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Expert system using multi-objective optimization of the direct current railway power supply system

    Manuel S. Nicolau Affiliation
    ; Jesús López Affiliation
    ; Santiago Tapia Affiliation
    ; José M. Mera Affiliation

Abstract

There are many different aspects to be analyzed when designing a railway infrastructure. The energy system, which withstands the demand for energy from operating trains, must consider many factors to create a functional infrastructure, in terms of demanded energy and cost sustainable. The methodology proposed gives a set of possible solutions to the designer or engineer. On the one hand, this method works with a multi-objective genetic algorithm (NSGA-II), with high time efficiency. The main target of this work is to obtain the best electrical configuration in terms of number and location of substations and characteristics of the overhead line system. On the other hand, best configurations must take into account things such as real railway operation, signalling system, infrastructure, costs linked with environment, maintenance, construction and connection with general electric network, losses of energy dissipated along the catenary. Hence, this methodology must combine all of these skills and integrate it with a railway configuration, modelling and simulation tool, Hamlet developed at CITEF (Research Centre on Railway Technologies by Technical University of Madrid, Spain). After using this methodology, designers will have a set of configurations in order to get the final choice of location of traction substations and type of overhead line system to achieve properly the power demand from trains in railway systems.


First published online 02 November 2015

Keyword : zone discretization, electric system optimization design, NSGA-II, maximum peak power demand, expert system

How to Cite
Nicolau, M. S., López, J., Tapia, S., & Mera, J. M. (2018). Expert system using multi-objective optimization of the direct current railway power supply system. Transport, 33(1), 131-142. https://doi.org/10.3846/16484142.2015.1108225
Published in Issue
Jan 26, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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