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基于贝叶斯网络的核电站应急电力系统安全评价

Application of Bayesian networks in safety assessment of a nuclear power plant's emergency power systems
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摘要 在故障树分析法(FTA)基础上提出了一种基于贝叶斯网络(BN)的核电站应急电力系统安全评价方法,比较了FTA和BN在建立安全评价模型和评价能力上的不同。该方法在应对众多影响因素上有很大优势,能进行更多有意义的分析:既能进行前向的预测推理,又能进行后向的诊断推理,可以找出影响故障的组合模式,从而能够找出系统的薄弱环节。同时采用基于Matlab的BNT软件包,大大简化了计算过程。通过对10MW高温气冷堆(HTR-10)应急电力系统的安全评价实例的分析,证明该方法是对传统的基于故障树分析的安全评价方法的有益改进。 This paper presents a Bayesian networks (BN) approach for safety assessment of the emergency power supply system of nuclear power plant on the basis of fault tree analysis (FTA). Compared with FTA, the modeling process and the assessing ability of BN has many advantages when dealing numerous factors. It can be used to make more significant analysis, such as forward inference (prediction), backward inference (diagnosis), and finding the integrated mode of inducing faults, so as to find the bottleneck of the system. Adopting the Bayes net toolbox (BNT) software based on Matlab, the modeling and analyzing process is greatly facilitated. Finally, an example on the emergency powder supply system of the 101VP~ high temperature gas-cooled reactor (HTR-10) was provided to illustrate that the BN medaod was an improvement for FTA in the safety assessment.
作者 魏利强 郑恒
出处 《高技术通讯》 CAS CSCD 北大核心 2007年第6期628-632,共5页 Chinese High Technology Letters
基金 863计划(863-614-02)资助项目.
关键词 应急电力系统 安全评价 贝叶斯网络 故障树分析 emergency power supply system, safety assessment, Bayesian networks, fault tree analysis
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  • 1吴宗鑫,肖宏才.模块式高温气冷堆的安全特性[J].高技术通讯,1994,4(11):34-38. 被引量:11
  • 2Coper G F. The computational complexity of probabilistic inference using Bayesian belief networks[J].Artificial Intelligence, 1990, 42 (2-3) : 393-405. 被引量:1
  • 3Poole D. Average-case analysis of a search algorithm for estimating prior and posterior probabilities in Bayesian networks with extreme probabilities [A].Proc 13th Int J Conf on Artificial Intelligence [C ].France, 1993. 606-612. 被引量:1
  • 4Skaanning C,Jensen F V,Kjaerulff U. Printer troubleshooting using Bayesian networks [A]. Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE) 2000[C]. New Orleans,2000. 101-108. 被引量:1
  • 5Stephenson T A. An introduction to Bayesian network theory and usage[R]. Switzerland: IDIAP-RR 00-03,Dale Molle Institute, 2000. 被引量:1
  • 6Lepar V, Shenoy P P. A comparison of lauritzen and spiegelhalter, hugin and sharer and shenoy architectures for computing marginals of probability distributions [A]. The Proc of the 14th Conf on Uncertainty in Artificial Intelligence [ C ]. San Francisco: Morgan Kaufmann Publishers, 1998. 328-337. 被引量:1
  • 7Bresnick T A, Buede D M, Tatman J A. Introduction to Bayesian networks[A]. The 66th MORS Symposium[C]. California: Naval Postgraduate School Monterey,1998.23-25. 被引量:1
  • 8Breese J S, Heckerman D. Decision-theoretic troubleshooting: A framework for repair and experiment[A].Proc 12th Conf on Uncertainty in Artificial Intelligence[C]. San Praneiseo: Morgan Kaufmann Publishers,1996. 124-132. 被引量:1
  • 9Heckerman D, Breese J, Rommelse K. Decision-theoretic troubleshooting[J]. Communications of the ACM ,1995,38(3) :49-57. 被引量:1
  • 10梅启智,标准法国900兆瓦(电)压水堆概率安全评价(主报告),1993年 被引量:1

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