Energy and Power Engineering, 2013, 5, 828-832

doi:10.4236/epe.2013.54B159 Published Online July 2013 (http://www.scirp.org/journal/epe)

High-speed Railway External Power Supply Reliability

Evalua t i o n o f B a y e s i an N e t w o r k

Zechuan Liang, Minwu Chen, Guoxu Shang, Baoyu Lv

School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

Email: liangzechuan@foxmail.com

Received April, 2013

ABSTRACT

Reliability evaluation is important in high speed railway external power supply design, based on probability reasoning

bayesian network applied in high-speed railway external power su pply reliability ev aluation, estab lish the minimum c ut

and the minimum path of bayesian network model, quantitative calculation external power supply system in each ele-

ment posterior probability, and the example analysis verified the feasibility and correctness of the above method. Using

bayesian network bidirection reasoning technology, quantitative calculation the posterior probability of each element in

external power supply system, realized the identification of weak link in external power supply. The research methods

and the results of the study can be used in the scheme optimization design of high speed railway external power supply.

Keywords: Bayesian Networks; The Minimal Path; The Minimal Cut; Extern al Power Source; Reliability Evaluation

1. Introduction

With the large-scale construction of electrified railway in

our country, especially, the high-speed railway opening,

reliability problem increasingly cause the attention of

people [1]. As the first level of power load, electrified

railway traction power supply system needs to provide

stable and reliable power system of the external power

supply. Due to the high speed rail (including passenger

dedicated line) operating speed is high, so the external

power supply in the event of failure, may result in trac-

tion power supply system of power supply interruption,

directly affect high-speed railway security, reliable and

efficient operation. So on the outside of the high speed

railway power supply scheme is essential in the design of

power supply reliability assessment, the quantitative cal-

culation of the external power supply reliability, accurate

identification of external power supp ly of the weak links,

optimizing the design of the external power supply

scheme, has important theoretical significance and ap-

plication va l ue.

Through based on probabilistic inference of bayesian

network used in high-speed railway external power sup-

ply reliability evaluation, aiming at traction substation

power supply without interruption, establishing bayesian

networks model, writing the reliability calculation pro-

gram, quantitative evaluation caused by external power

failure probability of traction power substation. Bidirec-

tional reasoning technology based on bayesian network,

identification the weak links of high speed railway ex-

ternal power supply to opti mize the design of high speed

railway external power supply scheme.

2. Summary of Bayesian Network

In recent years, the method of bayesian network has been

successful used in many fields. bayesian network in lit-

erature [2] is applied to fault diagnosis of power grid,

bayesian network in literature [3] is applied to reliability

evaluation of distribution network, bayesian network in

literature [4] is used in power system reliability assess-

ment. After years of development and perfection , bayes-

ian networks has become an effective evaluation of reli-

ability, general method.

Bayesian networks also called the reliability network

is a kind of probability netwo rk, it is based on probabilis-

tic graphical network [4]. A bayesian network is a di-

rected acyclic graph, it is represented by variables of

nodes and connecting the nodes of directed ar c s.

Bayesian use nodes to represent variables and directed

arcs between nodes represent the relationship between

variables, through the graphical expression of uncertain

knowledge, through annotations of the conditional prob-

ability distribution can be expressed local conditions de-

pendency in the model[5].

Because bayesian network has bidirectional reasoning

technology, so it can be calculated after the occurrence of

certain events T, X occurs the a posteriori probability of

the event, specially, 1 represents the event occurs, spe-

cific as follows:

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