Mostrar el registro sencillo del documento

Degradación térmica de los sistemas de aislamiento de transformadores

dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributor.advisorCano Plata, Eduardo Antonio
dc.contributor.advisorUstariz Farfan, Armando Jaime
dc.contributor.authorSoto Marín, Oscar Julián
dc.date.accessioned2023-01-24T14:14:08Z
dc.date.available2023-01-24T14:14:08Z
dc.date.issued2022
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/83084
dc.descriptionfotografías, graficas, ilustraciones, tablas
dc.description.abstractEste trabajo de investigación está enfocado al diagnóstico de los sistemas de aislamiento de los transformadores. Para este propósito, es necesario primero determinar la ley de envejecimiento de activo, el cual está centrado en la identificación de los factores o causas que originan la degradación del transformador. Dentro de este mismo proceso es necesario identificar el efecto sobre el transformador (reducción del nivel de aislamiento, disminución de la capacidad de refrigeración, pérdida de eficiencia, etc.), esto es el modo de falla. Con la comprensión e identificación plena de los factores presentes en el proceso del desarrollo de la falla, es posible medir los parámetros más apropiados para realizar el diagnóstico preciso del sistema del transformador que está presentando la falla, esto permitirá determinar las mejores prácticas en la gestión eficiente del transformador. La tesis propone el desarrollo de métodos de diagnósticos basados en los resultados de ensayos de envejecimiento acelerado realizados a los transformadores en aceite, transformadores tipo seco y transformadores de mediana frecuencia, fundamentado en la teoría de la cinética química. El realizar el estudio sobre diferentes tipos de aislamientos, permite realizar analogías de los procesos de degradación y diagnósticos; igualmente, comparar la ley de envejecimiento de cada tipo de transformador. (Texto tomado de la fuente)
dc.description.abstractThis research work is focused on the diagnosis of transformer insulation systems. For this purpose, it is first necessary to determine the asset aging law, which is focused on the identification of the factors or causes that cause deterioration of the transformer. Within this same process, it is necessary to identify the effect on the transformer (reduced insulation level, decreased cooling capacity, loss of efficiency, etc.), that is, the failure mode. With the full understanding and identification of the factors present in the fault development process, it is possible to measure the most appropriate parameters to perform an accurate diagnosis of the transformer system that is presenting the fault, this will allow to determine the best practices in the efficient management of the transformer. The thesis proposes the development of diagnostic methods based on the results of accelerated aging tests carried out on oil-filled transformers, dry-type transformers and medium-frequency transformers, based on the theory of chemical kinetics. Carrying out the study on different types of insulation allows analogies to be made of the degradation and diagnostic processes; similarly, compare the law of aging of each type of transformer.
dc.format.extentxviii, 145 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
dc.titleDegradación térmica de los sistemas de aislamiento de transformadores
dc.typeTrabajo de grado - Doctorado
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática
dc.contributor.researchgroupRedes de Distribución y Potencia Gredyp
dc.description.degreelevelDoctorado
dc.description.degreenameDoctor en Ingeniería - Ingeniería Automática
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.facultyFacultad de Ingeniería y Arquitectura
dc.publisher.placeManizales, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizales
dc.relation.references[1]. Huang, A. Q. (2016). Medium-voltage solid-state transformer: Technology for a smarter and resilient grid. IEEE Industrial Electronics Magazine, 10(3), 29-42.
dc.relation.references[2]. Huber, J. E., & Kolar, J. W. (2016). Solid-state transformers: On the origins and evolution of key concepts. IEEE Industrial Electronics Magazine, 10(3), 19-28.
dc.relation.references[3]. Bignucolo, F., Bertoluzzo, M., & Fontana, C. (2015, October). Applications of the solid-state transformer concept in the electrical power system. In 2015 AEIT International Annual Conference (AEIT) (pp. 1-6). IEEE.
dc.relation.references[4]. Hannan, M. A., Ker, P. J., Lipu, M. S. H., Choi, Z. H., Rahman, M. S. A., Muttaqi, K. M., & Blaabjerg, F. (2020). State of the art of solid-state transformers: Advanced topologies, implementation issues, recent progress and improvements. Ieee Access, 8, 19113-19132.
