Share:


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
Abstract Views
868
PDF Downloads
618
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Capuder, T.; Lugaric, L.; Brekalo-Strbic, J.; Krajcar, S. 2009. Optimizing the train power system in Zagreb, in VPPC’09: IEEE Vehicle Power and Propulsion Conference, 7–10 September 2009, Dearborn, MI, 41–45. http://dx.doi.org/10.1109/VPPC.2009.5289872

Chang, C. S.; Low, J. S.; Srinivasan, D. 1999. Application of tabu search in optimal system design and operation of MRT power supply systems, IEE Proceedings – Electric Power Applications 146(1): 75–80. http://dx.doi.org/10.1049/ip-epa:19990214

Chang, C. S.; Wa n g, W.; Liew, A. C.; We n, F. S.; Srinivasan, D. 1995. Genetic algorithm based bicriterion optimisation fortraction substations in DC railway system, in IEEE Inter-national Conference on Evolutionary Computation 1995, 29 November – 1 December 1995, Perth, WA, Australia. http://dx.doi.org/10.1109/ICEC.1995.489111

Chuang, H. J. 2005. Optimisation of inverter placement for mass rapid transit systems by immune algorithm, IEE Proceedings – Electric Power Applications 152(1): 61–71. http://dx.doi.org/10.1049/ip-epa:20041143

Coello, C. A. C. 2011. Evolutionary multiobjective optimization, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(5): 444–447. http://dx.doi.org/10.1002/widm.43

Davis, W. 1926. The Tractive Resistance of Electric Locomotives and Cars. General Electric.

Deb, K. 2006. Original Implementation (for Windows and Linux): NSGA-II in C (Real + Binary + Constraint Handling). Kanpur Genetic Algorithms Laboratory, India. Available from Internet: http://www.iitk.ac.in/kangal/codes.shtml

Deb, K.; Pratap, A.; Agarwal, S., Meyarivan, T. 2002. A fastand elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6(2): 182–197. http://dx.doi.org/10.1109/4235.996017

IEEE Std 1474.1-2004. IEEE Standard for Communications-Based Train Control (CBTC) Performance and Functional Requirements. http://dx.doi.org/10.1109/IEEESTD.2004.95746

Jiménez-Octavio, J. 2009. Análisis dinámico y optimización de catenarias para alta velocidad: Tesis Doctoral. Universidad Pontificia Comillas, Madrid, España. (in Spanish).

Johnston, W. D. 1975. Mainline Railway Electrification: an Economic Feasibility Model: MSc Thesis. University of British Columbia. Canada. 194 p. Available from Internet: https://circle.ubc.ca/handle/2429/19118

Mera, J. M.; Ta p i a, S.; Vera, C.; Jaen, J. A. 2000. Railway lines operation simulator: GifTren, in Seventh International Conference on Computers in Railways: Computers in Railways VII, 11–13 September 2000, Bologna, Italy, 997–1006.

Murata, T.; Ishibuchi, H. 1995. MOGA: multi-objective genetic algorithms, in IEEE International Conference on Evolutionary Computation 1995, 29 November – 1 December 1995, Perth, WA, Australia. http://dx.doi.org/10.1109/ICEC.1995.489161

Olofsson, M.; Andersson, G.; Söder, L. 1995. Optimal operation of the Swedish railway electrical system, in International Conference on Electric Railways in a United Europe1995, 27–30 March 1995, Amsterdam, Netherlands, 64–68. http://dx.doi.org/10.1049/cp:19950179

Pereira, F. H.; Lobo Pires, C.; Ikuyo Nabeta, S. 2014. Optimal placement of rectifier substations on DC traction systems, IET Electrical Systems in Transportation4(3): 62–69. http://dx.doi.org/10.1049/iet-est.2010.0063

Pilo, E.; Rouco, R.; Fernández, A.; Hernández-Velilla, A. 2000. A simulation tool for the design of the electrical supply sys-tem of high-speed railway lines, in IEEE Power Engineering Society Summer Meeting 2000, 16–20 July 2000, Seattle, WA, 2: 1053–1058. http://dx.doi.org/10.1109/PESS.2000.867519

Soler, M.; López, J .; Mera, J. M. 2012. Simulation system for the optimization of a block distribution under the ERTMS-1 signalling system, in Thirteenth International Conference on Design and Operation in Railway Engineering (COMPRAIL 2012), 11–13 September 2012, New Forest, UK, 61–72.

UNE-EN 50388:2006 CORR:2010. Aplicaciones ferroviarias. Alimentación eléctrica y material rodante. Criterios técnicos para la coordinación entre sistemas de alimentación (subestación) y el material rodante para alcanzar la interoperabilidad [Railway Applications. Power Supply and Rolling Stock. Technical Criteria for the Coordination Between Power Supply (Substation) and Rolling Stock to Achieve Interoperability] (in Spanish).

UNE-EN 50329:2004/A1:2011. Aplicaciones ferroviarias. Instalaciones fijas. Transformadores de tracción [Railway Applications. Fixed Installations. Traction Transformers] (in Spanish).

Viet, N. X. H.; Song, H.-S.; Nam, K. 2004. Locating power sup-plies on a personal rapid transit system to minimize system losses, IEEE Transactions on Industry Applications 40(6): 1671–1677. http://dx.doi.org/10.1109/TIA.2004.836310