Optimal PMU placement considering N-1contingencies constraints in electrical power systems
DOI:
https://doi.org/10.29019/enfoqueute.v10n1.437Keywords:
Phasor measurement units, N-1 Contingency, Power system measurements, Optimal PMU location, Electrical power SystemsAbstract
The measurement of electrical parameters through phasor measurement units in the power systems is fundamental, since the obtained data is used to estimate the state of its operation. In the present investigation the problem arises for the optimal deployment of phasor measurement units regarding restrictions of observability, redundancy and N-1 contingencies. Unit minimization considers the output of a transmission line or the failure of a phasor measurement unit and guarantees 100 % observability of the power system; For the optimization mixed integer linear programming was used. The proposed algorithm was tested with the IEEE test models of 9, 14, 30 and 118 nodes.
Downloads
Download data is not yet available.
References
Akhlaghi, S. (2016). Optimal PMU placement considering contingency-constraints for power system observability and measurement redundancy. 2016 IEEE Power and Energy Conference at Illinois, PECI 2016, 1-7. https://doi.org/10.1109/PECI.2016.7459251
Aminifar, F.; Khodaei, A.; Fotuhi-Firuzabad, M., y Shahidehpour, M. (2010). Contingency-Constrained PMU Placement in Power Networks. IEEE Transaction on Power Systems, 25(1), 516–523.
Arciniegas, A. F., Imbajoa, D. E., y Revelo, J. (2017). Diseño e implementación de un Sistema de Medición Inteligente para AMI de la microrred de la Universidad de Nariño (Design and implementation of a Smart Measurement System for AMI in the microgrid of the University of Nariño). Enfoque UTE, 7(1), 300-314. Recuperado de http://ingenieria.ute.edu.ec/enfoqueute/
Carrión, D.; García, E.; González, J. W.; Issac, I.; López, G. J., y Hincapié, R. (2017). Método heurístico de ubicación óptima de Centros de Transformación y Enrutamiento de Redes Eléctricas de Distribución. Revista Técnica “Energía”, 13(13), 90-96.
Carrión, D., y González, J. W. (2018). Optimal PMU Location in Electrical Power Systems Under N-1 Contingency. In 2018 International Conference on Information Systems and Computer Science (INCISCOS) (pp. 165-170). https://doi.org/10.1109/INCISCOS.2018.00031
Carrión, D.; González, J. W.; Isaac, I. A., y López, G. J. (2017). Optimal Fault Location in Transmission Lines Using Hybrid Method. In 2017 IEEE PES Innovative Smart Grid Technologies Conference (p. 6). Quito: Ieee. https://doi.org/10.1109/ISGT-LA.2017.8126757
Carrión, D.; González, J. W.; Isaac, I. A.; López, G. J., y Cardona, H. A. (2017). Load Characterization Based on Voltage and Current Phasorial Measurements in Micro-Grids. 2017 International Conference on Information Systems and Computer Science (INCISCOS), 1–6. https://doi.org/10.1109/INCISCOS.2017.23
Carrión, D.; Inga, E.; González, J. W., y Hincapié, R. (2016). Optimal Geographical Placement of Phasor Measurement Units based on Clustering Techniques. En 51st International Universities’ Power Engineering Conference (p. 6). Coimbra. https://doi.org/10.1109/UPEC.2016.8114003
Cevallos, H.; Intriago, G.; Plaza, D., y Idrovo, R. (2018). The extended Kalman filter in the dynamic state estimation of electrical power systems (El filtro extendido de Kalman en la estimación del estado dinámico de sistemas eléctricos de potencia). Enfoque UTE, 9(4), 120-130.
Correa, E.; Inga, E.; Inga, J., y Hincapié, R. (2018). Electrical consumption pattern base on meter data management system using big data techniques. Proceedings-2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017–Novem, pp. 334-339. https://doi.org/10.1109/INCISCOS.2017.19
Esmaili, M.; Shayanfar, H. A., y Gharani, K. (2014). Observability-Enhanced PMU Placement Considering Conventional Measurements and Contingencies. Iranian Journal of Electrical and Electronic Engineering, 10(4), 283-292. Recuperado de http://ijeee.iust.ac.ir/article-1-642-en.html.
