A simulation tool for the design of the electrical supply system of high-speed railway lines

Pilo, E.; Rouco, L.; Fernández, Antonio; Hernandez-Velilla, A.

doi:10.1109/pess.2000.867519

Cited by 28 publications

(22 citation statements)

References 5 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23], RPSS:s are designed by making changes in the system setup and simulating once more until the system fulfils the demands of the designer. The more sophisticated methods for designing the power supply in [24], [25] are followups to [23].…”

Section: B Decision Making or Planning For Dimensioning Of Power Supmentioning

confidence: 99%

“…The more sophisticated methods for designing the power supply in [24], [25] are followups to [23]. In [24], where substations are placed out and catenary setup is chosen to minimize the investment cost, a simplified DC model of an AC RPSS is used, using a set of operational scenarios where power consumption is assumed to be known instead of simulating all the train movements.…”

Section: B Decision Making or Planning For Dimensioning Of Power Supmentioning

confidence: 99%

“…One comparatively detailed 50 Hz RPSSOS working in discretized time is presented in [23], also considering phase imbalances in the public grid. All contact line voltages in [23] has to lie within a certain range.…”

Section: ) Commercialmentioning

confidence: 99%

See 2 more Smart Citations

Fast Estimation of Relations Between Aggregated Train Power System Data and Traffic Performance

Abrahamsson

Söder

2011

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

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 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 is not encountered before 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 the average velocity of them, for that distance. The output is the maximal attainable average velocity of an added train for the by the inputs 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.

show abstract

Section: B Decision Making or Planning For Dimensioning Of Power Supmentioning

confidence: 99%

Section: B Decision Making or Planning For Dimensioning Of Power Supmentioning

confidence: 99%

See 1 more Smart Citation

Fast Estimation of Relations Between Aggregated Train Power System Data and Traffic Performance

Abrahamsson

Söder

2011

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…In the study carried out by Pilo et al (2000), the main objectives of the authors, were to study the functional characteristics of an energy power system for high-speed railway systems, and thus a system of alternating current. The second objective they worked on was to optimize, or rather simplify, the electrical model by moving from a dual voltage model to models, which could be single voltage, thereby reducing the complexity of subsequent calculations (Jiménez-Octavio 2009), continuing with the route taken in the previous study, applies the same simulation model and modelling of the electrical system in such a way that, by implementing an optimization method based on genetic algorithms, more specifically multi-objective functions (Coello 2011), and contributing in some way to the MOGA algorithm (Murata, Ishibuchi 1995) to make the selection process more in tune with his requirements, obtains satisfactory results in terms of response times and the quality of the solutions provided.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

View full text Add to dashboard Cite

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.

show abstract

“…As shown, the electrical system is divided in electrically-isolated sectors, which are fed from the three-phase network through a traction substation [2]. These substations are connected between two of the three phases of the highvoltage network.…”

Section: Introductionmentioning

confidence: 99%

Stochastic traffic generator for Monte Carlo load flow simulation

Fuente¹,

Jiménez-Octavio²,

Pecharromán³

et al. 2011

Computational Methods and Experimental Measurements XV

View full text Add to dashboard Cite

This paper presents a stochastic traffic generator in which (i) the driving manner is assumed to be stochastic, (ii) random unexpected stops are considered and (iii) the stop time in the stations is also supposed to be stochastic. This simulation tool provides a large number of different driving scenarios to be used for a Monte Carlo load flow analysis. Traffic simulations algorithms are described in detail. Reference to a section of the Madrid-Barcelona high-speed line illustrates the potential of such a tool.

show abstract

A simulation tool for the design of the electrical supply system of high-speed railway lines

Cited by 28 publications

References 5 publications

Fast Estimation of Relations Between Aggregated Train Power System Data and Traffic Performance

Fast Estimation of Relations Between Aggregated Train Power System Data and Traffic Performance

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

Stochastic traffic generator for Monte Carlo load flow simulation

Contact Info

Product

Resources

About