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Research On Reliability Of Traction Substation Based On BDD And Fuzzy Gray Clustering Method

Posted on:2019-02-19Degree:MasterType:Thesis
Country:ChinaCandidate:X ChenFull Text:PDF
GTID:2382330548469625Subject:Power system and its automation
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The high-speed railway has the characteristics of safety,comfort and energy saving,and is a new engine to promote the development of the national economy.With the rapid development of high-speed railway,more and more people are traveling by Electric Multiple Units(EMUs)trains,and people are starting to pay attention to the reliability and safety of traction substations that provide electric power to EMUs.There are a large number of important electrical equipments in traction substation.Once these equipments break down,the EMUs will stop or be delayed,which will affect their normal operation or even cause accidents of body harms and property losses.Therefore,accurate analysis and measurement of the reliability of primary side equipments and the comprehensive reliability of traction substation are very important for operation and maintenance personnel to take timely measures to ensure the stable and reliable operation of traction power supply system.Firstly,the reliability theory and its indexes are introduced,and the process for construction of fault tree and the procedure for generation of Binary Decision Diagram(BDD)structure,and the process for calculation of BDD root node probability and importance of basic events are illustrated.Secondly,according to the analysis and calculation of the failure fault tree of the main electrical connections based on the BDD algorithm programming,the reliability of the main electrical connections and the importance of the primary side equipments are obtained.Thirdly,the basic theory of fuzzy grey clustering,combination weighting method and fuzzy comprehensive evaluation method are introduced.Aiming at the shortcomings of research on comprehensive reliability assessment of traction substation,the three-level index system of reliability assessment for traction substation is established.In addition,by attaining the subjective and objective reliability data of the traction substation,the whitenization weight function values and the combination weights of the second-level indexes are calculated,and then the reliability evaluation matrix under the first-level indexes are obtained,which acts as the fuzzy evaluation matrix,and the reliability assessment results of the entire traction substation are obtained through fuzzy comprehensive evaluation.Finally,according to the results of reliability analysis and reliability assessment,the maintenance suggestions for traction substation are given.The BDD method is used to calculate the reliability of the main electrical connections and the importance of the primary side equipments for traction substation in this thesis.By comparison,the analysis results under the BDD method are consistent with those under the cut set method,and the correctness of the BDD method is verified.The BDD method can not only obtain the accurate values of the top event occurrence probability and the importance of the basic events,but also have a fast calculation speed and a simple process,thus providing a new idea for the reliability analysis of the main electrical connections for traction substation.Through the mode of combining both subjective assessment data and objective measurement data of the equipments,a comprehensive reliability assessment for the traction substation is carried out by using fuzzy gray clustering and combination weighting method,taking account of both subjective willingness of decision makers and inherent objective information of assessment target.The assessment results,which provide a theoretical basis for the overhaul and maintenance of traction substation,are in line with the actual situation.The proposed method is also applicable to similar systems in which subjective and objective evaluation data coexist.
Keywords/Search Tags:Traction substation, Reliability analysis, Reliability assessment, BDD method, Fuzzy gray clustering
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