dc.relation.references[5]. Shamshuddin, M. A., Rojas, F., Cardenas, R., Pereda, J., Diaz, M., & Kennel, R. (2020). Solid state transformers: Concepts, classification, and control. Energies, 13(9), 2319.
dc.relation.references[6]. Franklin, A. C., & Franklin, D. P. (2016). The J & P transformer book: a practical technology of the power transformer. Elsevier.
dc.relation.references[7]. Tang, W. H., & Wu, Q. H. (2011). Condition monitoring and assessment of power transformers using computational intelligence. Springer Science & Business Media.
dc.relation.references[8]. A. Abu-Siada, Power Transformer Condition Monitoring and Diagnosis, The Institution of Engineering and Technology, London, United Kingdom, 2018.
dc.relation.references[9]. Vahidi, B., & Teymouri, A. (2019). Quality confirmation tests for power transformer insulation systems. Springer International Publishing.
dc.relation.references[10]. Chakravorti, S., Dey, D., & Chatterjee, B. (2013). Recent trends in the condition monitoring of transformers. Power Systems Springer-Verlag: London, UK.
dc.relation.references[11]. Saha, T. K., & Purkait, P. (Eds.). (2017). Transformer ageing: monitoring and estimation techniques. John Wiley & Sons.
dc.relation.references[12]. Reclamation, B. (2005). Transformers: Basics, maintenance and diagnostics. US Department of the Interior Bureau of Reclamation. Denver, Colorado, USA
dc.relation.references[13]. Smith J.M., Van Ness, H.C., Abbott, M.M., (2001). Introduction to chemical engineering thermodynamics, McGraw-Hill Education.
dc.relation.references[14]. Dahm, K. D., & Visco, D. P. (2014). Fundamentals of Chemical Engineering Thermodynamics. Cengage Learning.
dc.relation.references[15]. Koretsky, M. D. (2012). Engineering and chemical thermodynamics. John Wiley & Sons.
dc.relation.references[16]. Halstead, W. D. (1973). A thermodynamic assessment of the formation of gaseous hydrocarbons in faulty transformers. Journal of the Institute of Petroleum, 59(569), 239-41.
dc.relation.references[17]. Transformers Committee. (2008). Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers; IEEE Std C57. 104. IEEE: New York, NY, USA.
dc.relation.references[18]. International Electrotechnical Commission. (1999). Mineral oil-impregnated electrical equipment in service—guide to the interpretation of dissolved and free gases analysis. International Standard IEC, 60599.
dc.relation.references[19]. Shirai, M., Shimoji, S., & Ishii, T. (1977). Thermodynamic study on the thermal decomposition of insulating oil. IEEE Transactions on Electrical Insulation, (4), 272-280.
dc.relation.references[20]. Dornenburg, E., & Strittmatter, W. (1974). Monitoring oil-cooled transformers by gas-analysis. Brown Boveri Review, 61(5), 238-247.
dc.relation.references[21]. Rogers, R. R. (1978). IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis. IEEE transactions on electrical insulation, (5), 349-354.
dc.relation.references[22]. Sun, H. C., Huang, Y. C., & Huang, C. M. (2012). A review of dissolved gas analysis in power transformers. Energy Procedia, 14, 1220-1225.
dc.relation.references[23]. Faiz, J., & Soleimani, M. (2017). Dissolved gas analysis evaluation in electric power transformers using conventional methods a review. IEEE Transactions on Dielectrics and Electrical Insulation, 24(2), 1239-1248.
dc.relation.references[24]. Duval, M. (1989). Dissolved gas analysis: It can save your transformer. IEEE electrical insulation magazine, 5(6), 22-27.
dc.relation.references[25]. Duval, M. (2008). The Duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers. IEEE Electrical Insulation Magazine, 24(6), 22-29.
dc.relation.references[26]. Duval, M. (2002). A review of faults detectable by gas-in-oil analysis in transformers. IEEE electrical Insulation magazine, 18(3), 8-17.
dc.relation.references[27]. Duval, M., & DePabla, A. (2001). Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases. IEEE Electrical Insulation Magazine, 17(2), 31-41.