García, E. M.; López, B. D. B., y Millán, I. A. I. (2018). Analysis of the Voltage Profile by the Insertion of Electric Vehicles in the Distribution Network Considering Response to Demand. Proceedings-2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017–Novem, 7-13. https://doi.org/10.1109/INCISCOS.2017.26
García, E. M., y Millán, I. A. I. (2018). Multi-objective Optimization for the Management of the Response to the Electrical Demand in Commercial Users. Proceedings-2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017–Novem, 14-20. https://doi.org/10.1109/INCISCOS.2017.25
Gou, B. (2008). Generalized integer linear programming formulation for optimal PMU placement. IEEE Transactions on Power Systems, 23(3), 1099-1104. https://doi.org/10.1109/TPWRS.2008.926475
Gou, B. (2008). Optimal Placement of PMUs by Integer Linear Programming. IEEE Transactions on Power Systems, 23(3), 1525-1526. https://doi.org/10.1109/TPWRS.2008.926723
Grigoras, G.; Cartina, G., y Gavrilas, M. (2009). Using of Clustering Techniques in Optimal Placement of Phasor Measurements Units. In 9th WSEAS/IASME International Conference on Electric Power Systems, High Voltager, Electric Machines (pp. 104-108)
Guerrón, G.; García, E., y Montero, A. (2014). Influencia de las ráfagas de viento en la calidad de la energía de los parques eólicos (Influence of wind gusts in power quality on wind farms). Enfoque UTE, 5(3), 25-44
Huang, L.; Sun, Y.; Xu, J.; Gao, W.; Zhang, J., y Wu, Z. (2014). Optimal PMU Placement Considering Controlled Islanding of Power System. IEEE Transactions on Power Systems, 29(2), pp. 742–755. https://doi.org/10.1109/TPWRS.2013.2285578
Inga, E.; Carrión, D.; Águila, A.; García, E., y Hincapié, R. (2016). Minimal Deployment and Routing Geographic of PMUs on Electrical Power System based on MST Algorithm. IEEE Latin America Transactions 14 (5), 2264–2270. https://doi.org/10.1109/TLA.2016.7530422
Inga Ortega, E.; Inga, J.; Correa, E., y Hincapié, R. (2018). Reconstrucción del patrón de consumo eléctrico a partir de Big Data mediante técnica de MapReduce. Enfoque UTE, 9(1), 177-187. https://doi.org/10.29019/enfoqueute.v9n1.220
Jaramillo, C., y Carrión, D. (2016). Ubicación óptima de PMUs basado en criterios de observabilidad y evaluación mediante Búsqueda Tabú. Universidad Politécnica Salesiana.
Jiang, Q.; Li, X.; Wang, B., y Wang, H. (2012). PMU-Based Fault Location Using Voltage Measurements in Large Transmission Networks. IEEE Transactions on Power Delivery, 27(3), 1644-1652. https://doi.org/10.1109/TPWRD.2012.2199525
Jiang, Z.; Miao, S.; Xu, H.; Liu, P., y Zhang, B. (2012). An effective fault location technique for transmission grids using phasor measurement units. International Journal of Electrical Power y Energy Systems, 42(1), 653-660. https://doi.org/10.1016/j.ijepes.2012.03.020.
Kekatos, V.; Giannakis, G. B., y Wollenberg, B. (2012). Optimal placement of phasor measurement units via convex relaxation. IEEE Transactions on Power Systems, 27(3), 1521–1530. https://doi.org/10.1109/TPWRS.2012.2185959
Ketabi, A.; Nosratabadi, S. M., y Sheibani, M. R. (2012). Optimal PMU placement with uncertainty using Pareto method. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/501893.
