Fast Estimation Of Aggregated Results Of Many
Load Flow Solutions In Electric Traction Systems
Author(s)
L. Abrahamsson & L. S¨oder
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 naturally not known for certain. This
means investment planning for an uncertain future. The more remote the uncertain
future, the greater the amount of scenarios that have to be considered. Large numbers
of scenarios make time demanding (some tens of minutes, each) simulations
less attractive and simplifications more so. The aim of this paper is to present a fast
approximator that uses aggregated traction system information as inputs and outputs.
This facilitates studies of many future railway power system loading scenarios,
combined with different power system configurations, for investment planning
analysis. Since the electrical and mechanical relations governing an electric traction
system are quite intricate, an approximator based on neural networks (NN), is
applied. This paper presents a design suggestion for a NN estimating power system
caused limits on active and reactive power load, i.e., limits on the levels of train
traffic.
Keywords: railway, neural networks, load flow.
1 Introduction
For environmental and economic reasons, in Sweden and the rest of Europe, both
personal and goods transports on rail are increasing. Therefore major railway
infrastructure investments are expected. An important part of this infrastructure
is the railway power supply system.
One phase low frequency AC railway power supply systems are normally connected
to the normal 50 Hz power system through frequency converters, see
Figure 1.
Keywords
railway, neural networks, load flow.
Related Book
Other papers in this volume
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