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 . 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  is applied to fault diagnosis of power grid, bayesian network in literature  is applied to reliability evaluation of distribution network, bayesian network in literature  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 . 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. 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: Copyright © 2013 SciRes. EPE Z. C. LIANG ET AL. 829Posterior probability from the Angle of the fault diag-nosis reflects the importance of the components in the system size, especially suitable for weak links in recog-nition system, fault diagnosis and inspection and repair planning. 3. Bayesian Network Model and Algorithm Based on the traditional shortest path set method or the minimum cut set method when evaluating the reliability of the system, finally will involve the minimization algo-rithm of path sets and minimum cut sets processing, to-quantitative calculation of system reliability finally. But when the system is v ery complex, the element number is larger, the minimization algorithm processing will be complicated. Because bayesian network itself contains the conditional independence between the node variable precision, so based on shortest path set method or mini-mum cut set method of bayesian networks need not minimum cut set or sets the path minimization algorith m processing. Based on shortest path set method or minimum cut set method of bayesian network model using the path set method or minimum cut set method of bayesian networks is established. Specific steps are as follows: 1) Finding out the path sets and minimum cut sets of the system; 2) Setting the components in the path set and mini-mum cut as initial model element node; 3) Setting all the node path sets and minimum cut as subsystem node; 4) Using conditional probability tables to describe the relationship of the subsystem node and the initial element nodes. Logical relationship in here is mainly refers to “or” and “and”, conversions with the bayesian network are shown in Figure 1: (a) Or gate (b) And gate Figure 1. Logic relation and Bayesian network transforma-tion. After convert the path sets and minimum cut sets to bayesian networks, reliability indices can be reasoning calculationcan. The algorithm about precise reasoning of bayesian networks is mainly based on Poly Tree Propa-gation, Bucket-Elimination, Clique Tree Propagation. Bayesian networks are precise reasoning algorithm is mainly based on Poly real Tree Propagation method, the method based on combinatorial optimization problems, and based on Clique Tree Propagation method. The me-thod of Poly Tree Propagation is only applicable to sim-ply connected, Bucket - Elimination and Clique Tree Propagation fit with both simply connected also with multiply connected, but when there are multiple asked node in the system, often using a Clique Tree Propaga-tion method. 4. Example Analysis 4.1. High Speed Railway External Power Supply Reliability Index Using the reliability evaluation model of international general IEEE RBTS - (6) system as an example, putting forward high-speed railway external power supply scheme which is based on shortest path set method of bayesian network and bayesian network based on mini-mal cut set method to quantitative ev aluation the scheme of power supply reliability of the traction substation, re-spectively. RBTS (6 nodes) system are shown in Figure 2: Figure 2: a power on bus 1, 2, one bus in the power plant has fou r generatin g un i ts，they are G11、G12、G13、G14, and its capacity are P11, P12, P13 and P14, P11 = P12 = 40 MW, P13 = 20 MW, P14 = 10 MW; Bus 2 Figure 2. RBTS（6 node）syste m . Copyright © 2013 SciRes. EPE Z. C. LIANG ET AL. Copyright © 2013 SciRes. EPE 830 there are 7 station in power plant, namely, G21, G22, G23, G24, G25, G26 and G27, its capacity is P21 , P22, P23, P24, P25, P26 and P27, the P21= 40 MW, P22 = P23 = P24 = P25,= 20 MW 、P26 = P27 = 5 MW. The reliability of components of the system parameters are shown in Tables 1-2. Assumes that the bus bars in Figures 2-4 respectively with the traction substation TPSS2 - TPSS6, these traction substation to supply one high speed railway. 