (2020) Sensors (Switzerland), 20 (8), art. no. 2187. Cited 25 times. https://www.mdpi.com/1424-8220/20/8/2187/pdf doi: 10.3390/s20082187Shahin, M.A., Maier, H.R., Jaksa, M.B. Data division for developing neural networks applied to geotechnical engineering (Open Access) (2004) Journal of Computing in Civil Engineering, 18 (2), pp. 105-114. Cited 289 times. doi: 10.1061/(ASCE)0887-3801(2004)18:2(105)Bishop, C.M. (2006) Pattern Recognition and Machine Learning. Cited 33833 times. New York: SpringerOrozco-Henao, C., Marin-Quintero, J., Castillo-Sierra, R., Velez, J.C., Oliveros, I., Pardo, M. Active Distribution Networks Laboratory: A Case of Experiments in Power Quality (2019) 2019 IEEE Workshop on Power Electronics and Power Quality Applications, PEPQA 2019 - Proceedings, art. no. 8851535. Cited 2 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8844570 ISBN: 978-172811626-6 doi: 10.1109/PEPQA.2019.8851535Kersting, W.H. Distribution system modeling and analysis: Fourth edition (Open Access) (2017) Distribution System Modeling and Analysis: Fourth Edition, pp. 1-526. Cited 819 times. http://www.tandfebooks.com/doi/book/10.1201/9781315120782 ISBN: 978-149877214-3; 978-149877213-6 doi: 10.1201/9781315120782Pérez-Londoño, Garcés, S., Bueno-López, A. (2020) Components modelling in AC microgrids. Cited 4 times. Jan. https://doi.org/https://doi.org/10.22517/97895(2013) User Manual Powerfactory v15.0. Cited 2 times. DIgSILENT GmbH, GermanyCastro, L., López, M.F.B., Ocampo, M.Á.R. (2021) Control jerárquico en micro-redes AC Jun. https://hdl.handle.net/11059/13701http://purl.org/coar/resource_type/c_6501CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53ORIGINALScopus - Document details - Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids.pdfScopus - Document details - Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids.pdfapplication/pdf210614https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/1/Scopus%20-%20Document%20details%20-%20Adaptive%20Fault%20Detection%20Based%20on%20Neural%20Networks%20and%20Multiple%20Sampling%20Points%20for%20Distribution%20Networks%20and%20Microgrids.pdfa9257b2bc5120140fd40c78d338134a4MD51TEXTScopus - Document details - Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids.pdf.txtScopus - Document details - Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids.pdf.txtExtracted texttext/plain3228https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/4/Scopus%20-%20Document%20details%20-%20Adaptive%20Fault%20Detection%20Based%20on%20Neural%20Networks%20and%20Multiple%20Sampling%20Points%20for%20Distribution%20Networks%20and%20Microgrids.pdf.txt20126728cf8cd0de88bd53430b7a2982MD54THUMBNAILScopus - Document details - Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids.pdf.jpgScopus - Document details - Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids.pdf.jpgGenerated Thumbnailimage/jpeg7127https://repositorio.utb.edu.co/bitstream/20.500.12585/12159/5/Scopus%20-%20Document%20details%20-%20Adaptive%20Fault%20Detection%20Based%20on%20Neural%20Networks%20and%20Multiple%20Sampling%20Points%20for%20Distribution%20Networks%20and%20Microgrids.pdf.jpg785f1cd70635426d25a4eeb1a18d197bMD5520.500.12585/12159oai:repositorio.utb.edu.co:20.500.12585/121592023-07-20 00:17:51.394Repositorio Institucional UTBrepositorioutb@utb.edu.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

Publicaciones similares