Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Maria Paz Comech"'
Autor:
Maria Teresa Villen Martinez, Maria Paz Comech, Anibal Antonio Prada Hurtado, Miguel Angel Olivan, David Lopez Corton, Carlos Rodriguez Del Castillo
Publikováno v:
IEEE Access, Vol 12, Pp 11549-11560 (2024)
The IEC 61850 standard has brought new opportunities for developing Centralized Substation Protection and Control (CPC) platforms to be used in digital substations. The CPC operation requires a proper signal processing module that provides the necess
Externí odkaz:
https://doaj.org/article/d737ebe8c53f4736ae39d64ab441ce3a
Autor:
Marta Bernal-Sancho, Roberto Rocca, Gregorio Fernandez-Aznar, Maria Paz Comech, Noemi Galan-Hernandez
Publikováno v:
IEEE Access, Vol 11, Pp 76768-76780 (2023)
Vehicle-to-Grid chargers (V2Gs) are currently considered a key component for supporting future power systems in primary and especially secondary frequency regulation. Most of the research works conducted so far on this topic are based on a vehicle-ce
Externí odkaz:
https://doaj.org/article/6e55ca9a6b764b039f5872396db8daa9
Autor:
María Teresa Villén, Maria Paz Comech, Eduardo Martinez Carrasco, Aníbal Antonio Prada Hurtado
Publikováno v:
Energies, Vol 15, Iss 16, p 6018 (2022)
Renewable power is expected to increase drastically in the coming years due to the energy transition. A large part of the newly installed generators will be connected to the power system through inverters and electronic converters, whose behaviour di
Externí odkaz:
https://doaj.org/article/aaf73cc10b0948f19963cafb9772c723
Autor:
Maria Teresa Villen, Maria Paz Comech, Anibal Prada, Miguel Angel Olivan, Carlos Rodriguez del Castillo, David Lopez Corton, Ruben Andrino Gallego
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8739f45ba1efd5d410cb63a4193c5f37
https://doi.org/10.2139/ssrn.4408300
https://doi.org/10.2139/ssrn.4408300
Autor:
Eduardo Martinez Carrasco, Samuel Borroy Vicente, Maria Teresa Villén Martínez, Maria Paz Comech Moreno
Publikováno v:
Energies
Zaguán. Repositorio Digital de la Universidad de Zaragoza
instname
Energies, Vol 13, Iss 3, p 558 (2020)
Energies; Volume 13; Issue 3; Pages: 558
Zaguán. Repositorio Digital de la Universidad de Zaragoza
instname
Energies, Vol 13, Iss 3, p 558 (2020)
Energies; Volume 13; Issue 3; Pages: 558
The high penetration of renewable energies will affect the performance of present protection algorithms due to fault current injection from generators based on power electronics. This paper explains the process followed for analyzing this effect on d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fd3199981ceda897776c16be7313030
http://zaguan.unizar.es/record/88246
http://zaguan.unizar.es/record/88246
Publikováno v:
Zaguán. Repositorio Digital de la Universidad de Zaragoza
instname
instname
This work proposes a methodology to automate the recognition of Partial Discharges (PD) sources in Electrical Distribution Networks using a Deep Neural Network (DNN) model called Convolutional Autoencoder (CAE), which is able to automatically extract
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f0d1a1e3c1fa4700f8522571156fd5c
http://zaguan.unizar.es/record/108362
http://zaguan.unizar.es/record/108362
Publikováno v:
2017 International Conference on Information Systems and Computer Science (INCISCOS).
Awareness concerning the global warming and environmental issues has been a trigger to expand the utilization of renewable energies replacing electricity from fossil fuels. The deployment and integration of cleaner and eco-friendly generators to the
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2638 (2024)
The electric vehicle deployment, due to the plans defined according to the energy transition objectives, produces new challenges for the electrical system. These challenges are associated with the charging infrastructure of these vehicles since they
Externí odkaz:
https://doaj.org/article/5b0b59285cd148e4b3b86c3c7e488f6a
Publikováno v:
Energies, Vol 12, Iss 13, p 2485 (2019)
This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems.
Externí odkaz:
https://doaj.org/article/c09868d545574e98bfbec86927781411