Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales
Este proyecto presenta un modelo para el diagnóstico de sistemas eléctricos industriales mediante el uso de redes bayesianas y análisis de sags. La construcción de la red bayesiana propuesta se fundamenta en modelos teóricos, datos históricos y la experiencia de expertos. Se llevará a cabo la implem...
- Autores:
-
Rios Andrade, Cristian Alejandro
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/73576
- Acceso en línea:
- https://hdl.handle.net/1992/73576
- Palabra clave:
- Diagnóstico
Red bayesiana
Inferencia causal
Sistemas eléctricos industriales
Calidad de la potencia
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International
| id |
UNIANDES2_5ff812c2357a2d358ca8f71b71b611c2 |
|---|---|
| oai_identifier_str |
oai:repositorio.uniandes.edu.co:1992/73576 |
| network_acronym_str |
UNIANDES2 |
| network_name_str |
Séneca: repositorio Uniandes |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| title |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| spellingShingle |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales Diagnóstico Red bayesiana Inferencia causal Sistemas eléctricos industriales Calidad de la potencia Ingeniería |
| title_short |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| title_full |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| title_fullStr |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| title_full_unstemmed |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| title_sort |
Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales |
| dc.creator.fl_str_mv |
Rios Andrade, Cristian Alejandro |
| dc.contributor.advisor.none.fl_str_mv |
Ramos López, Gustavo Andrés |
| dc.contributor.author.none.fl_str_mv |
Rios Andrade, Cristian Alejandro |
| dc.contributor.jury.none.fl_str_mv |
Ríos Mesías, Mario Alberto |
| dc.subject.keyword.spa.fl_str_mv |
Diagnóstico Red bayesiana Inferencia causal Sistemas eléctricos industriales Calidad de la potencia |
| topic |
Diagnóstico Red bayesiana Inferencia causal Sistemas eléctricos industriales Calidad de la potencia Ingeniería |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
Este proyecto presenta un modelo para el diagnóstico de sistemas eléctricos industriales mediante el uso de redes bayesianas y análisis de sags. La construcción de la red bayesiana propuesta se fundamenta en modelos teóricos, datos históricos y la experiencia de expertos. Se llevará a cabo la implementación de esta red bayesiana en Python, seguida de una exhaustiva caracterización para verificar su correcto funcionamiento y resiliencia. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-01-29T20:13:24Z |
| dc.date.available.none.fl_str_mv |
2024-01-29T20:13:24Z |
| dc.date.issued.none.fl_str_mv |
2024-01-12 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| format |
http://purl.org/coar/resource_type/c_7a1f |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/1992/73576 |
| dc.identifier.instname.none.fl_str_mv |
instname:Universidad de los Andes |
| dc.identifier.reponame.none.fl_str_mv |
reponame:Repositorio Institucional Séneca |
| dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
| url |
https://hdl.handle.net/1992/73576 |
| identifier_str_mv |
instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
"Ieee recommended practice for monitoring electric power quality," IEEE Std 1159-1995, pp. 1–80, 1995. M. H. Bollen, Understanding power quality problems, vol. 3. IEEE press New York, 2000. R. C. Dugan, Electrical power system quality. The McGraw Hill Companies,, 2000. A. Baggini, Handbook of power quality. John Wiley & Sons, 2008. A. K. Goswami, C. P. Gupta, and G. K. Singh, “Assessment of financial losses due to voltage sags in an Indian distribution system,” in 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, pp. 1–6, IEEE, 2008. D. Montenegro and G. Ramos, “Smart diagnosis of power quality disturbances using bayesian networks,” in 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), pp. 1–5, IEEE, 2012. G. A. Ramos López et al., “Análisis de la seguridad de los sistemas eléctricos industriales,” 2008. C. P. Gupta and J. Milanovic, “Costs of voltage sags: Comprehensive assessment procedure,” in 2005 IEEE Russia Power Tech, pp. 1–7, IEEE, 2005. A. Torres, M. T. Rueda, and D. Reyes, “Bayesian networks for power quality analysis in the industrial sector,” in 2006 International Conference on Probabilistic Methods Applied to Power Systems, pp. 1–7, IEEE, 2006. K. Srinath, “Python–the fastest growing programming language,” International Research Journal of Engineering and Technology, vol. 4, no. 12, pp. 354–357, 2017. J. M. E. Forero, G. Ramos, and A. Ovalle, “Decision making methodology based on expert systems for minimizing economic impact of voltage