dc.relation.references[28]. Duval, M., Hoehlein, I., Scatiggio, F., Cyr, M., Grisaru, M., Frotscher, R., ... & Westlin, L. (2010). DGA in Non-Mineral Oils and Load Tap Changers and Improved DGA Diagnosis Criteria. CIGRE brochure, 443.
dc.relation.references[29]. Lowe, R. I. (1985). Artificial intelligence techniques applied to transformer oil dissolved gas analysis. In Proceedings of the 1985 International Conference of Doble Clients. Boston, Massachusetts.
dc.relation.references[30]. Dukarm, J. J. (1993). Transformer oil diagnosis using fuzzy logic and neural networks. In Proceedings of Canadian Conference on Electrical and Computer Engineering (pp. 329-332). IEEE.
dc.relation.references[31]. Mohammad, G., Sahar, S. S., & Mohammad, A. V. (2011). Artificial neural networks applied to DGA for fault diagnosis in oil-filled power transformers. Journal of Electrical and Electronics Engineering Research, 3(1), 1-10.
dc.relation.references[32]. Patekar, K. D., & Chaudhry, B. (2019, November). DGA analysis of transformer using Artificial neutral network to improve reliability in Power Transformers. In 2019 IEEE 4th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) (pp. 1-5). IEEE.
dc.relation.references[33]. Flores, W. C., Mombello, E. E., Jardini, J. A., Rattá, G., & Corvo, A. M. (2011). Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems. Expert Systems with Applications, 38(7), 8119-8127.
dc.relation.references[34]. Khan, S. A., Equbal, M. D., & Islam, T. (2015). A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models. IEEE Transactions on Dielectrics and Electrical Insulation, 22(1), 590-596.
dc.relation.references[35]. Bakar, N. A., & Abu-Siada, A. (2016). Fuzzy logic approach for transformer remnant life prediction and asset management decision. IEEE Transactions on Dielectrics and Electrical Insulation, 23(5), 3199-3208.
dc.relation.references[36]. Hossam-Eldin, A. A., Refaey, M., & Ramadan, H. (2017, December). New approach to power transformer asset management and life assessment using fuzzy logic techniques. In 2017 Nineteenth International Middle East power Systems Conference (MEPCON) (pp. 901-908). IEEE.
dc.relation.references[37]. Arshad, M., Islam, S. M., & Khaliq, A. (2014). Fuzzy logic approach in power transformers management and decision making. IEEE Transactions on Dielectrics and Electrical Insulation, 21(5), 2343-2354.
dc.relation.references[38]. Malik, H., & Mishra, S. (2016). Application of gene expression programming (GEP) in power transformers fault diagnosis using DGA. IEEE Transactions on Industry Applications, 52(6), 4556-4565.
dc.relation.references[39]. Li, J., Zhang, Q., Wang, K., Wang, J., Zhou, T., & Zhang, Y. (2016). Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Transactions on Dielectrics and Electrical Insulation, 23(2), 1198-1206.
dc.relation.references[40]. Zhang, Y., Li, X., Zheng, H., Yao, H., Liu, J., Zhang, C., ... & Jiao, J. (2019). A fault diagnosis model of power transformers based on dissolved gas analysis features selection and improved krill herd algorithm optimized support vector machine. IEEE Access, 7, 102803-102811.
dc.relation.references[41]. Shintemirov, A., Tang, W., & Wu, Q. H. (2008). Power transformer fault classification based on dissolved gas analysis by implementing bootstrap and genetic programming. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 39(1), 69-79
dc.relation.references[42]. Duval, M., & Lamarre, L. (2014). The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers. IEEE Electrical Insulation Magazine, 30(6), 9-12.
dc.relation.references[43]. Marques, A., de Moura, N. K., Dias, Y. A., de Jesus Ribeiro, C., Rocha, A. S., da Cunha Brito, L., ... & dos Santos, J. A. L. (2017). Method for the evaluation and classification of power transformer insulating oil based on physicochemical analyses. IEEE Electrical Insulation Magazine, 33(1), 39-49.
