Diagnóstico de fallas en líneas de transmisión de energía eléctrica empleando métodos de inteligencia artificial

Carreño Pérez, Juan Carlos

Diagnóstico de fallas en líneas de transmisión de energía eléctrica empleando métodos de inteligencia artificial

dc.contributor.advisor	Espinel Ortega, Álvaro
dc.contributor.author	Carreño Pérez, Juan Carlos
dc.contributor.orcid	Espinel Ortega, Álvaro [0000-0002-7747-7718]
dc.creator.email	juan_carlos_carreno@hotmail.com
dc.creator.id	20221700001
dc.date.accessioned	2026-06-11T21:12:12Z
dc.date.available	2026-06-11T21:12:12Z
dc.date.created	2026-04-20
dc.description	La confiabilidad y continuidad del suministro de energía eléctrica constituyen elementos esenciales para el desarrollo económico y social, razón por la cual las redes eléctricas deben operar bajo altos estándares de seguridad y eficiencia. Estas redes desempeñan un papel fundamental al conectar los sistemas de generación con los usuarios finales mediante infraestructuras de transmisión, distribución y transformación de energía. Debido a su relevancia estratégica, cualquier interrupción en su funcionamiento puede generar impactos significativos tanto en los sectores productivos como en la calidad de vida de la población. En este contexto, el análisis de fallas en las líneas de transmisión adquiere una importancia crucial, ya que permite identificar de manera oportuna las causas de los eventos que afectan la estabilidad del sistema. La detección y clasificación adecuada de las fallas facilita la implementación de acciones correctivas rápidas, contribuyendo a reducir los tiempos de interrupción del servicio y los costos asociados a la recuperación de la operación normal. Asimismo, los sistemas de protección cumplen una función esencial al aislar los componentes afectados y evitar que las perturbaciones se propaguen a otras partes de la red. La vulnerabilidad de las líneas de transmisión frente a fenómenos atmosféricos y condiciones geográficas diversas incrementa la necesidad de desarrollar métodos avanzados de monitoreo y diagnóstico. Por ello, el fortalecimiento de las estrategias de protección y análisis de fallas resulta indispensable para garantizar sistemas eléctricos más seguros, resilientes y confiable
dc.description.abstract	The reliability and continuity of electricity supply are essential for economic and social development, which is why electrical grids must operate under high standards of safety and efficiency. These grids play a fundamental role in connecting generation systems with end users through energy transmission, distribution, and transformation infrastructure. Due to their strategic importance, any disruption in their operation can have significant impacts on both productive sectors and the population's quality of life. In this context, fault analysis in transmission lines becomes crucial, as it allows for the timely identification of the causes of events that affect system stability. The detection and proper classification of faults facilitates the implementation of rapid corrective actions, helping to reduce service interruption times and the costs associated with restoring normal operation. Likewise, protection systems play an essential role by isolating affected components and preventing disturbances from propagating to other parts of the grid. The vulnerability of transmission lines to atmospheric phenomena and diverse geographical conditions increases the need to develop advanced monitoring and diagnostic methods. Therefore, strengthening protection and failure analysis strategies is essential to ensuring safer, more resilient, and more reliable electrical systems
dc.format.mimetype	pdf
dc.identifier.uri	http://hdl.handle.net/11349/101753
dc.language.iso	spa
dc.publisher	Universidad Distrital Francisco Jose de Caldas
dc.relation.references	Afroz. (2025, February 8). https://www.kaggle.com/code/pythonafroz/fault-prediction-using-decision-tree-algorithm. Https://Www.Kaggle.Com/Code/Pythonafroz/Fault-Prediction-Using-Decision-Tree-Algorithm/Input.
dc.relation.references	Agudo, M. E., Young, W., Kasperek, B., & Thompson, S. I. (2000). End-to-end relay tests use secondary injection. IEEE Computer Applications in Power, 13(3), 32–37. https://doi.org/10.1109/67.849023
dc.relation.references	ALSTOM (Firm). (2011). Network protection & automation guide : protective relays, measurement & control. Alstom Grid.
dc.relation.references	Anderson, J. B., Anderson, ~ M, Eden, M., El-Hawary, M. E., Furui, S., Haddad, A. H., Herrick, R., Kartalopoulos, S., Kirk, D., Laplante, P., Padgett, M., Reeve, W. D., Zobrist, G., Moore, K., Larsen, S. L., Wollenberg, B. F., Zocholl, S. E., Fellow, I., B R … S K B (1999). Power System Protection.