sags in industrial power systems,” in 2013 IEEE Grenoble Conference, pp. 1–6, IEEE, 2013. G. Ramos, A. Torres, and M. Rios, “Analysis of electrical industrial systems using probabilistic networks,” IEEE Latin America Transactions, vol. 8, no. 5, pp. 505–511, 2010. M. H. Bollen and L. Zhang, “Different methods for classification of three-phase unbalanced voltage dips due to faults,” Electric power systems research, vol. 66, no. 1, pp. 59–69, 2003. J. A. Gámez, S. Moral, and A. S. Cerdan, Advances in Bayesian networks, vol. 146. Springer, 2013. D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques. MIT press, 2009. "Ieee recommended practice for the design of reliable industrial and commercial power systems (gold book)," IEEE Std 493-1997 [IEEE Gold Book], pp. 1–464, 1998. P. Brief, “7: Undervoltage ride-through performance of off-the-shelf personal computers,” EPRI Power Electronics Application Centre, Knoxville, TN, 1994. Y. Sekine, “Present state of momentary voltage dip interferences and the countermeasures in japan,” CIGRE 1992, 1992. T. Colliau, G. Rogers, Z. Hughes, and C. Ozgur, “Matlab vs. python vs. r,” Journal of Data Science, vol. 15, no. 3, 2017. A. Ankan and A. Panda, “pgmpy: Probabilistic graphical models using python,” in Proceedings of the 14th Python in Science Conference (SCIPY 2015), Citeseer, 2015. M. Scutari, “Dirichlet bayesian network scores and the maximum relative entropy principle,” Behaviormetrika, vol. 45, pp. 337–362, 2018. |
| dc.rights.en.fl_str_mv |
Attribution 4.0 International |
| dc.rights.uri.none.fl_str_mv |
http://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 |
Attribution 4.0 International http://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 |
46 páginas |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad de los Andes |
| dc.publisher.program.none.fl_str_mv |
Ingeniería Eléctrica |
| dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingeniería |
| dc.publisher.department.none.fl_str_mv |
Departamento de Ingeniería Eléctrica y Electrónica |
| publisher.none.fl_str_mv |
Universidad de los Andes |
| institution |
Universidad de los Andes |
| bitstream.url.fl_str_mv |
https://repositorio.uniandes.edu.co/bitstreams/49256d85-e505-4dc0-ad9f-5891fc526cf5/download https://repositorio.uniandes.edu.co/bitstreams/b3fc770c-b2cf-4065-9fb6-30ebb5428bc6/download https://repositorio.uniandes.edu.co/bitstreams/2d141712-2cb4-4fd0-986e-c57b305fdc54/download https://repositorio.uniandes.edu.co/bitstreams/792d044d-331f-4235-8132-166b50390177/download https://repositorio.uniandes.edu.co/bitstreams/9e5ccc9f-ab55-49a5-88cc-f06c93c08bf1/download https://repositorio.uniandes.edu.co/bitstreams/6216f6f0-de2f-46c3-bc80-62541229951e/download https://repositorio.uniandes.edu.co/bitstreams/7c44eae3-f816-4848-b042-5c5c021be82e/download https://repositorio.uniandes.edu.co/bitstreams/223419d5-cea5-4ecc-9549-3212d197daaf/download |
| bitstream.checksum.fl_str_mv |
365f343968bea583b35aceabb5f3a359 089fe82548bde09124e79c55b0975812 0175ea4a2d4caec4bbcc37e300941108 ae9e573a68e7f92501b6913cc846c39f b89af1ee98cd7f4e4d711289b349b7da 714f882ac4308bb7a985c80aa05c394c 043728a8652d4634e2157630f1767cce a308fea6f5fd210b6558685ccbba619d |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio institucional Séneca |
| repository.mail.fl_str_mv |
adminrepositorio@uniandes.edu.co |
| _version_ |
1812133924481007616 |
| spelling |
Ramos López, Gustavo AndrésRios Andrade, Cristian AlejandroRíos Mesías, Mario Alberto2024-01-29T20:13:24Z2024-01-29T20:13:24Z2024-01-12https://hdl.handle.net/1992/73576instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Este proyecto presenta un modelo para el diagnóstico de sistemas eléctricos industriales mediante el uso de redes bayesianas y análisis de sags. La construcción de la red bayesiana propuesta se fundamenta en modelos teóricos, datos históricos y la experiencia de expertos. Se llevará a cabo la implementación de esta red bayesiana en Python, seguida de una exhaustiva caracterización para verificar su correcto funcionamiento y resiliencia.Ingeniero EléctricoPregrado46 páginasapplication/pdfspaUniversidad de los AndesIngeniería EléctricaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industrialesTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPDiagnósticoRed bayesianaInferencia causalSistemas eléctricos industrialesCalidad de la potenciaIngeniería"Ieee recommended practice for monitoring electric power quality," IEEE Std 1159-1995, pp. 1–80, 1995.M. H. Bollen, Understanding power quality problems, vol. 3. IEEE press New York, 2000.R. C. Dugan, Electrical power system quality. The McGraw Hill Companies,, 2000.A. Baggini, Handbook of power quality. John Wiley & Sons, 2008.A. K. Goswami, C. P. Gupta, and G. K. Singh, “Assessment of financial