SVC implementation using neural networks for an AC electrical railway

Authors:
Saeid Veysi Raygani;Bijan Moaveni;Seyed Saeed Fazel;Amir Tahavorgar
Affiliations:
Electrical Railway Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran;Electrical Railway Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran;Electrical Railway Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran;Electrical Railway Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Venue:
WSEAS Transactions on Circuits and Systems
Year:
2011

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Abstract

This paper presents an on-line method for implementation of a static var compensator (SVC) in a real ac autotransformer (AT)-fed electrical railway for reactive power compensation using Neural Networks (NN). Genetic algorithm (GA) can be the off-line minimizing function for reactive power compensation. Consequently, the nonlinear auto-regressive model with exogenous Inputs networks in series-parallel arrangement (NARXSP) is implemented as a predictor and methodology in order to diminish calculation time and making this method practicable. To study load flow and reactive power compensation for this unique system, forward/backward sweep (FBS) load flow method is applied. MATLAB software is used for programming and simulations.