dc.relation.references	Arranz, R., Paredes, Á., Rodríguez, A., & Muñoz, F. (2021). Fault location in Transmission System based on Transient Recovery Voltage using Stockwell transform and Artificial Neural Networks. Electric Power Systems Research, 201, 107569. https://doi.org/https://doi.org/10.1016/j.epsr.2021.107569
dc.relation.references	Associated Press. (2025). Chile imposes curfew after nationwide blackout leaves millions without power. AP News. https://apnews.com/
dc.relation.references	Baidyk, T., & Kussul, E. (2009). Redes neuronales, visión computacional y micromecánica (Itaca, Ed.; 1st ed., Vol. 1). ISBN: 9786072000216.
dc.relation.references	C37.111-2013 IEEE/IEC Measuring Relays and Protection Equipment, 011 (2013). https://ieeexplore.ieee.org/document/6512503
dc.relation.references	Carreño, J. C., Vega, A. M., & Espinel, A. (2022). Artificial Intelligence Methods to Solve Energy Transmission Problems Through Data Analysis from Different Data Sources. Communications in Computer and Information Science, 1685 CCIS, 122–136. https://doi.org/10.1007/978-3-031-20611-5_11
dc.relation.references	Carreño, J., Ladino, J., & Espinel, A. (2025). Performance of the Traveling Wave Fault Location Method in Electric Power Transmission Lines. International Review of Electrical Engineering (IREE), 20(1). https://www.praiseworthyprize.org/jsm/index.php?journal=iree&page=article&op=view&path%5B%5D=28716
dc.relation.references	Chai, E., Zeng, P. P., Ma, S., Xing, H., & Zhao, B. (2019). Artificial intelligence approaches to fault diagnosis in power grids: A review. Chinese Control Conference, CCC, 2019-July, 7346–7353. https://doi.org/10.23919/ChiCC.2019.8865533
dc.relation.references	Chakrabarti, S., Chakrabarti, S., & Swetapadma, A. (2018). A Review on Various Artificial Intelligence Techniques Used for Transmission Line Fault Location. Proceedings of the 3rd International Conference on Inventive Computation Technologies, ICICT 2018, 105–109. https://doi.org/10.1109/ICICT43934.2018.9034333
dc.relation.references	Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide. CRISP-DM.
dc.relation.references	Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
dc.relation.references	Chen, W., Feng, B., Tan, Z., Wu, N., & Song, F. (2022). Intelligent fault diagnosis framework of microgrid based on cloud–edge integration. Energy Reports, 8, 131–139. https://doi.org/https://doi.org/10.1016/j.egyr.2022.01.151
dc.relation.references	Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., & Stoddart, K. (2016). A review of cyber security risk assessment methods for SCADA systems. Computers & Security, 56, 1–27. https://doi.org/10.1016/J.COSE.2015.09.009
dc.relation.references	Chinmay Patil Kaushal Dave Pratik Hiralkar Rahul Katore Vaishnavi Bhalerao Tejas Bhoinwad, B., & Mankar, R. K. (2023). Detection and classification of transmission line faults using Stockwell transform and artificial neural network [Department of Electrical Engineering Shri Sant Gajanan Maharaj College of Engineering]. https://www.ssgmce.ac.in/uploads/UG_Projects/elpo/G6_RKM.pdf
dc.relation.references	Community Members XGBoost. (2025). XGBoost Python Package. https://xgboost.readthedocs.io/en/stable/tutorials/model.html
dc.relation.references	Community Pandas. (2025). pandas.get_dummies. Pandas.Pydata.Org. https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html#pandas.get_dummi
dc.relation.references	Community scikit-learn. (2025a). StandardScaler. Scikit-Learn.Org. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
dc.relation.references	Community scikit-learn. (2025b). SVC. Scikit-Learn.Org. https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
dc.relation.references	Consejo Nacional de Operación. (2019). Acuerdo CNO 1239. https://www.cno.org.co/
dc.relation.references	CREG 025-1995. (1995). Resolución CREG 025 de 1995. Código de Redes. http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/3a940408d14bf2e80525785a007a653b?OpenDocument
dc.relation.references	Doria-García, J., Orozco-Henao, C., Leborgne, R., Montoya, O. D., & Gil-González, W. (2021). High impedance fault modeling and location for transmission line✰. Electric Power Systems Research, 196, 107202. https://doi.org/10.1016/J.EPSR.2021.107202
dc.relation.references	El País. (2025). Apagón masivo en Chile: cuatro millones de hogares sin luz, militares en las calles y el Festival de Viña suspendido. https://elpais.com/chile/2025-02-26/masivo-apagon-en-chile-cuatro-millones-de-hogares-sin-luz-militares-en-las-calles-y-el-festival-de-vina-suspendido.html
dc.relation.references	Elmore, W. (2006). Protective Relaying Theory and Applications. 2.