losses due to voltage sags in an Indian distribution system,” in 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, pp. 1–6, IEEE, 2008.D. Montenegro and G. Ramos, “Smart diagnosis of power quality disturbances using bayesian networks,” in 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), pp. 1–5, IEEE, 2012.G. A. Ramos López et al., “Análisis de la seguridad de los sistemas eléctricos industriales,” 2008.C. P. Gupta and J. Milanovic, “Costs of voltage sags: Comprehensive assessment procedure,” in 2005 IEEE Russia Power Tech, pp. 1–7, IEEE, 2005.A. Torres, M. T. Rueda, and D. Reyes, “Bayesian networks for power quality analysis in the industrial sector,” in 2006 International Conference on Probabilistic Methods Applied to Power Systems, pp. 1–7, IEEE, 2006.K. Srinath, “Python–the fastest growing programming language,” International Research Journal of Engineering and Technology, vol. 4, no. 12, pp. 354–357, 2017.J. M. E. Forero, G. Ramos, and A. Ovalle, “Decision making methodology based on expert systems for minimizing economic impact of voltage sags in industrial power systems,” in 2013 IEEE Grenoble Conference, pp. 1–6, IEEE, 2013.G. Ramos, A. Torres, and M. Rios, “Analysis of electrical industrial systems using probabilistic networks,” IEEE Latin America Transactions, vol. 8, no. 5, pp. 505–511, 2010.M. H. Bollen and L. Zhang, “Different methods for classification of three-phase unbalanced voltage dips due to faults,” Electric power systems research, vol. 66, no. 1, pp. 59–69, 2003.J. A. Gámez, S. Moral, and A. S. Cerdan, Advances in Bayesian networks, vol. 146. Springer, 2013.D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques. MIT press, 2009."Ieee recommended practice for the design of reliable industrial and commercial power systems (gold book)," IEEE Std 493-1997 [IEEE Gold Book], pp. 1–464, 1998.P. Brief, “7: Undervoltage ride-through performance of off-the-shelf personal computers,” EPRI Power Electronics Application Centre, Knoxville, TN, 1994.Y. Sekine, “Present state of momentary voltage dip interferences and the countermeasures in japan,” CIGRE 1992, 1992.T. Colliau, G. Rogers, Z. Hughes, and C. Ozgur, “Matlab vs. python vs. r,” Journal of Data Science, vol. 15, no. 3, 2017.A. Ankan and A. Panda, “pgmpy: Probabilistic graphical models using python,” in Proceedings of the 14th Python in Science Conference (SCIPY 2015), Citeseer, 2015.M. Scutari, “Dirichlet bayesian network scores and the maximum relative entropy principle,” Behaviormetrika, vol. 45, pp. 337–362, 2018.201915798PublicationORIGINALautorizacion tesis electrica-CAR.pdfautorizacion tesis electrica-CAR.pdfHIDEapplication/pdf361674https://repositorio.uniandes.edu.co/bitstreams/49256d85-e505-4dc0-ad9f-5891fc526cf5/download365f343968bea583b35aceabb5f3a359MD51Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales.pdfModelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales.pdfapplication/pdf1410614https://repositorio.uniandes.edu.co/bitstreams/b3fc770c-b2cf-4065-9fb6-30ebb5428bc6/download089fe82548bde09124e79c55b0975812MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908https://repositorio.uniandes.edu.co/bitstreams/2d141712-2cb4-4fd0-986e-c57b305fdc54/download0175ea4a2d4caec4bbcc37e300941108MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-82535https://repositorio.uniandes.edu.co/bitstreams/792d044d-331f-4235-8132-166b50390177/downloadae9e573a68e7f92501b6913cc846c39fMD54TEXTautorizacion tesis electrica-CAR.pdf.txtautorizacion tesis electrica-CAR.pdf.txtExtracted texttext/plain1993https://repositorio.uniandes.edu.co/bitstreams/9e5ccc9f-ab55-49a5-88cc-f06c93c08bf1/downloadb89af1ee98cd7f4e4d711289b349b7daMD55Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales.pdf.txtModelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales.pdf.txtExtracted texttext/plain82543https://repositorio.uniandes.edu.co/bitstreams/6216f6f0-de2f-46c3-bc80-62541229951e/download714f882ac4308bb7a985c80aa05c394cMD57THUMBNAILautorizacion tesis electrica-CAR.pdf.jpgautorizacion tesis electrica-CAR.pdf.jpgGenerated Thumbnailimage/jpeg10883https://repositorio.uniandes.edu.co/bitstreams/7c44eae3-f816-4848-b042-5c5c021be82e/download043728a8652d4634e2157630f1767cceMD56Modelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales.pdf.jpgModelo de redes bayesianas para el diagnóstico de sistemas eléctricos industriales.pdf.jpgGenerated Thumbnailimage/jpeg10036https://repositorio.uniandes.edu.co/bitstreams/223419d5-cea5-4ecc-9549-3212d197daaf/downloada308fea6f5fd210b6558685ccbba619dMD581992/73576oai:repositorio.uniandes.edu.co:1992/735762024-04-08 10:43:14.347http://creativecommons.org/licenses/by/4.0/Attribution 4.0 Internationalopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.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 |