Korres, G. N.; Georgilakis, P. S.; Koutsoukis, N. C., y Manousakis, N. M. (2013). Numerical observability method for optimal phasor measurement units placement using recursive Tabu search method. IET Generation, Transmission y Distribution, 7(4), 347-356. https://doi.org/10.1049/iet-gtd.2012.0377
Maldonado, M. G. R. (2017). Wireless Sensor Network for Smart Home Services Using Optimal Communications. 2017 International Conference on Information Systems and Computer Science (INCISCOS), 27–32. https://doi.org/10.1109/INCISCOS.2017.21.
Manousakis, N. M.; Korres, G. N., y Georgilakis, P. S. (2011). Optimal placement of phasor measurement units: A literature review. 2011 16th International Conference on Intelligent System Applications to Power Systems, 1–6. https://doi.org/10.1109/ISAP.2011.6082183
Mccamish, B.; Kulkarni, J.; Ke, Z.; Harpool, S.; Huo, C.; Brekken, T., yYokochi, A. (2017). A rapid PMU-based load composition and PMU estimation method. Electric Power Systems Research, 143, 44–52. https://doi.org/10.1016/j.epsr.2016.10.028
Mori, H., y Tsuzuki, S. (1991). A fast method for topological observability analysis using a minimum spanning tree technique. Power Systems, IEEE Transactions On, 6(2), 491-500. https://doi.org/10.1109/59.76691
Moscoso-Zea, O., y Luján-Mora, S. (2017). Metodologías Sugeridas de Evaluación y Selección de Software de Arquitectura Empresarial para la Digitalización del Conocimiento (Suggested Methodologies for Evaluation and Selection of Enterprise Architecture Software for Knowledge Digitization). Enfoque UTE, 7(1), 315–328. https://doi.org/10.29019/enfoqueute.v8n1.144
Nuqui, R. F., y Phadke, A. G. (2005). Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Transactions on Power Delivery, 20(4), 2381-2388. https://doi.org/10.1109/TPWRD.2005.855457
Paudel, J., Xu, X., Balasubramaniam, K., y Makram, E. B. (2015). A Strategy for PMU Placement Considering the Resiliency of Measurement System, (November), 29–36.
Rahman, N. H. A., y Zobaa, A. F. (2016). Optimal PMU placement using topology transformation method in power systems. Journal of Advanced Research, 7(5), 625-634. https://doi.org/10.1016/j.jare.2016.06.003
Ruíz, M.; Masache, P., y Dominguez, J. (2018). High Availability Network for Critical Communications on Smart Grids, (Ssn), 1-5.
Sánchez, A., y Carrión, D. (2017). Modeling of the Behavior Power Flow on Transmission Lines Based on Voltage and Current Synchronopasors. IEEE Latin America Transactions, 16(4), 1142-1149. https://doi.org/10.1109/TLA.2018.8362149
Sodhi, R., Srivastava, S. C., y Singh, S. N. (2009). Optimal PMU placement to ensure system observability under contingencies. 2009 IEEE Power and Energy Society General Meeting, PES ’09. https://doi.org/10.1109/PES.2009.5275618
Song, Y.; Ma, S.; Wu, L.; Quan, W., y He, H. (2009). PMu placement based on power system characteristics. 1st International Conference on Sustainable Power Generation and Supply, SUPERGEN ’09, 1-6. https://doi.org/10.1109/SUPERGEN.2009.5348359
Srivastava, A. (2015). Optimal PMU Placement for Complete Power System Observability using Binary Cat Swarm Optimization, 0-5.
Taher, S. A.; Mahmoodi, H., y Aghaamouei, H. (2016). Optimal PMU location in power systems using MICA. Alexandria Engineering Journal, 55(1), 399–406. https://doi.org/10.1016/j.aej.2015.12.002
Werho, T.; Member, S.; Vittal, V.; Kolluri, S., y Wong, S. M. (2016). Power System Connectivity Monitoring Using a Graph Theory Network Flow Algorithm, 1-8.
