المؤلفون: Riego Martínez, Jesús, Pérez Alonso, Marcelo, Duque Pérez, Óscar

مصطلحات موضوعية: Railway engineering, Rail transportation, Energy consumption, 3323 Tecnología de Los Ferrocarriles, 3322.04 Transmisión de Energía

وصف الملف: application/pdf

Relation: https://www.sciencedirect.com/science/article/pii/S0142061524003442; https://doi.org/10.1016/j.ijepes.2024.110123; International Journal of Electrical Power & Energy Systems, septiembre 2024, vol. 160, 110123; https://uvadoc.uva.es/handle/10324/73153; 110123; International Journal of Electrical Power & Energy Systems; 160

الاتاحة: https://uvadoc.uva.es/handle/10324/73153
https://doi.org/10.1016/j.ijepes.2024.110123

المؤلفون: Davila Sacoto, Miguel, Hernández Callejo, Luis, Luis Gerardo, Gonzalez, Duque-Perez, Oscar, Zorita-Lamadrid, Angel, Ochoa Correa, Danny

المصدر: Sensors, 24(12), (2024-06-10)

Relation: https://doi.org/10.3390/s24123768; oai:zenodo.org:13853292

الاتاحة: https://doi.org/10.3390/s24123768

المؤلفون: Arranz-Gimon, Angel, Morinigo-Sotelo, Daniel, Zorita-Lamadrid, Angel, Duque-Perez, Oscar

المصدر: IEEE Access ; page 1-1 ; ISSN 2169-3536

الاتاحة: http://dx.doi.org/10.1109/access.2024.3474176
http://xplorestaging.ieee.org/ielx8/6287639/6514899/10705286.pdf?arnumber=10705286

المؤلفون: Dávila-Sacoto, Miguel, González, L. G., Hernández-Callejo, Luis, Duque-Perez, Óscar, Zorita-Lamadrid, Ángel L., Víctor Alonso-Gómez, Espinoza, J. L.

المصدر: Electronics, 12(11), 2415, (2023-05-23)

Relation: https://doi.org/10.3390/electronics12112415; oai:zenodo.org:11297620

الاتاحة: https://doi.org/10.3390/electronics12112415

المؤلفون: Henao-Gallo, Julián Andrés, Duque-Pérez, Oscar Mauricio

المساهمون: Giraldo-Sánchez, Carlos Eduardo, Universidad Católica de Oriente. Facultad de Ciencias Agropecuarias

مصطلحات موضوعية: Control biológico, Depredador, Parasitoide, Liriomyza, Colombia, Biological control, Predator, Parasitoid, Moscas, Dípteros, Plagas de campo, Patología vegetal, Fitopatología, Insectos nocivos para la agricultura

جغرافية الموضوع: Sudamérica, Colombia, Rionegro, Antioquia

وصف الملف: 13p.; application/pdf

Relation: https://repositorio.uco.edu.co; https://repositorio.uco.edu.co/jspui/handle/20.500.13064/1817; Henao Gallo, Julián Andrés; Duque Pérez, Oscar Mauricio. Efecto de la aspiración mecánica sobre minadores (Diptera: Agromyzidae) y sus enemigos naturales, en un cultivo de crisantemo del oriente antioqueño (Trabajo de grado) Rionegro, Antioquia: Universidad Católica de Oriente; 2022. 13p.

الاتاحة: https://repositorio.uco.edu.co/jspui/handle/20.500.13064/1817
https://hdl.handle.net/20.500.13064/1817
https://repositorio.uco.edu.co

المؤلفون: Deza Blanco, Mario, Duque Pérez, Óscar, Universidad de Valladolid. Escuela de Ingenierías Industriales

مصطلحات الفهرس: Electricidad, Armónicos, Generación fotovoltaica, Irradiancia, MATLAB, 3322.01 Distribución de la Energía, info:eu-repo/semantics/bachelorThesis

URL: https://worldcat.org/search?q=on:ESUDE+http://uvadoc.uva.es/oai/request+DCG_ENTIRE_REPOSITORY+CNTCOLL