dc.relation.references	Garcia, J., Molina, J., Berlanga, A., Patricio, M., Bustamante, A., & Padilla, W. (2018). Ciencia de datos. Técnicas Analíticas y Aprendizaje Estadístico. Bogotá, Colombia. Publicaciones Altaria, SL.
dc.relation.references	Grainger, J., & Stevenson, W. (1996). Power System Analysis.
dc.relation.references	Güvengir, G., Yalman, Y., & Byk, C. (2025). Power Quality Event Classification Using XGBoost and Deep Learning-Based Hybrid Models. Electric Power Systems Research, 229, 108047. https://doi.org/10.1016/j.epsr.2024.108047
dc.relation.references	Hamza, A., Ali, Z., Dudley, S., Saleem, K., Uneeb, M., & Christofides, N. (2025). A multi-stage review framework for AI-driven predictive maintenance and fault diagnosis in photovoltaic systems. Applied Energy, 393, 126108. https://doi.org/https://doi.org/10.1016/j.apenergy.2025.126108
dc.relation.references	Hsu, C.-C., Frusque, G., Forest, F., Macedo, F., Franck, C. M., & Fink, O. (2025). Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers. Reliability Engineering & System Safety, 263, 111199. https://doi.org/https://doi.org/10.1016/j.ress.2025.111199
dc.relation.references	Hurtado, J. (2000). Metodología de la Investigación Holística (1st ed., Vol. 1).
dc.relation.references	IEEE. (2014). IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines. IEEE. https://doi.org/10.1109/IEEESTD.2015.7024095
dc.relation.references	IEEE Std 602. (2007). IEEE Recommended Practice for Electric Systems in Health Care Facilities. IEEE Std 602-2007 (Revision of IEEE Std 602-1996), 1–436. https://doi.org/10.1109/IEEESTD.2007.4299432
dc.relation.references	IEEE Std C37.111. (1999). IEEE Standard Common Format for Transient Data Exchange (COMTRADE) for Power Systems. IEEE Std C37.111-1999, 1–55. https://doi.org/10.1109/IEEESTD.1999.90571
dc.relation.references	IEEE Std C37.114. (2015). IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines. IEEE Std C37.114-2014 (Revision of IEEE Std C37.114-2004), 1–76. https://doi.org/10.1109/IEEESTD.2015.7024095
dc.relation.references	Inmersis. (2023). Obtenido de Usos y ventajas de los gemelos digitales con realidad mixta en la educación (Inmersis - Grupo Gestionet, Ed.). https://inmersis.com/usos-y-ventajas-de-los-gemelos-digitales-con-realidad-mixta-en-la-educacion
dc.relation.references	Jadidi, S., Badihi, H., & Zhang, Y. (2020). Fault Diagnosis in Microgrids with Integration of Solar Photovoltaic Systems:A Review. IFAC-PapersOnLine, 53(2), 12091–12096. https://doi.org/https://doi.org/10.1016/j.ifacol.2020.12.763
dc.relation.references	Kezunovic, M., & Rikalo, I. (1996). Detect and classify faults using neural nets. IEEE Computer Applications in Power, 9(4), 42–47. https://doi.org/10.1109/67.539846
dc.relation.references	King, C. J. (1981). Sequence of Events Recording Systems. IEEE Transactions on Power Apparatus and Systems, PAS-100(9), 4250–4254. https://doi.org/10.1109/TPAS.1981.316976
dc.relation.references	Leija, E. B. H. (2014). Localización de fallas en líneas de transmisión. Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León.
dc.relation.references	Lundqvist, B. (2002). 100 years of relay protection, the Swedish ABB relay history.