4.2. Bayesian Network Based on Shortest Path Set Method Based on high-speed rail as shown in Figure 2 external power supply network, can respectively calculate the traction substation power supply of the path set, here in traction substation TPSS2 on bus 2 as an example, using Boolean determinant method, can get the shortest path sets of TPSS2: G1L3, G1L1L2L4 G1L1L4L7, G1L2L4L6, G1L1L2L5L8 G1L1L5L7L8, G2, G1L4L6L7, G1L2L5L6L8 G1L5L6L7L8. According to the shortest path of TPSS2 set can make corresponding bayesian networks, shown as in Figure 3. Figure 3: L1, L2, L3,... , L9 respectively represent L1 - L9 in the system of RBTS (6 nodes); A1, A1, A2,... A5, representing the path G1L3, G1L1L2L4, G1L1L4L7, G1L2L4L6 G1L4L6L7, G1L1L2L5L8; the relationship A1, A2,... , A5 and T is “or”. In combination with the conditional probability tables of A1, A2,... , A5, T and the failure rate of components, and connecting with the development of reliability assessment based on bayesian networks we can calculate P(T)2: 6PT2 6.634810 Among them, P (T) 2 is the outage probability of the traction substation on bus 2. Similarly, we can calculate the probability o f power traction substatio n outage on the bus 3 to 6 bus on as follows: 63646546P T=6.634810P T=6.634810PT=6.720610P T=2.967110 which we can see, the probabilit y of power outag e on the bus 6 (P (T)6) is greater than the rest of the few relatively traction substation, because the bus 6 use single wire(L9) accessing, so the failure rate is relatively large. Table 1. IEEE-RBTS (6 node) system line data Line Bus from to Permanent outage rate (per year) 1 1 3 1.5 2 2 4 5.0 3 1 2 4.0 4 3 4 1.0 5 3 5 1.0 6 1 3 1.5 7 2 4 5.0 8 4 5 1.0 9 5 6 1.0 Table 2. IEEE-RBTS(6 node) system generating set data. Unit Size(MW) Type Fsilure Rate Per year 5 hydro 2.0 10 thermal 4.0 20 thermal 5.0 40 thermal 6.0 Figure 3. the minimal path of bayesian network(bus 2). Z. C. LIANG ET AL. 831 4.3. Bayesian Network Based on Minimal Cut Set Method Based on high-speed rail as shown in Figure 2 external power supply network, can respectively calculate the traction substation power supply of the minimum cut set, here in traction substation TPSS2 on bus 2 as an example, using Boolean determinant method, can get the min imum cut set of TPSS2: G1G2、L1L3L6G2、L3L4L8G2 L3L4L5G2、L2L3L7G2. Accord ing to the minimum cut set of TPSS2 can make corresponding bayesian networks, shown as in Figure 4. Based on bayesian networks is shown in Figure 4, combining with the development of the bayesian netwo rk reliability evaluation calculation program we can calcu-late P(T)2: 62P T=6.634810 Among them, P (T) 2 is the outage probability of the traction substation on bus 2.Comparison shows that this result with bayesian network based on shortest path set method in the example calculation results P (T) 2 is the same, in the same way,we can also verify TPSS3 - TPSS6 power outage probability and minimum path set method of the calculation results are the same, therefore, by comparing the calculation results of the two methods, verify the feasibility of high speed railway external pow-er supply reliability assessment and accurate. 4.4. Bayesian network Identification System Vulnerabilities Because the bayesian network can bidirectional reason-ing, so it can calculate when system failure, the po sterior probability of each component, then through analysis the posteriori probability of each element, you can easily identify weak links of the system. For high-speed rail external power supply system as shown in Figure 2, the posteriori probability of various components as shown in Table 3. T L1 L2 L3 L4 L5 L6 L7 L8 G1 G2 A1 A2 A3 A4 A5 Figure 4. The minimal cut of bayesian network(bus 2). Table 3. Main parts of posterior probability. Bus 2 Bus 3 Bus 4 Bus 5 Bus 6 L1 5.10×10-4 4.30×10-4 4.30×10-4 4.30×10-4 4.30×10-4 L2 1.43×10-3 1.43×10-3 1.43×10-3 1.52×10-3 1.43×10-3 L3 1.14×10-3 1.14×10-3 1.14×10-3 1.13×10-3 1.11×10-3 L4 2.90×10-4 2.90×10-4 2.90×10-4 5..46×10-4 2.96×10-4 L5 2.90×10-4 2.90×10-4 2.90×10-4 1.28×10-2 5.73×10-4 L6 4.3×10-4 4.30×10-4 4.30×10-4 4.30×10-4 4.30×10-4 L7 2.90×10-4 1.43×10-3 1.43×10-3 1.52×10-3 1.43×10-3 L8 2.90×10-4 2.90×10-4 2.90×10-4 1.28×10-2 5.73×10-4 G1 1 1 1 0.98725 0.97735 G2 1 1 1 0.98744 0.02425 L9 0 0 0 0 0.0257 Copyright © 2013 SciRes. EPE Z. C. LIANG ET AL. 832 Through analysis posteriori probability of the various components in Table 3, high-speed railway external power of the weak link are L9 and G1, G2, so reduce the failure rate and maintenance time of generator G1, G2, and L9, will significantly improve the reliability of high speed railway external power supply. 