المؤلفون: Gutiérrez Martín, Antonio, Duque Pérez, Óscar, Universidad de Valladolid. Escuela de Ingenierías Industriales

مصطلحات الفهرس: Autoconsumo, Energía Fotovoltaica, Energía Renovable, Hospital, Conexión a red, 3322.02 Generación de Energía, info:eu-repo/semantics/bachelorThesis

URL: https://worldcat.org/search?q=on:ESUDE+http://uvadoc.uva.es/oai/request+DCG_ENTIRE_REPOSITORY+CNTCOLL

المؤلفون: Gándara González, Óscar, Duque Pérez, Óscar, Universidad de Valladolid. Escuela de Ingenierías Industriales

مصطلحات الفهرس: Historia, Electricidad, Magnetismo, Relatividad especial, 2202.03 Electricidad, info:eu-repo/semantics/bachelorThesis

URL: https://worldcat.org/search?q=on:ESUDE+http://uvadoc.uva.es/oai/request+DCG_ENTIRE_REPOSITORY+CNTCOLL

المؤلفون: Bonet Jara, Jorge, Morinigo-Sotelo, Daniel, Duque-Perez, Oscar, Serrano-Iribarnegaray, Luis, Pons-Llinares, Joan

المصدر: IEEE Transactions on Industry Applications, 58(4), 4522 - 4531, (2022-07-22)

Relation: https://doi.org/10.1109/TIA.2022.3166876; oai:zenodo.org:13838574

الاتاحة: https://doi.org/10.1109/TIA.2022.3166876

المؤلفون: Mariano, Deyslen, Hernández-Callejo, Luis, Solís, Martín, Zorita-Lamadrid, Angel, Duque-Perez, Oscar, González, L. G., Alonso-Gómez, Víctor, Jaramillo Duque, Alvaro, Santos García, F.

المصدر: Energy Science & Engineering, 10(12), 4694-4707, (2022-08-22)

Relation: https://doi.org/10.1002/ese3.1298; oai:zenodo.org:11298223

الاتاحة: https://doi.org/10.1002/ese3.1298

المؤلفون: Arranz Gimón, Ángel Eugenio, Zorita Lamadrid, Ángel Luis, Moríñigo Sotelo, Daniel, Duque Pérez, Óscar

مصطلحات موضوعية: Electric power, Energía eléctrica, Hann function, Función Hann, Adjustable speed drive, Variadores de velocidad, Induction motors, Motores de inducción, 3306 Ingeniería y Tecnología Eléctricas

وصف الملف: application/pdf

Relation: https://doi.org/10.1016/j.epsr.2022.107833; Electric Power Systems Research, 2022, vol. 206, 107833; https://uvadoc.uva.es/handle/10324/52105

الاتاحة: https://uvadoc.uva.es/handle/10324/52105
https://doi.org/10.1016/j.epsr.2022.107833

المؤلفون: Mariano-Hernández, Deyslen, Hernández Callejo, Luis, Solís, Martín, Zorita Lamadrid, Angel Luis, Duque-Perez, Oscar, Gonzalez Morales, Luis Gerardo, Santos Garcia, Felix, Jaramillo Duque, Álvaro, Ospino C., Adalberto, Alonso Gómez, Víctor, Bello, Hugo J.

المصدر: https://www.mdpi.com/2071-1050/14/10/5857.

مصطلحات موضوعية: Drift detection, Electrical consumption forecasting, Energy forecasting, Machine learning, Smart buildings