Zhu, H. L.; Duan, Y. X.; Zhang, X. P.; Qi, H., y Huang, C. X. (2013). Hybrid of MST and genetic algorithm on minimizing PMU placement. Proceedings of the 2013 3rd International Conference on Intelligent System Design and Engineering Applications, ISDEA 2013, (2), 820-823. https://doi.org/10.1109/ISDEA.2012.195
Aminifar, F.; Khodaei, A.; Fotuhi-Firuzabad, M., y Shahidehpour, M. (2010). Contingency-Constrained PMU Placement in Power Networks. IEEE Transaction on Power Systems, 25(1), 516–523.
Arciniegas, A. F., Imbajoa, D. E., y Revelo, J. (2017). Diseño e implementación de un Sistema de Medición Inteligente para AMI de la microrred de la Universidad de Nariño (Design and implementation of a Smart Measurement System for AMI in the microgrid of the University of Nariño). Enfoque UTE, 7(1), 300-314. Recuperado de http://ingenieria.ute.edu.ec/enfoqueute/
Carrión, D.; García, E.; González, J. W.; Issac, I.; López, G. J., y Hincapié, R. (2017). Método heurístico de ubicación óptima de Centros de Transformación y Enrutamiento de Redes Eléctricas de Distribución. Revista Técnica “Energía”, 13(13), 90-96.
Carrión, D., y González, J. W. (2018). Optimal PMU Location in Electrical Power Systems Under N-1 Contingency. In 2018 International Conference on Information Systems and Computer Science (INCISCOS) (pp. 165-170). https://doi.org/10.1109/INCISCOS.2018.00031
Carrión, D.; González, J. W.; Isaac, I. A., y López, G. J. (2017). Optimal Fault Location in Transmission Lines Using Hybrid Method. In 2017 IEEE PES Innovative Smart Grid Technologies Conference (p. 6). Quito: Ieee. https://doi.org/10.1109/ISGT-LA.2017.8126757
Carrión, D.; González, J. W.; Isaac, I. A.; López, G. J., y Cardona, H. A. (2017). Load Characterization Based on Voltage and Current Phasorial Measurements in Micro-Grids. 2017 International Conference on Information Systems and Computer Science (INCISCOS), 1–6. https://doi.org/10.1109/INCISCOS.2017.23
Carrión, D.; Inga, E.; González, J. W., y Hincapié, R. (2016). Optimal Geographical Placement of Phasor Measurement Units based on Clustering Techniques. En 51st International Universities’ Power Engineering Conference (p. 6). Coimbra. https://doi.org/10.1109/UPEC.2016.8114003
Cevallos, H.; Intriago, G.; Plaza, D., y Idrovo, R. (2018). The extended Kalman filter in the dynamic state estimation of electrical power systems (El filtro extendido de Kalman en la estimación del estado dinámico de sistemas eléctricos de potencia). Enfoque UTE, 9(4), 120-130.
Correa, E.; Inga, E.; Inga, J., y Hincapié, R. (2018). Electrical consumption pattern base on meter data management system using big data techniques. Proceedings-2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017–Novem, pp. 334-339. https://doi.org/10.1109/INCISCOS.2017.19
Esmaili, M.; Shayanfar, H. A., y Gharani, K. (2014). Observability-Enhanced PMU Placement Considering Conventional Measurements and Contingencies. Iranian Journal of Electrical and Electronic Engineering, 10(4), 283-292. Recuperado de http://ijeee.iust.ac.ir/article-1-642-en.html.