5. Conclusions 1) Based on probabilistic inference of bayesian net-work used in high-speed railway external power supply reliability evaluation, established the minimum cut set and minimum path set method of bayesian network mod-el, and develops the corresponding program for calculat-ing the credible degree. 2) Using the IEEE RBTS system as an example to es-timate the reliability of high speed railway external pow-er supply scheme, and quantitative calculate the high-speed railway traction power substation of probabil-ity, proving the correctness and feasibility of the method which can be applied to high speed railway external power supply reliability evaluation. 3) Based on bidirectional reasoning technology of bayesian network, we can quantitative calculation the posterior probability of each elements in the external power supply system, and realize the identification of weakness about th e external power supply, providing the basis for carrying out external power supply reliability distribution in the further. REFERENCES  M. W. Chen, “The Reliability Assessment of Traction Substation of High Speed Railway by the GO Methodol-ogy,” Power System Protection and Control, Vol. 39, No. 18, 2011, pp. 56-61.  Y. Zhang, Z. Y. He and S. Lin, “A Power System Fault Diagnosis Method Based on DS Evidence Theory. Power System Protection and Control,” Vol. 36, No. 9, 2008, pp. 5-10.  Y. F. Xie, “Reliability Assessment of Distribution Sys-tems Based on Bayesian Networks,” Baoding：Hebei Agricultural University, 2008.  H. F. Su, “The Bayesian Networks and It’s Application for Generation Systems Reliability Assessment,” Baod-ing : Hebei Agricultural University, 2004.  X. W. Yin, W. X. Qian and L. Y. Xie, “A Method for System Reliability Assessment Based on Bayesian Net-works,” Aviation Journal, Vol. 29, No. 6, 2008, pp. 1482-1489.  Q. Z. Luo, S. Shi，Y. H. Zhang and W. J. Liu, “The Ap-plication of Bayesian Network in Reliability Assessment of Multi-state System,” Micro Computer Information, Vol. 26, No. 8-1, 2010, pp. 209-212.  L. M. Huo, Y. L. Zhu, G. F. Fan, et al., “A Method Based on Bayesian Network Power System Reliability Assess-ment of the New Method,” Automation of Electric Power Systems, Vol. 27, No. 5, 2003, pp. 36-40.  W. X. Yin, “Reliability Evaluation of Mechanical System Based on Bayesian Network,” Shenyang: Northeastern University. 2007.  B. Xie, M. Z. Zhang and Y. X. Yan, “Improve Faulty Tree Analysis by Bayesian Networks,” Journal of Yan-shan University, Vol. 28, No. 1, 2004, pp. 55-58.  R. Billinton and S. Kumar, “A Reliability Test System For Educational Purposes,” IEEE Transactions On Power Systems, Vol. 4, No. 3, pp. 1238-1244. doi:10.1109/59.32623 Copyright © 2013 SciRes. EPE
{"ft_lang_label":"__label__en","ft_lang_prob":0.8282392,"math_prob":0.9278089,"size":15963,"snap":"2020-34-2020-40","text_gpt3_token_len":4446,"char_repetition_ratio":0.17513628,"word_repetition_ratio":0.097893916,"special_character_ratio":0.2682453,"punctuation_ratio":0.15055418,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9652327,"pos_list":[0,1,2,3,4,5,6,7,8,9,10],"im_url_duplicate_count":[null,2,null,2,null,2,null,2,null,2,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-09-23T07:56:12Z\",\"WARC-Record-ID\":\"<urn:uuid:d7e59877-b5a1-4746-a7d2-51155883b1ad>\",\"Content-Length\":\"62785\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:88bad6b2-5bd6-45ed-a17e-311391fc12bf>\",\"WARC-Concurrent-To\":\"<urn:uuid:f1943bc5-5956-4889-bb77-6606ae93fd1b>\",\"WARC-IP-Address\":\"209.141.51.63\",\"WARC-Target-URI\":\"https://file.scirp.org/Html/39371.html\",\"WARC-Payload-Digest\":\"sha1:5XDUO4FW63XO3RRWNL433DJNGPBDQEDK\",\"WARC-Block-Digest\":\"sha1:ZUOKPR3YBJODYNJDG6MVV6O73DNVMJGH\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-40/CC-MAIN-2020-40_segments_1600400209999.57_warc_CC-MAIN-20200923050545-20200923080545-00562.warc.gz\"}"}