وصف الملف: 14 páginas; application/pdf

Relation: Sustainability; 1. IEA Tracking Buildings. 2021. Available online: https://www.iea.org/reports/tracking-buildings-2021 (accessed on 16 March 2022).; 2. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Guz, Ł.; Bocian, M.; Balaras, C.A. An easy and widelapplicable forecast control for heating systems in existing and new buildings: First field experiences. J. Clean. Prod. 2022, 352, 131605. [CrossRef]; 3. Devagiri, V.M.; Boeva, V.; Abghari, S.; Basiri, F.; Lavesson, N. Multi-view data analysis techniques for monitoring smart building systems. Sensors 2021, 21, 6775. [CrossRef] [PubMed]; 4. Izidio, D.M.; de Mattos Neto, P.S.; Barbosa, L.; de Oliveira, J.F.; Marinho, M.H.D.N.; Rissi, G.F. Evolutionary hybrid system for energy consumption forecasting for smart meters. Energies 2021, 14, 1794. [CrossRef]; 5. Hong, T.; Wang, Z.; Luo, X.; Zhang, W. State-of-the-art on research and applications of machine learning in the building life cycle. Energy Build. 2020, 212, 109831. [CrossRef]; 6. Kim, J.Y.; Cho, S.B. Electric energy consumption prediction by deep learning with state explainable autoencoder. Energies 2019, 12, 739. [CrossRef]; 7. Zeng, A.; Ho, H.; Yu, Y. Prediction of building electricity usage using Gaussian Process Regression. J. Build. Eng. 2020, 28, 101054. [CrossRef]; 8. Xu, W.; Peng, H.; Zeng, X.; Zhou, F.; Tian, X.; Peng, X. A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl. Intell. 2019, 49, 3002–3015. [CrossRef]; 9. Cholewa, T.; Siuta-Olcha, A.; Smolarz, A.; Muryjas, P.; Wolszczak, P.; Anasiewicz, R.; Balaras, C.A. A simple building energy model in form of an equivalent outdoor temperature. Energy Build. 2021, 236, 110766. [CrossRef]; 11. Iwashita, A.S.; Papa, J.P. An Overview on Concept Drift Learning. IEEE Access 2019, 7, 1532–1547. [CrossRef]; 12. Baier, L.; Kühl, N.; Satzger, G.; Hofmann, M.; Mohr, M. Handling concept drifts in regression problems—the error intersection approach. In WI2020 Zentrale Tracks; GITO Verlag: Berlin, Germany, 2020; pp. 210–224.; 13. Kahraman, A.; Kantardzic, M.; Kahraman, M.; Kotan, M. A data-driven multi-regime approach for predicting energy consumption. Energies 2021, 14, 6763. [CrossRef]; 14. Webb, G.I.; Lee, L.K.; Goethals, B.; Petitjean, F. Analyzing concept drift and shift from sample data. Data Min. Knowl. Discov. 2018, 32, 1179–1199. [CrossRef]; 15. Lu, J.; Liu, A.; Dong, F.; Gu, F.; Gama, J.; Zhang, G. Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 2019, 31, 2346–2363. [CrossRef]; 16. Brzezinski, D.; Stefanowski, J. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 2014, 25, 81–94. [CrossRef]; 17. Wadewale, K.; Desai, S.; Tennant, M.; Stahl, F.; Rana, O.; Gomes, J.B.; Thakre, A.A.; Redes, E.M.; Padmalatha, E.; Rani, P.; et al. Survey on Method of Drift Detection and Classification for time varying data set. Comput. Biol. Med. 2016, 32, 1–7.; 18. Khezri, S.; Tanha, J.; Ahmadi, A.; Sharifi, A. A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams. Neurocomputing 2021, 442, 125–145. [CrossRef]; 19. Fekri, M.N.; Patel, H.; Grolinger, K.; Sharma, V. Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network. Appl. Energy 2021, 282, 116177. [CrossRef]; 20. Jagait, R.K.; Fekri, M.N.; Grolinger, K.; Mir, S. Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA. IEEE Access 2021, 9, 98992–99008. [CrossRef]; 21. Fenza, G.; Gallo, M.; Loia, V. Drift-aware methodology for anomaly detection in smart grid. IEEE Access 2019, 7, 9645–9657. [CrossRef]; 22. Mehmood, H.; Kostakos, P.; Cortes, M.; Anagnostopoulos, T.; Pirttikangas, S.; Gilman, E. Concept drift adaptation techniques in distributed environment for real-world data streams. Smart Cities 2021, 4, 349–371. [CrossRef]; 23. Ceci, M.; Corizzo, R.; Japkowicz, N.; Mignone, P.; Pio, G. ECHAD: Embedding-Based Change Detection from Multivariate Time Series in Smart Grids. IEEE Access 2020, 8, 156053–156066. [CrossRef]; 24. Yang, Z.; Al-Dahidi, S.; Baraldi, P.; Zio, E.; Montelatici, L. A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments. IEEE Trans. Neural Netw. Learn. Syst. 2020, 31, 309–320. [CrossRef] [PubMed]; 25. Silva, R.P.; Zarpelão, B.B.; Cano, A.; Barbon Junior, S. Time series segmentation based on stationarity analysis to improve new samples prediction. Sensors 2021, 21, 7333. [CrossRef] [PubMed]; 26. Heusinger, M.; Raab, C.; Schleif, F.M. Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl. 2022, 34, 89–100. [CrossRef]; 27. Raab, C.; Heusinger, M.; Schleif, F.M. Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing 2020, 416, 340–351. [CrossRef]; 28. Togbe, M.U.; Chabchoub, Y.; Boly, A.; Barry, M.; Chiky, R.; Bahri, M. Anomalies detection using isolation in concept-drifting data streams. Computers 2021, 10, 13. [CrossRef]; 29. Moon, J.; Park, S.; Rho, S.; Hwang, E. A comparative analysis of artificial neural network architectures for building energy consumption forecasting. Int. J. Distrib. Sens. Netw. 2019, 15, 155014771987761. [CrossRef]; 30. Kiprijanovska, I.; Stankoski, S.; Ilievski, I.; Jovanovski, S.; Gams, M.; Gjoreski, H. HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning. Energies 2020, 13, 2672. [CrossRef]; 31. Zor, K.; Çelik, Ö.; Timur, O.; Teke, A. Short-term building electrical energy consumption forecasting by employing gene expression programming and GMDH networks. Energies 2020, 13, 1102. [CrossRef]; 32. Li, Z.; Friedrich, D.; Harrison, G.P. Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model. Energies 2020, 13, 780. [CrossRef]; 33. Culaba, A.B.; Del Rosario, A.J.R.; Ubando, A.T.; Chang, J.-S. Machine learning-based energy consumption clustering and forecasting for mixed-use buildings. Int. J. Energy Res. 2020, 44, 9659–9673. [CrossRef]; 34. Wang, Z.; Wang, Y.; Zeng, R.; Srinivasan, R.S.; Ahrentzen, S. Random Forest based hourly building energy prediction. Energy Build. 2018, 171, 11–25. [CrossRef]; 35. Sauer, J.; Mariani, V.C.; dos Santos Coelho, L.; Ribeiro, M.H.D.M.; Rampazzo, M. Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings. Evol. Syst. 2021, 1–12. [CrossRef]; 36. Bassi, A.; Shenoy, A.; Sharma, A.; Sigurdson, H.; Glossop, C.; Chan, J.H. Building energy consumption forecasting: A comparison of gradient boosting models. In Proceedings of the 12th International Conference on Advances in Information Technology, Bangkok, Thailand, 29 June–1 July 2021. [CrossRef]; 37. Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Perez, O.; Gonzalez-Morales, L.; SantosGarcía, F. A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings. Appl. Sci. 2021, 11, 7886. [CrossRef]; 38. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J. Build. Eng. 2022, 45, 103406. [CrossRef]; 39. Lemos, V.H.B.; Almeida, J.D.S.; Paiva, A.C.; Junior, G.B.; Silva, A.C.; Neto, S.M.B.; Lima, A.C.M.; Cipriano, C.L.S.; Fernandes, E.C.; Silva, M.I.A. Temporal convolutional network applied for forecasting individual monthly electric energy consumption. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 2002–2007.; 40. Bendaoud, N.M.M.; Farah, N. Using deep learning for short-term load forecasting. Neural Comput. Appl. 2020, 32, 15029–15041. [CrossRef]; 41. Gao, Y.; Ruan, Y.; Fang, C.; Yin, S. Deep learning and transfer learning models of energy consumption forecasting for a building with poor information data. Energy Build. 2020, 223, 110156. [CrossRef]; 42. Bifet, A.; Gavaldà, R. Learning from time-changing data with adaptive windowing. In Proceedings of the 7th SIAM International Conference on Data Mining, Minneapolis, MN, USA, 26–28 April 2007; pp. 443–448.; 43. Moon, J.; Kim, Y.; Son, M.; Hwang, E. Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron. Energies 2018, 11, 3283. [CrossRef]; 44. Khosravani, H.; Castilla, M.; Berenguel, M.; Ruano, A.; Ferreira, P. A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building. Energies 2016, 9, 57. [CrossRef]; 45. Ali, U.; Shamsi, M.H.; Bohacek, M.; Hoare, C.; Purcell, K.; Mangina, E.; O’Donnell, J. A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings. Appl. Energy 2020, 267, 114861. [CrossRef]; 46. Andelkovi´c, A.S.; Bajatovi´c, D. Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas ¯ consumption prediction. J. Clean. Prod. 2020, 266, 122096. [CrossRef]; 14; 10; Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857; https://hdl.handle.net/11323/9472; https://doi.org/10.3390/su14105857; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.co/

