Comparison of ANN and ANFIS Methods for the Voltage-Drop Prediction on an Electric Railway Line

Year 2018, Volume: 18 Issue: 1, 26 - 35, 23.02.2018

İlhan Kocaarslan Mehmet Taciddin Akçay , Abdurrahim Akgündoğdu Hasan Tiryaki

Abstract

Railway electrification
systems are designed with regard to the operating data and design parameters.
The minimum voltage rating required by traction during the operation should be
provided. The maximum voltage drop on a line determines the minimum traction
voltage. This voltage should be maintened within certain limits for the
continuity of operation. In this study, the maximum voltage drop generated via
traction was determined using artificial neural network (ANN) and adaptive
neuro-fuzzy inference system (ANFIS) for a 25-kV AC-supplied railway. The
voltage drop on line was calculated with regard to the operating data using ANN
and ANFIS. ANN and ANFIS were explained, and the results were compared. The
Levenberg–Marquardt (LM) algorithm was used for the ANN model. The LM algorithm
is preferred because of the speed and stability it provides for the training of
ANNs. The data created for one-way supply status were examined for simulation. 

Keywords

ANFIS, ANN, electrification, railway, traction

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