Fast estimation of the relation between aggregated train power system information and the power and energy converted

Transports on rail are increasing and major investments in the railway infrastructure, including the Railway Power Supply System (RPSS), are expected. The future railway power demands are naturally not known for certain. The more remote the uncertain future, the greater the number of scenarios that have to be considered. Large numbers of scenarios make time demanding simulations unattractive. The aim of this paper is to present a fast approximator that uses aggregated RPSS information. Since the electrical and mechanical relations governing an RPSS are quite intricate, an approximator based on neural networks (NN), is applied. This paper presents a design suggestion for an NN estimating the power and energy flows through each converter station, given RPSS data and levels of train traffic. Even if the future usage of the NN is investment planning, the modeling of such an approximator has a value in itself concerning the understanding of the relations between RPSS and train traffic.