Ampacity forecasting using neural networks
Ampacity forecasting using neural networks
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.
This work was supported by the Spanish Government under the R+D initiative INNPACTO with reference IPT-2011-1447-920000.
Neural networks (NNs), Grid integration, Ampacity, Monitoring system, Wind energy
Neural networks (NNs), Grid integration, Ampacity, Monitoring system, Wind energy
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