Ampacity forecasting using neural networks

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Identificadores
URI: http://hdl.handle.net/10902/9865
ISBN: 978-84-616-8196-9
ISSN: 2172-038X
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Autoría
Martínez Torre, RaquelAutoridad Unican; González Diego, AntonioAutoridad Unican; Madrazo Maza, AlfredoAutoridad Unican; Mañana Canteli, MarioAutoridad Unican; Arroyo Gutiérrez, AlbertoAutoridad Unican; Cavia Soto, María de los ÁngelesAutoridad Unican; Domingo Fernández, RodrigoAutoridad Unican; Sierra Molleda, Alberto; Laso Pérez, AlbertoAutoridad Unican
Fecha
2014-04
Derechos
© The European Association for the Development of Renewable Energies, Environment and Power Quality (EA4EPQ)
Publicado en
Renewable Energy & Power Quality Journal, 2014, Vol.1 (12), 120-123
International Conference on Renewable Energies and Power Quality (ICREPQ’14), Córdoba
Editorial
The European Association for the Development of Renewable Energies, Environment and Power Quality (EA4EPQ)
Palabras clave
Wind energy
Ampacity
Neural networks (NNs)
Grid integration
Monitoring system
Resumen/Abstract
Ampacity techniques have been used by Distributor System Operators (DSO) and Transport System Operators (TSO) in order to increase the static rate of transport and distribution infrastructures, especially those who are used for the grid integration of renewable energy. One of the main drawbacks of this technique is related with the fact that DSO and TSO need to do some planning tasks in advance. In order to perform a previous planning it is compulsory to forecast the weather conditions in the short-time. This paper analyses the application of the neural network to the estimation of the ampacity in order to increase the amount of power produced by wind farms that can be integrated into the grid.
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