EXPERT SYSTEM USING MULTI-OBJECTIVE OPTIMIZATION OF THE DIRECT CURRENT RAILWAY POWER SUPPLY SYSTEM

被引:1
作者
Soler Nicolau, Manuel [1 ]
Lopez, Jesus [1 ]
Tapia, Santiago [1 ]
Manuel Mera, Jose [1 ]
机构
[1] Tech Univ Madrid, Res Ctr Railway Technol CITEF, Madrid, Spain
关键词
zone discretization; electric system optimization design; NSGA-II; maximum peak power demand; expert system; PLACEMENT; ALGORITHM;
D O I
10.3846/16484142.2015.1108225
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
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.
引用
收藏
页码:131 / 142
页数:12
相关论文
共 20 条
[1]  
[Anonymous], CORR
[2]  
[Anonymous], 503292004A12011 UNEE
[3]  
[Anonymous], 1926, TRACTIVE RESISTANCE
[4]  
Capuder Tomislav, 2009, 2009 IEEE Vehicle Power and Propulsion Conference (VPPC), P41, DOI 10.1109/VPPC.2009.5289872
[5]  
Chang C. S., 1995, IEEE INT C EV COMP 1
[6]   Application of tabu search in optimal system design and operation of MRT power supply systems [J].
Chang, CS ;
Low, JS ;
Srinivasan, D .
IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS, 1999, 146 (01) :75-80
[7]   Optimisation of inverter placement for mass rapid transit systems by immune algorithm [J].
Chuang, HJ .
IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS, 2005, 152 (01) :61-71
[8]   Evolutionary multiobjective optimization [J].
Coello Coello, Carlos A. .
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2011, 1 (05) :444-447
[9]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[10]  
Deb K., 2006, ORIGINAL IMPLEMENTAT