Fast Estimation of Relations Between Aggregated Train Power System Data and Traffic Performance
2011 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 60, no 1, 16-29 p.Article in journal (Refereed) Published
Transports via rail are increasing, and major railway infrastructure investments are expected. An important part of this infrastructure is the railway power supply system (RPSS). 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 has not previously been encountered 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 their average velocity for that distance. The output is the maximal attainable average velocity of an added train for the 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.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2011. Vol. 60, no 1, 16-29 p.
Load flow, neural networks, railway
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-30549DOI: 10.1109/TVT.2010.2091293ISI: 000286385700003ScopusID: 2-s2.0-78751660527OAI: oai:DiVA.org:kth-30549DiVA: diva2:401542
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QC 201103032012-01-172011-02-282012-12-06Bibliographically approved