dc.relation.references	Mahela, O. P., Sharma, V. D., Khan, B., Agarwal, S., & Alhelou, H. H. (2022). Chapter 31 - Identification and classification of faults using Stockwell transform and decision rule. In J. R.Vacca (Ed.), Smart Cities Policies and Financing (pp. 439–455). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-819130-9.00026-7
dc.relation.references	Mazinani, A., Davoli, L., Belli, L., & Ferrari, G. (2025). AI-enabled Early Faults and Anomalies Detection in Electric Inverters. IFAC-PapersOnLine, 59(9), 217–222. https://doi.org/https://doi.org/10.1016/j.ifacol.2025.08.139
dc.relation.references	Mian, T., & Kundu, P. (2026). A comprehensive review on generative artificial intelligence models for fault diagnostics and prognostics of rotating machineries. Engineering Applications of Artificial Intelligence, 166, 113594. https://doi.org/https://doi.org/10.1016/j.engappai.2025.113594
dc.relation.references	Mishra, M., & Singh, J. G. (2025). A comprehensive review on deep learning techniques in power system protection: Trends, challenges, applications and future directions. Results in Engineering, 25, 103884. https://doi.org/https://doi.org/10.1016/j.rineng.2024.103884
dc.relation.references	Mukherjee, A., Kundu, P. K., & Das, A. (2021). Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods. In Journal of The Institution of Engineers (India): Series B (Vol. 102, Number 4, pp. 855–877). Springer. https://doi.org/10.1007/s40031-020-00530-0
dc.relation.references	Nabamita Banerjee Roy; Kesab Bhattacharya. (2022). Application of signal processing tools and artificial neural network in diagnosis of power system faults (CRC Press, Ed.; First, Vol. 1). 2022.
dc.relation.references	PES, I. (2005). EMTP Reference Models for Transmission Line Relay Testing. In Ed. (Ed.), IEEE Power System Relaying Committee (pp. 16–33). IEEE.
dc.relation.references	Pouabe Eboule, P. S., & Hasan, A. N. (2021). Accurate fault detection and location in power transmission line using concurrent neuro fuzzy technique \| (Precyzyjne wykrywanie i k w k w ł w j w ł j h ki neuro-rozmytej). Przeglad Elektrotechniczny, 97(1), 37–45. https://doi.org/10.15199/48.2021.01.07
dc.relation.references	Raza, A., Benrabah, A., Alquthami, T., & Akmal, M. (2020). A review of fault diagnosing methods in power transmission systems. In Applied Sciences (Switzerland) (Vol. 10, Number 4). MDPI AG. https://doi.org/10.3390/app10041312
dc.relation.references	Resolución CREG 011 de 2009, 011 (2009).
dc.relation.references	Reuters. (2025a). Chile suffers nationwide power outage; government declares night curfew in affected regions. Reuters. https://www.reuters.com/world/americas/
dc.relation.references	Rodriguez, A. (2006). Sistemas SCADA (Union Europea, Vol. 1). ISBN: 84-267-1418-8.
dc.relation.references	Roy, N., & Bhattacharya, K. (2015). Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network. Electric Power Components and Systems, 43(4), 461–472. https://doi.org/10.1080/15325008.2014.986776
dc.relation.references	Sánchez, E., & Alanís, A. (2006). Redes Neuronales: Conceptos Fundamentales y Aplicaciones a Control Automático. Prentice Hall.
dc.relation.references	Schneider, E. (2021). A1 Fundamentals of Protection Practice Network Protection & Automation Guide. https://www.se.com/ww/en/tools/npag-full-online-unlocked-1130re14y/section.html#section1
dc.relation.references	Schweitzer, E. (2024). Schweitzer Engineering Laboratories. Https://Selinc.Com/Company/History/.
dc.relation.references	Siemens. (2024). Siemens Digital Twin: Virtual testing of SIPROTEC 5 protection devices in the cloud. https://www.siemens.com/global/en/products/energy/energy-automation-and-smart-grid/protection-relays-and-control/engineering-tools-for-protection/virtual-testing-of-siprotec-5-protection-devices-in-the-cloud-siprotect-digitaltwin.html
dc.relation.references	Siemens AG. (2017). SIPROTEC 5 for Every Application and Requirement SIPROTEC 5-Devices Protection, Automation and Monitoring Catalog SIPROTEC 5.01 · Edition 4. www.siemens.com/siprotec
dc.relation.references	Stockwell, R. G., Mansinha, L., & Lowe, R. P. (1996). Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 44(4), 998–1001. https://doi.org/10.1109/78.492555
dc.relation.references	Talei, A., Gharehpetian, G. B., & Mortazavi, S. A. (2023). Energy Efficiency Improvement in Smart Buildings Using Self-Organizing Maps (SOM). Energy and Buildings, 288, 112983. https://doi.org/10.1016/j.enbuild.2023.112983