dc.relation.references[44]. Zeinoddini‐Meymand, H., & Vahidi, B. (2016). Health index calculation for power transformers using technical and economical parameters. IET Science, Measurement & Technology, 10(7), 823-830.
dc.relation.references[45]. Jakob, F., Noble, P., & Dukarm, J. J. (2011). A thermodynamic approach to evaluation of the severity of transformer faults. IEEE Transactions on Power Delivery, 27(2), 554-559.
dc.relation.references[46]. Mharakurwa, E. T., Nyakoe, G. N., & Akumu, A. O. (2019). Power transformer fault severity estimation based on dissolved gas analysis and energy of fault formation technique. Journal of Electrical and Computer Engineering, 2019.
dc.relation.references[47]. Cruz, V. G., Costa, A. L., & Paredes, M. L. (2015). Development and evaluation of a new DGA diagnostic method based on thermodynamics fundamentals. IEEE Transactions on Dielectrics and Electrical Insulation, 22(2), 888-894.
dc.relation.references[48]. Dukarm, J., & Jakob, F. (2016, May). Thermodynamic estimation of transformer fault severity. In 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) (pp. 1-1). IEEE.
dc.relation.references[49]. Ghoneim, S. S. (2018). Intelligent prediction of transformer faults and severities based on dissolved gas analysis integrated with thermodynamics theory. IET Science, Measurement & Technology, 12(3), 388-394.
dc.relation.references[50]. Cruz, V. G. M., Costa, A. L., & Paredes, M. L. (2015). Simulation of thermal decomposition of mineral insulating oil. Brazilian Journal of Chemical Engineering, 32(3), 781-794.
dc.relation.references[51]. Marques, A.P., Moura, N.K., Dias, Y.A., Ribeiro, C.D., Rocha, A.S., Brito, L.D., Azevedo, C.H., & Santos, J.A. (2017). Method for the evaluation and classification of power transformer insulating oil based on physicochemical analyses. IEEE Electrical Insulation Magazine, 33, 39-49.
dc.relation.references[52]. Mcmurray, W. (1970). U.S. Patent No. 3,517,300. Washington, DC: U.S. Patent and Trademark Office
dc.relation.references[53]. Brooks, J. L. (1980). Solid State Transformer Concept Development. CIVIL ENGINEERING LAB (NAVY) PORT HUENEME CA.
dc.relation.references[54]. Reischl, P. (1995). Proof of the principle of the solid-state transformer and the AC/AC switch mode regulator. Electric Power Research Institute, Palo Alto, CA.
dc.relation.references[55]. Kang, M., Enjeti, P. N., & Pitel, I. J. (1999). Analysis and design of electronic transformers for electric power distribution system. IEEE Transactions on Power Electronics, 14(6), 1133-1141.
dc.relation.references[56]. Pena-Alzola, R., Gohil, G., Mathe, L., Liserre, M., & Blaabjerg, F. (2013, September). Review of modular power converters solutions for smart transformer in distribution system. In 2013 IEEE Energy Conversion Congress and Exposition (pp. 380-387). IEEE.
dc.relation.references[57]. Kolar, J. W., & Ortiz, G. (2014, May). Solid-state-transformers: Key components of future traction and smart grid systems. In Proceedings of the International Power Electronics Conference-ECCE Asia (IPEC 2014) (pp. 18-21). IEEE.
dc.relation.references[58]. Zhao, C., Dujic, D., Mester, A., Steinke, J. K., Weiss, M., Lewdeni-Schmid, S., ... & Stefanutti, P. (2013). Power electronic traction transformer—Medium voltage prototype. IEEE Transactions on Industrial Electronics, 61(7), 3257-3268.
dc.relation.references[59]. Raju, R. N., Zhang, R. S., Stevanovic, L. D., Slotnick, J. N., Steigerwald, R. L., & Garces, L. J. (2014). U.S. Patent No. 8,644,037. Washington, DC: U.S. Patent and Trademark Office.
dc.relation.references[60]. Grider, D., Das, M., Agarwal, A., Palmour, J., Leslie, S., Ostop, J., ... & Hefner, A. (2011, April). 10 kV/120 A SiC DMOSFET half H-bridge power modules for 1 MVA solid state power substation. In 2011 IEEE Electric Ship Technologies Symposium (pp. 131-134). IEEE.
