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In this study, the determination of the maximum value of the voltage drop created by the traction force was performed for a 1500 V DC-fed rail system by means of the artificial neural networks (ANN) and support vector machines (SVM). The voltage drop occurring on the line was calculated with regard to the operating parameters by means of the ANN and SVM. The ANN and SVM were explained and a comparison was made. The Levenberg-Marquardt (LM) algorithm was used for the ANN model. The Levenberg-Marquardt algorithm was preferred due to the speed and stability it provides for the training of artificial neural networks. The electrification system in the rail systems is designed with regard to the operating data and design parameters. The minimum voltage rating that the traction force requires during the operation needs to be provided in the electrification system. The highest value of the voltage drop on the line determines the lowest value of the traction force voltage. This value must be in certain limits for the continuity of the operation. The created datas regarding one-way and two-way supply conditions were examined for simulation. In this way railway electrification traction power simulation was done by artificial intelligence methods and traction power simulation results were predicted without doing traction simulation. With the designed system, the success rate is over %95.

The Comparison of the ANN and SVM Methods for the Prediction of Voltage Drop on a Subway Line