dc.relation.references	The Guardian. (2025). Spain and Portugal blackout: how a technical glitch plunged the Iberian peninsula into darkness. The Guardian. https://www.theguardian.com/world/2025/apr/29/spain-and-portugal-blackout-how-a-technical-glitch-plunged-the-iberian-peninsula-into-darkness
dc.relation.references	Thukral, S., Mahela, O. P., & Kumar, B. (2019). Detection of Transmission Line Faults in the P W E P w G S U S kw ’ T 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 1, 1–6. https://doi.org/10.1109/ICICT46931.2019.8977695
dc.relation.references	Trivedi, R., & Khadem, S. (2022). Implementation of artificial intelligence techniques in microgrid control environment: Current progress and future scopes. Energy and AI, 8, 100147. https://doi.org/https://doi.org/10.1016/j.egyai.2022.100147
dc.relation.references	Turner, S. (2013). End-to-end testing transmission line protection schemes and double-ended fault locators. 2013 66th Annual Conference for Protective Relay Engineers, 365–376. https://doi.org/10.1109/CPRE.2013.6822051
dc.relation.references	Villegas Berbesi, T. (2012). Aplicación de técnicas robustas para detección y diagnóstico de fallos. https://www.educacion.gob.es/teseo/imprimirFicheroTesis.do?idFichero=Iswt9xQyzNY%3D
dc.relation.references	Xu, J., & Yue, H. (2020). Research on Fault Diagnosis Method of Power Grid Based on Artificial Intelligence. 2020 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2020, 113–116. https://doi.org/10.1109/TOCS50858.2020.9339711
dc.relation.references	Yadav, A., & Dash, Y. (2014). An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination. Advances in Artificial Neural Systems, 2014, 1–20. https://doi.org/10.1155/2014/230382
dc.relation.references	Yan, R., Geng, G., Zeng, G., & Jiang, Q. (2019). Single-Ended Fault Location for Hybrid Transmission Line using Embedded Artificial Intelligence. IEEE Power and Energy Society General Meeting, 2019-Augus. https://doi.org/10.1109/PESGM40551.2019.8973915
dc.relation.references	Yang, Q., Li, J., Le Blond, S., & Wang, C. (2016). Artificial Neural Network Based Fault Detection and Fault Location in the DC Microgrid. Energy Procedia, 103, 129–134. https://doi.org/https://doi.org/10.1016/j.egypro.2016.11.261
dc.relation.references	Zheng, L., Yan, P., & Chen, F. (2020). Power Grid Fault Diagnosis Method Based on Stacked Sparse Denoising Auto-Encoder and GRU Network. Journal of Physics: Conference Series, 1631(1). https://doi.org/10.1088/1742-6596/1631/1/012114
dc.relation.references	Zheng, Z., Yang, S., Guo, Y., Jin, X., & Wang, R. (2023). Meta-heuristic Techniques in Microgrid Management: A Survey. Swarm and Evolutionary Computation, 78, 101256. https://doi.org/https://doi.org/10.1016/j.swevo.2023.101256
dc.relation.references	Zhou, X., Wang, Q., Ye, J., Ren, B., Xu, L., & Zhou, N. (2026). AI-driven fault diagnosis strategy for reliable power transmission systems: A self-supervised learning perspective. Sustainable Energy, Grids and Networks, 46, 102180. https://doi.org/https://doi.org/10.1016/j.segan.2026.102180
dc.relation.references	Zimmerman, K., & Costello, D. (2010). Impedance-Based Fault Location Experience. Schweitzer Engineering Laboratories, Inc. https://www.academia.edu/8404488/Impedance_Based_Fault_Location_Experience
dc.rights.acceso	Abierto (Texto Completo)
dc.rights.accessrights	OpenAccess
dc.subject	Inteligencia artificial
dc.subject	Diagnóstico de fallas
dc.subject	Líneas de transmisión
dc.subject	Clasificación de fallas
dc.subject.keyword	Artificial intelligence
dc.subject.keyword	Fault diagnosis
dc.subject.keyword	Transmission lines
dc.subject.keyword	Fault classification
dc.subject.lemb	Doctorado en Ingeniería -- Tesis y disertaciones académicas
dc.subject.lemb	Sistemas eléctricos de potencia
dc.subject.lemb	Líneas de transmisión de energía eléctrica
dc.subject.lemb	Protección de sistemas eléctricos
dc.subject.lemb	Estabilidad del sistema eléctrico
dc.title	Diagnóstico de fallas en líneas de transmisión de energía eléctrica empleando métodos de inteligencia artificial
dc.title.titleenglish	Diagnosing faults in electrical power transmission lines using artificial intelligence methods
dc.type	doctoralThesis
dc.type.coar	http://purl.org/coar/resource_type/c_7a1f
dc.type.degree	Investigación-Innovación
dc.type.driver	info:eu-repo/semantics/bachelorThesis