Fast Estimation of Relations Between Aggregated Train Power System Data and Traffic Performance

Abstract

Abstract-Transports on rail are increasing and major railway infrastructure investments are expected. An important part of this infrastructure is the railway power supply system. The future railway power demands are not known. The more distant the uncertain future, the greater the number of scenarios that have to be considered. Large numbers of scenarios make time demanding (some minutes, each) full simulations of electric railway power systems less attractive and simplifications more so.The aim, and main contribution, of this paper is to propose a fast approximator, that uses aggregated traction system information as inputs and outputs. This approximator can be used as an investment planning constraint in the optimization. It considers that there is a limit on the intensity of the train traffic, depending on the strength of the power system. This approximator approach is not encountered before in the literature.In the numerical example of this paper, the approximator inputs are the power system configuration; the distance between a connection from contact line to the public grid, to another connection, or to the end of the contact line; the average values and the standard deviations of the inclinations of the railway; the average number of trains; and the average velocity of them, for that distance. The output is the maximal attainable average velocity of an added train for the by the inputs described railway power system section.The approximator facilitates studies of many future railway power system loading scenarios, combined with different power system configurations, for investment planning analysis. The approximator is based on neural networks.An additional value of the approximator is that it provides an understanding of the relations between power system configuration and train traffic performance.

On the Use of Graph Theory for Railway Power Supply Systems Characterization

“…Comparing both schemes, it can be observed that edge (6, 7) is invariant. Edges (1, 5), (3,5) and (2, 7) also appear in both instants. Figure 14 shows the graphs corresponding to the two instants.…”

“…It can be observed that the edge representing the adjacency (6,7), is e 19 in both cases. Furthermore, the adjacencies appearing in both cases ((1, 5), (3,5) and (2, 7)) are represented by the same edges (e 4 , e 13 and e 11 ). Table 4 shows the "active" and "non active" edges at each instant and Fig.…”

“…For this reason, large efforts have been done to obtain an accurate, robust, easy to implement and computationally light power flow method [3,5,[26][27][28]30]. In [2], an unified AD/DC power flow method to simultaneously solve the whole equation system was proposed.…”

BFS Algorithm for Voltage-Constrained Meshed DC Traction Networks With Nonsmooth Voltage-Dependent Loads and Generators

“…The matrix equation (3) represents the KCL in all nodes. It has n N equations and (n N + n B ) unknowns, being n N the number of nodes and n B the number of branches.…”

On board accumulator model for power flow studies in DC traction networks

“…The authors run a steady state simulations at each simulation instant neglecting the transients between successive instants as it was proposed in [8]. This approach, known as stationary equivalent method for moving loads, is widely accepted among the authors, not only for modelling DC traction networks, but also for modelling AC traction networks for high speed trains [2][3][4][5][9][10][11][12][13][14]. This method assumes that the speed of the trains is not very high to induce pronounced electrical transients and the dc traction network slowly moves from one state to another as the locations and the input power of the trains vary.…”