dc.relation.references[61]. Tabatabaian, M., Rajput, R. K. (2018). Advanced Thermodynamics: Fundamentals, Mathematics and Applications, Mercury Learning and information.
dc.relation.references[62]. Cengel, Y. A., Boles, M. A., & Kanoğlu, M. (2011). Thermodynamics: an engineering approach. New York: McGraw-hill.
dc.relation.references[63]. Atkins, P., & De Paula, J. (2011). Physical chemistry for the life sciences. Oxford University Press, USA.
dc.relation.references[64]. Mortimer, R. G. (2000). Physical chemistry. Academic Press.
dc.relation.references[65]. Bruice, P. Y. (2017). Organic chemistry. Pearson.
dc.relation.references[66]. Poonnoy, N., Suwanasri, C., & Suwanasri, T. (2020). Fuzzy logic approach to dissolved gas analysis for power transformer failure index and fault identification. Energies, 14(1), 36.
dc.relation.references[67]. Ispirli, M. M., Adali, H., Kalenderli, Ö., Celik, M. Z., & Oral, B. (2021). Application and Comparative Analysis of Fuzzy Inference System for Transformer Fault Diagnosis with Dissolved Gases in Oil. In 2021 3rd International Conference on High Voltage Engineering and Power Systems (ICHVEPS) (pp. 187-190).
dc.relation.references[68]. Thango, B. A., Nnachi, A. F., Jordaan, J. A., Akumu, A. O., & Abe, B. T. (2021). A Diagnostic Study of Dissolved Gases in Transformers based on Fuzzy Logic Approach. In 2021 IEEE PES/IAS PowerAfrica (pp. 1-5). IEEE.
dc.relation.references[69]. Wang, X., Guo, F., & Xu, W. (2020, June). DGA fuzzy logic diagnostic method based on subordinating function. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 1381-1384).
dc.relation.references[70]. Taha, I. B., Ibrahim, S., & Mansour, D. E. A. (2021). Power transformer fault diagnosis based on DGA using a convolutional neural network with noise in measurements. IEEE Access, 9, (pp. 111162-111170).
dc.relation.references[71]. Wang, W., Zhang, R., Li, Y., Lv, G., Luo, Z., & Xu, H. (2022, February). Fault Diagnosis for Power Transformer Based on Uniform Distribution of Grasshopper Optimization Algorithm Combined with Neural Network. In 2022 International Conference on Power Energy Systems and Applications (ICoPESA) (pp. 225-230). IEEE.
dc.relation.references[72]. Poonnoy, N., Suwanasri, C., & Suwanasri, T. (2022, March). Neural Network Approach to Dissolved Gas Analysis for Fault Analysis in Power Transformers. In 2022 International Electrical Engineering Congress (iEECON) (pp. 1-4). IEEE.
dc.relation.references[73]. Chen, W., Zai, H., He, H., Zhang, K., Xi, R., & Fu, F. (2021, October). Research on Fault Diagnosis Method of Power Transformer Based on Graph Neural Network. In 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2) (pp. 4289-4294). IEEE.
dc.relation.references[74]. Zhang, Y., Wang, Y., Fan, X., Zhang, W., Zhuo, R., Hao, J., & Shi, Z. (2020). An integrated model for transformer fault diagnosis to improve sample classification near decision boundary of support vector machine. Energies, 13(24), 6678.
dc.relation.references[75]. Li, J., Li, G., Hai, C., & Guo, M. (2021). Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm. IEEE Access, 10, 1522-1532
dc.relation.references[76]. Li, X., Li, Y., Xu, Y., Li, R., & Zhang, G. (2022, March). Fault Diagnostics of Oil-immersed Power Transformer via SMOTE and GWO-SVM. In 2022 4th Asia Energy and Electrical Engineering Symposium (AEEES) (pp. 935-939).
dc.relation.references[77]. Ghoneim, S. S., Mahmoud, K., Lehtonen, M., & Darwish, M. M. (2021). Enhancing diagnostic accuracy of transformer faults using teaching-learning-based optimization. Ieee Access, 9, 30817-30832
dc.relation.references[78]. Nelson, W. B. (2009). Accelerated testing: statistical models, test plans, and data analysis. John Wiley & Sons.