الاتاحة: https://hdl.handle.net/11323/9472
https://doi.org/10.3390/su14105857
https://repositorio.cuc.edu.co/

المؤلفون: De Boni, Gabriele, Fernandez-Cavero, Vanesa, Frosini, Lucia, Duque-Perez, Oscar, Morinigo-Sotelo, Daniel

المساهمون: De Boni, Gabriele, Fernandez-Cavero, Vanesa, Frosini, Lucia, Duque-Perez, Oscar, Morinigo-Sotelo, Daniel

مصطلحات موضوعية: induction motor, closed-loop control, inverter, fault detection, misalignment

وصف الملف: ELETTRONICO

Relation: info:eu-repo/semantics/altIdentifier/isbn/979-8-3503-2077-0; ispartofbook:2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED); 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED); firstpage:443; lastpage:449; numberofpages:7; https://hdl.handle.net/11571/1489876; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85175252847

الاتاحة: https://hdl.handle.net/11571/1489876
https://doi.org/10.1109/SDEMPED54949.2023.10271455

المؤلفون: Fernandez-Cavero, Vanesa, García Escudero, Luis Ángel, Pons-Llinares, Joan, Fernández Temprano, Miguel Alejandro, Duque Pérez, Óscar, Moríñigo Sotelo, Daniel

مصطلحات موضوعية: induction motors, transient analysis, fault diagnosis, functional ANOVA

وصف الملف: application/pdf

Relation: https://www.mdpi.com/2076-3417/11/9/3769; https://doi.org/10.3390/app11093769; https://uvadoc.uva.es/handle/10324/65692; 11; 12; Applied Sciences

الاتاحة: https://uvadoc.uva.es/handle/10324/65692
https://doi.org/10.3390/app11093769

المؤلفون: Mariano-Hernández, Deyslen, Hernández Callejo, Luis, Solis, Martín, Zorita Lamadrid, Ángel Luis, Duque Pérez, Óscar, González-Morales, Luis, Santos García, Félix

مصطلحات موضوعية: Ingeniería Eléctrica, Modelos de previsión. Consumo de energía eléctrica. Previsión a corto plazo. Edificio inteligente, Forecasting models, energy consumption, multi-step forecasting, short-term forecasting, smart building, 3306 Ingeniería y Tecnología Eléctricas

وصف الملف: application/pdf

Relation: https://www.mdpi.com/2076-3417/11/17/7886; https://doi.org/10.3390/app11177886; Applied Sciences, vol 11, n 17, Agosto 2021; https://uvadoc.uva.es/handle/10324/64657; 17; 11

الاتاحة: https://uvadoc.uva.es/handle/10324/64657
https://doi.org/10.3390/app11177886

المؤلفون: Fernández Cavero, Vanessa, Pons Llinares, Joan, Duque Pérez, Óscar, Moríñigo Sotelo, Daniel