García, E. M.; López, B. D. B., y Millán, I. A. I. (2018). Analysis of the Voltage Profile by the Insertion of Electric Vehicles in the Distribution Network Considering Response to Demand. Proceedings-2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017–Novem, 7-13. https://doi.org/10.1109/INCISCOS.2017.26
García, E. M., y Millán, I. A. I. (2018). Multi-objective Optimization for the Management of the Response to the Electrical Demand in Commercial Users. Proceedings-2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017–Novem, 14-20. https://doi.org/10.1109/INCISCOS.2017.25
Gou, B. (2008). Generalized integer linear programming formulation for optimal PMU placement. IEEE Transactions on Power Systems, 23(3), 1099-1104. https://doi.org/10.1109/TPWRS.2008.926475
Gou, B. (2008). Optimal Placement of PMUs by Integer Linear Programming. IEEE Transactions on Power Systems, 23(3), 1525-1526. https://doi.org/10.1109/TPWRS.2008.926723
Grigoras, G.; Cartina, G., y Gavrilas, M. (2009). Using of Clustering Techniques in Optimal Placement of Phasor Measurements Units. In 9th WSEAS/IASME International Conference on Electric Power Systems, High Voltager, Electric Machines (pp. 104-108)
Guerrón, G.; García, E., y Montero, A. (2014). Influencia de las ráfagas de viento en la calidad de la energía de los parques eólicos (Influence of wind gusts in power quality on wind farms). Enfoque UTE, 5(3), 25-44
Huang, L.; Sun, Y.; Xu, J.; Gao, W.; Zhang, J., y Wu, Z. (2014). Optimal PMU Placement Considering Controlled Islanding of Power System. IEEE Transactions on Power Systems, 29(2), pp. 742–755. https://doi.org/10.1109/TPWRS.2013.2285578
Inga, E.; Carrión, D.; Águila, A.; García, E., y Hincapié, R. (2016). Minimal Deployment and Routing Geographic of PMUs on Electrical Power System based on MST Algorithm. IEEE Latin America Transactions 14 (5), 2264–2270. https://doi.org/10.1109/TLA.2016.7530422
Inga Ortega, E.; Inga, J.; Correa, E., y Hincapié, R. (2018). Reconstrucción del patrón de consumo eléctrico a partir de Big Data mediante técnica de MapReduce. Enfoque UTE, 9(1), 177-187. https://doi.org/10.29019/enfoqueute.v9n1.220
Jaramillo, C., y Carrión, D. (2016). Ubicación óptima de PMUs basado en criterios de observabilidad y evaluación mediante Búsqueda Tabú. Universidad Politécnica Salesiana.
Jiang, Q.; Li, X.; Wang, B., y Wang, H. (2012). PMU-Based Fault Location Using Voltage Measurements in Large Transmission Networks. IEEE Transactions on Power Delivery, 27(3), 1644-1652. https://doi.org/10.1109/TPWRD.2012.2199525
Jiang, Z.; Miao, S.; Xu, H.; Liu, P., y Zhang, B. (2012). An effective fault location technique for transmission grids using phasor measurement units. International Journal of Electrical Power y Energy Systems, 42(1), 653-660. https://doi.org/10.1016/j.ijepes.2012.03.020.
Kekatos, V.; Giannakis, G. B., y Wollenberg, B. (2012). Optimal placement of phasor measurement units via convex relaxation. IEEE Transactions on Power Systems, 27(3), 1521–1530. https://doi.org/10.1109/TPWRS.2012.2185959
Ketabi, A.; Nosratabadi, S. M., y Sheibani, M. R. (2012). Optimal PMU placement with uncertainty using Pareto method. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/501893.
Korres, G. N.; Georgilakis, P. S.; Koutsoukis, N. C., y Manousakis, N. M. (2013). Numerical observability method for optimal phasor measurement units placement using recursive Tabu search method. IET Generation, Transmission y Distribution, 7(4), 347-356. https://doi.org/10.1049/iet-gtd.2012.0377
Maldonado, M. G. R. (2017). Wireless Sensor Network for Smart Home Services Using Optimal Communications. 2017 International Conference on Information Systems and Computer Science (INCISCOS), 27–32. https://doi.org/10.1109/INCISCOS.2017.21.