dc.relation.references[79]. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007). Numerical recipes 3rd edition: The art of scientific computing. Cambridge university press.
dc.relation.references[80]. Ben-Israel, A. (1966). A Newton-Raphson method for the solution of systems of equations. Journal of Mathematical analysis and applications, 15(2), 243-252.
dc.relation.references[81]. Church, J. O., Haupert, T. J., & Jakob, F. (1987). Analyze incipient faults with dissolved gas Nomograph. Electrical world, 201(10), 40-44.
dc.relation.references[82]. Pahlavanpour, B., & Pablo, A. D. (2010). Insulating Oil Reclamation and Dechlorination. CIGRE. Cigre, CIGRE. WG D1, 1.
dc.relation.references[83]. IEEE. (2013). IEEE Guide for Diagnostic Field Testing of Fluid-Filled Power Transformers, Regulators, and Reactors. Proceedings of the IEEE Std C57. 152-2013, 1-121.
dc.relation.references[84]. IEEE Standard for General Requirements for Liquid-Immersed Distribution, Power, and Regulating Transformers, IEEE Standard C57.12.00, 2015.
dc.relation.references[85]. IEEE Guide for Diagnostic Field Testing of Electric Power Apparatus- Part 1: Oil Filled Power Transformers, Regulators, and Reactors, IEEE Standard 62, 2005.
dc.relation.references[86]. IEEE Std 62‐1995. (1995). IEEE guide for diagnostic field testing of electric power apparatus‐part 1: oil filled power transformers, regulators, and reactors.
dc.relation.references[87]. IEEE Guide for the Statistical Analysis of Thermal Life Test Data, in ANSI/IEEE Std 101-1987(R2010) (Revision of IEEE Std 101-1972) , vol., no., pp.1-34, 31 Dec. 1988, doi: 10.1109/IEEESTD.1988.7473795.
dc.relation.references[88]. Meeker, W. Q., Escobar, L. A., & Pascual, F. G. (2022). Statistical methods for reliability data. John Wiley & Sons.
dc.relation.references[89]. Dhillon, B. S. (2007). Applied reliability and quality: fundamentals, methods and procedures. Springer Science & Business Media
dc.relation.references[90]. Manzini, R., Regattieri, A., Pham, H., & Ferrari, E. (2010). Maintenance for industrial systems. London: Springer.
dc.relation.references[91]. Hoyland, A., & Rausand, M. (2009). System reliability theory: models and statistical methods. John Wiley & Sons.
dc.relation.references[92]. Elsayed, A., (2021) Reliability Engineering-Wiley (2021), John Wiley & Sons
dc.relation.references[93]. Modarres, M., Amiri, M., & Jackson, C. (2017). Probabilistic physics of failure approach to reliability: modeling, accelerated testing, prognosis and reliability assessment. John Wiley & Sons.
dc.relation.references[94]. Mendoza, H, Bautista, G. (2002). Diseño Experimental. Universidad Nacional de Colombia, http://www.virtual.unal.edu.co/cursos/ciencias/2000352/. Licencia: Creative Commons BY-NC-ND.
dc.relation.references[95]. Dean, R. B., & Dixon, W. J. (1951). Simplified statistics for small numbers of observations. Analytical chemistry, 23(4), 636-638.
dc.relation.references[96]. https://www.labe.unal.edu.co/
dc.relation.references[97]. Grisaru, M. (2020). A century of dissolved gas analysis-part III. Transformers Magazine, 7(1), 50-58.
dc.relation.references[98]. Wang, M. V. A. J., Vandermaar, A. J., & Srivastava, K. D. (2002). Review of condition assessment of power transformers in service. IEEE Electrical insulation magazine, 18(6), 12-25.
dc.relation.references[99]. Hagman, W. H., Kirtley Jr, J. L., Lesieutre, B. C., Boyd, M. J., Warren, E. P., Chou, H. P., & Tabors, R. D. (1996). Model-based Monitoring of Transformers. Massachusetts Institute of Technology, Laboratory for Electromagnetic and Electronic Systems.
