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Traction Power System Capacity Limitations at Various Traffic Levels
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0003-2109-060X
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-8189-2420
2011 (English)In: WCRR, World Congress on Railway Research, 2011Conference paper (Refereed)
Abstract [en]

The aim, and main contribution, of this paper is to propose a fine-tuned fast approximator, based on neural networks, 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. In the numerical examples 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, and the average number of trains for that distance. The output is the maximal attainable average velocity of trains of a specific kind for the by the inputs described railway power system section. An alternative output – the traveling time is also presented. The main emphasis of this paper is on the example section, since the contribution of this paper is mainly to show on the improved simplicity and reality compliance. The applicative contribution is twofold, an improved TPSA as a planning/decision making program constraint, whereas it also can be used as a scientifically developed rule of thumb for a planner active in the field. The aim is not primarily to show that the idea works, or to motivate the principal idea, since that is done earlier. The approximator facilitates studies of many 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
Keyword [en]
railway power systems, neural networks, load flow, simulation, approximation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-52665OAI: diva2:467473
9th world congress on railway research, May 22-26 2011
QC 20111222Available from: 2012-01-17 Created: 2011-12-19 Last updated: 2012-12-06Bibliographically approved
In thesis
1. Optimal Railroad Power Supply System Operation and Design: Detailed system studies, and aggregated investment models
Open this publication in new window or tab >>Optimal Railroad Power Supply System Operation and Design: Detailed system studies, and aggregated investment models
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Railway power supply systems (RPSSs) differ mainly from public power systems from that the loads are moving. These moving loads are motoring trains. Trains can also be regenerating when braking and are then power sources. These loads consume comparatively much power, causing substantial voltage drops, not rarely so big that the loads are reduced. By practical reasons most RPSSs are single-phase AC or DC. Three-phase public grid power is either converted into single-phase for feeding the railway or the RPSS is compartmentalized into separate sections fed individually from alternating phase-pairs of the public grid. The latter is done in order not to overload any public grid phase unnecessarily much.

This thesis summarizes various ways of optimally operating or designing the railway power supply system. The thesis focuses on converter-fed railways for the reasons that they are more controllable, and also has a higher potential for the future. This is also motivated in a literature-reviewing based paper arguing for the converter usage potential. Moreover, converters of some kind have to be used when the RPSS uses DC or different AC frequency than the public grid.

The optimal operation part of this thesis is mainly about the optimal power flow controls and unit commitments of railway converter stations in HVDC-fed RPSSs. The models are easily generalized to different feeding, and they cope with regenerative braking. This part considers MINLP (mixed integer nonlinear programming) problems, and the main part of the problem is non-convex nonlinear. The concept is presented in one paper. The subject of how to model the problem formulations have been treated fully in one paper.

The thesis also includes a conference article and a manuscript for an idea including the entire electric train driving strategy in an optimization problem considering power system and mechanical couplings over time. The latter concept is a generalized TPSS (Train Power Systems Simulator), aiming for more detailed studies, whereas TPSS is mainly for dimensioning studies. The above optimal power flow models may be implemented in the entire electric train driving strategy model.

The optimal design part of this thesis includes two aggregation models for describing reduction in train traffic performance. The first one presented in a journal, and the second one, adapted more useful with different simulation results was presented at a conference. It also includes an early model for optimal railway power converter placements.

The conclusions to be made are that the potential for energy savings by better operation of the railway power system is great. Another conclusion is that investment planning models for railway power systems have a high development potential. RPSS planning models are computationally more attractive, when aggregating power system and train traffic details.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xii, 77 p.
Trita-EE, ISSN 1653-5146 ; 2012:062
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
urn:nbn:se:kth:diva-107037 (URN)978-91-7501-584-2 (ISBN)
Public defence
2012-12-17, sal Q2, Osquldasväg 10, KTH, Stockholm, 10:00 (English)

QC 20121206

Available from: 2012-12-06 Created: 2012-12-05 Last updated: 2013-02-25Bibliographically approved

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