1. Key Laboratory of Advanced Reactor Engineering and Safety of the Ministry of Education, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Abstract：The status of nuclear reactors in commercial nuclear power plants needs to be closely monitored to maintain normal operations. When a failure occurs, rapid and effective fault diagnostics and proper handling of failures is extremely important. This paper applies dynamic uncertain causality graph (DUCG) theory to fault diagnostics of nuclear power plants. The method was applied to a model with 8 typical second and loop faults based on the Ningde Nuclear Power Plant Unit 1 CPR1000 of the China Guangdong Nuclear Power Group (CGNPC) to verify the fault diagnostics and initial progression forecasts. Simulations were used to test each fault 20 times. The method and stimulator tests both showed that DUCG can accurately, quickly and efficiently diagnose faults.
[1] United States. President's Commission on the Accident at Three Mile Island. The Need for Change, the Legacy of TMI:Report of the President's Commission on the Accident at Three Mile Island[M]. Washington D C, USA:Government Printing Office, 1979.
[2] Rogovin M. Three Mile Island:A Report to the Commissioners and to the Public[R]. Washington D C,USA:Nuclear Regulatory Commission, 1979.
[3] ZHANG Qin. Dynamic uncertain causality graph for knowledge representation and reasoning:Discrete DAG cases[J].Journal of Computer Science and Technology, 2012,27(1):1-23.
[4] ZHANG Qin. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning:directed cyclic graph and joint probability distribution[J].IEEE Transactions on Neural Networks and Learning Systems, 2015,26(7):1503-1517.
[5] ZHANG Qin, DONG Chunling, CUI Yan, et al. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning:Statistics base, matrix, and application[J].IEEE Transactions on Neural Networks and Learning Systems, 2014,25(4):645-663.
[6] ZHANG Qin, GENG Shichao. Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems[J].IEEE Transactions on Reliability, 2015,64(3):910-927.
[7] ZHANG Qin. Dynamic uncertain causality graph for knowledge representation and reasoning:Continuous variable, uncertain evidence, and failure forecast[J].IEEE Transactions on Systems, Man, and Cybernetics, 2015,45(7):990-1003.
[8] 赵越, 张勤, 邓宏琛, 等. DUCG在核电站二回路故障诊断中的应用[J]. 原子能科学技术, 2014,48(S1):496-501. ZHAO Yue, ZHANG Qin, DENG Hongchen, et al. Application of DUCG in fault diagnosis of nuclear power plant secondary loop[J].Atomic Energy Science and Technology, 2014,48(S1):496-501. (in Chinese)
[9] DONG Chunling, WANG Yanjun, ZHANG Qin, et al. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo[J].Computer Methods and Programs in Biomedicine, 2014,113(1):162-174.
[10] Nelson W R. Reactor:An expert system for diagnosis and treatment of nuclear reactor accidents[C]//Proceedings of the 2nd National Conference on Artificial Intelligence. Pittsburgh, USA:AAAI Press, 1982:296-301.
[11] 周刚, 杨立. 核电厂智能诊断方法研究的进展[J]. 原子能科学技术, 2008,42(B09):92-99. ZHOU Gang, YANG Li. Advance in study of intelligent diagnostic method for nuclear power plant[J].Atomic Energy Science and Technology, 2008,42(B09):92-99. (in Chinese)
[12] 陈志辉, 夏虹, 刘邈. 核电系统故障诊断专家系统研究[J]. 核动力工程, 2006,26(5):523-527. CHEN Zhihui, XIA Hong, LIU Miao. Study of expert system of fault diagnosis for nuclear power plant[J].Nuclear Power Engineering, 2006,26(5):523-527. (in Chinese)
2016, Vol. 56 