dc.relation.references[100]. IEEE Power & Energy Society. (2012). Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators.
dc.relation.references[101]. https://en.wikipedia.org/wiki/Icosane
dc.relation.references[102]. IEEE Recommended IEEE Recommended Practice for Performing Temperature Rise Tests on Oil-Immersed Power Transformers at Loads Beyond Nameplate Ratings, IEEE Standard C57.119.
dc.relation.references[103]. Ben-gang, W., Chen-zhao, F., Hong-lei, L., Ke-jun, L., & Yong-liang, L. (2016, May). The improved thermal-circuit model for hot-spot temperature calculation of oil-immersed power transformers. In 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference (pp. 809-813).
dc.relation.references[104]. Dodson, B., & Schwab, H. (2006). Accelerated testing. SAE International.
dc.relation.references[105]. McPherson, J. W. (2018). Reliability physics and engineering: time-to-failure modeling. Springer.
dc.relation.references[106]. Modarres, M., Kaminskiy, M. P., & Krivtsov, V. (2009). Reliability engineering and risk analysis: a practical guide. CRC press.
dc.relation.references[107]. ReliaSoft, C. (2014). Accelerated life testing reference. Tucson, AZ: ReliaSoft Publishing.
dc.relation.references[108]. LuValle, M. J., Lefevre, B. G., & Kannan, S. (2004). Design and analysis of accelerated tests for mission critical reliability. Chapman and Hall/CRC.
dc.relation.references[109]. Du, L., Wang, Y., Wang, W., & Chen, X. (2018). Studies on a Thermal Fault Simulation Device and the Pyrolysis Process of Insulating Oil. Energies, 11(12), 3392.
dc.relation.references[110]. Chen, G., & Pham, T. T. (2000). Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC press.
dc.relation.references[111]. Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction to fuzzy logic using MATLAB (Vol. 1). Berlin: Springer.
dc.relation.references[112]. Hurley, W. G., & Wölfle, W. H. (2013). Transformers and inductors for power electronics: theory, design and applications. John Wiley & Sons.
dc.relation.references[113]. McLyman, C. W. T. (2004). Transformer and inductor design handbook. CRC press.
dc.relation.references[114]. Valchev, V. C., & Van den Bossche, A. (2018). Inductors and transformers for power electronics. CRC press.
dc.relation.references[115]. METGLAS® 2605-SA1 core datasheet, National Energy Technology Laboratory, 2018
dc.relation.references[116]. Sullivan, C. R., & Zhang, R. Y. (2014, March). Simplified design method for Litz wire. In 2014 IEEE Applied Power Electronics Conference and Exposition-APEC 2014 (pp. 2667-2674). IEEE.
dc.relation.references[117]. Metglas, High frequency distributed gap inductor core, Technical bulletin
dc.relation.references[118]. https://www.mkmagnetics.com
dc.relation.references[119]. https://metglas.com/
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembRedes eléctricas
dc.subject.lembTransformadores eléctricos
dc.subject.proposalEnvejecimiento acelerado
dc.subject.proposalEcuación de Arrhenius
dc.subject.proposalDiagnóstico del transformador
dc.subject.proposalTransformador inmerso en aceite
dc.subject.proposalTransformadores de media frecuencia
dc.subject.proposalAccelerated aging
dc.subject.proposalArrhenius equation
dc.subject.proposalTransformer diagnosis
dc.subject.proposalOil immersed transformer
dc.subject.proposalMedium frequency transformers
dc.title.translatedThermal degradation of transformer insulation systems
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentImage
dc.type.contentText
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentMaestros
dcterms.audience.professionaldevelopmentPúblico general
dc.description.curricularareaEléctrica, Electrónica, Automatización Y Telecomunicaciones


Archivos en el documento

Thumbnail
Nombre:
75034637.2022.pdf
Tamaño:
4.535Mb
Formato:
PDF
Descripción:
Tesis de Doctorado en Ingeniería ...
Descargar

Este documento aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del documento

Atribución-NoComercial 4.0 InternacionalEsta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.Este documento ha sido depositado por parte de el(los) autor(es) bajo la siguiente constancia de depósito