مصطلحات موضوعية: time-frequency analysis, transient analysis, fault diagnosis, broken rotor bar, induction motors, inverters

وصف الملف: application/pdf

Relation: https://www.sciencedirect.com/science/article/pii/S0019057820304158?via%3Dihub; https://doi.org/10.1016/j.isatra.2020.10.020; ISA Transactions, vol. 109, 2021, Pages 352-367; https://uvadoc.uva.es/handle/10324/64562; ISA Transactions; 109

الاتاحة: https://uvadoc.uva.es/handle/10324/64562
https://doi.org/10.1016/j.isatra.2020.10.020

المؤلفون: Bazán, Gustavo Henrique, Goedtel, Alessandro, Duque Pérez, Óscar, Moríñigo Sotelo, Daniel

مصطلحات موضوعية: Ingeniería eléctrica, Motores de inducción, Multi-fault diagnosis, Principal component analysis, Pattern recognition, Diagnóstico multifallo, Análisis de componentes principales, Reconocimiento de patrones, 3306 Ingeniería y Tecnología Eléctricas

وصف الملف: application/pdf

Relation: https://www.mdpi.com/2079-9292/10/12/1462; https://doi.org/10.3390/electronics10121462; Electronics, 2021, vol. 10, n. 12, 1462; https://uvadoc.uva.es/handle/10324/59546; 1462; 12; Electronics; 10

الاتاحة: https://uvadoc.uva.es/handle/10324/59546
https://doi.org/10.3390/electronics10121462

المؤلفون: Merizalde Zamora, Yury Humberto, Hernández Callejo, Luis, Duque Pérez, Óscar, Alonso Gómez, Víctor

مصطلحات موضوعية: Wind turbines, Artificial intelligence, Inteligencia artificial, Motores de inducción, Faults diagnostic, Synthetic data, 3313.30 Turbinas

وصف الملف: application/pdf

Relation: https://www.mdpi.com/2076-3417/11/15/6942; https://doi.org/10.3390/app11156942; Applied Sciences, 2021, Vol. 11, Nº. 15, 6942; https://uvadoc.uva.es/handle/10324/59537; 6942; 15; Applied Sciences; 11

الاتاحة: https://uvadoc.uva.es/handle/10324/59537
https://doi.org/10.3390/app11156942

المؤلفون: Rubia Herrera, Antonio de la, Zorita Lamadrid, Ángel Luis, Duque Pérez, Óscar, Moríñigo Sotelo, Daniel

مصطلحات موضوعية: Scontrol strategies, Medium voltage direct current (MVDC), Optimize power grid, Three-phase short-circuit, Unified power flow converter (UPFC), Voltage sourced converter (VSC), 33 Ciencias Tecnológicas

وصف الملف: application/pdf

Relation: https://doi.org/10.1002/2050-7038.13038; International Transactions on Electrical Energy Systems, 2021, e13038.; https://uvadoc.uva.es/handle/10324/48145; International Transactions on Electrical Energy Systems

الاتاحة: https://uvadoc.uva.es/handle/10324/48145
https://doi.org/10.1002/2050-7038.13038

المؤلفون: Izquierdo Monge, Óscar, Peña Carro, Paula, Villafafila Robles, Roberto, Duque Pérez, Óscar, Zorita Lamadrid, Ángel Luis, Hernández Callejo, Luis

مصطلحات موضوعية: Microgrids (Smart power grids), Redes eléctricas (Energía), Electric power systems - Control, Control automático, Renewable energy resources, Home Assistant, Asistente de hogar, 3306 Ingeniería y Tecnología Eléctricas

وصف الملف: application/pdf

Relation: https://www.mdpi.com/2076-3417/11/11/5012; https://doi.org/10.3390/app11115012; Applied Sciences, 2021, Vol. 11, Nº. 11, 5012; https://uvadoc.uva.es/handle/10324/59770; 5012; 11; Applied Sciences

الاتاحة: https://uvadoc.uva.es/handle/10324/59770
https://doi.org/10.3390/app11115012