Manousakis, N. M.; Korres, G. N., y Georgilakis, P. S. (2011). Optimal placement of phasor measurement units: A literature review. 2011 16th International Conference on Intelligent System Applications to Power Systems, 1–6. https://doi.org/10.1109/ISAP.2011.6082183
Mccamish, B.; Kulkarni, J.; Ke, Z.; Harpool, S.; Huo, C.; Brekken, T., yYokochi, A. (2017). A rapid PMU-based load composition and PMU estimation method. Electric Power Systems Research, 143, 44–52. https://doi.org/10.1016/j.epsr.2016.10.028
Mori, H., y Tsuzuki, S. (1991). A fast method for topological observability analysis using a minimum spanning tree technique. Power Systems, IEEE Transactions On, 6(2), 491-500. https://doi.org/10.1109/59.76691
Moscoso-Zea, O., y Luján-Mora, S. (2017). Metodologías Sugeridas de Evaluación y Selección de Software de Arquitectura Empresarial para la Digitalización del Conocimiento (Suggested Methodologies for Evaluation and Selection of Enterprise Architecture Software for Knowledge Digitization). Enfoque UTE, 7(1), 315–328. https://doi.org/10.29019/enfoqueute.v8n1.144
Nuqui, R. F., y Phadke, A. G. (2005). Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Transactions on Power Delivery, 20(4), 2381-2388. https://doi.org/10.1109/TPWRD.2005.855457
Paudel, J., Xu, X., Balasubramaniam, K., y Makram, E. B. (2015). A Strategy for PMU Placement Considering the Resiliency of Measurement System, (November), 29–36.
Rahman, N. H. A., y Zobaa, A. F. (2016). Optimal PMU placement using topology transformation method in power systems. Journal of Advanced Research, 7(5), 625-634. https://doi.org/10.1016/j.jare.2016.06.003
Ruíz, M.; Masache, P., y Dominguez, J. (2018). High Availability Network for Critical Communications on Smart Grids, (Ssn), 1-5.
Sánchez, A., y Carrión, D. (2017). Modeling of the Behavior Power Flow on Transmission Lines Based on Voltage and Current Synchronopasors. IEEE Latin America Transactions, 16(4), 1142-1149. https://doi.org/10.1109/TLA.2018.8362149
Sodhi, R., Srivastava, S. C., y Singh, S. N. (2009). Optimal PMU placement to ensure system observability under contingencies. 2009 IEEE Power and Energy Society General Meeting, PES ’09. https://doi.org/10.1109/PES.2009.5275618
Song, Y.; Ma, S.; Wu, L.; Quan, W., y He, H. (2009). PMu placement based on power system characteristics. 1st International Conference on Sustainable Power Generation and Supply, SUPERGEN ’09, 1-6. https://doi.org/10.1109/SUPERGEN.2009.5348359
Srivastava, A. (2015). Optimal PMU Placement for Complete Power System Observability using Binary Cat Swarm Optimization, 0-5.
Taher, S. A.; Mahmoodi, H., y Aghaamouei, H. (2016). Optimal PMU location in power systems using MICA. Alexandria Engineering Journal, 55(1), 399–406. https://doi.org/10.1016/j.aej.2015.12.002
Werho, T.; Member, S.; Vittal, V.; Kolluri, S., y Wong, S. M. (2016). Power System Connectivity Monitoring Using a Graph Theory Network Flow Algorithm, 1-8.
Zhu, H. L.; Duan, Y. X.; Zhang, X. P.; Qi, H., y Huang, C. X. (2013). Hybrid of MST and genetic algorithm on minimizing PMU placement. Proceedings of the 2013 3rd International Conference on Intelligent System Design and Engineering Applications, ISDEA 2013, (2), 820-823. https://doi.org/10.1109/ISDEA.2012.195
Published
2019-03-29
How to Cite
Carrión Galarza, D. F., & González Sánchez, J. W. (2019). Optimal PMU placement considering N-1contingencies constraints in electrical power systems. Enfoque UTE, 10(1), pp. 1 – 12. https://doi.org/10.29019/enfoqueute.v10n1.437
Issue
Section
Automation and Control, Mechatronics, Electromechanics, Automotive
License
The articles and research published by the UTE University are carried out under the Open Access regime in electronic format. This means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.
The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.
This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).
The Enfoque UTE journal guarantees and declares that authors always retain all copyrights and full publishing rights without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.
Enfoque UTE - Facultad de Ciencias de la Ingeniería e Industrias - Universidad UTE
