Abstract
In order to improve the timeliness and accuracy of the fault identification for SB-LOCA (small break-loss of coolant accident), a hierarchical and multidimensional fault identification method is proposed, and a intelligent early-warning system is established to locate and evaluate the degree of the fault in the early stage, which can improve the operating safety of nuclear power plants. The faults in different kinds of locations and degrees are artificially inserted into the nuclear power simulator and are recognized by the early-warning system based on the method researched above. The results show that it can accurately locate and evaluate the tiny degree of fault, which verifies the validity and feasibility of the intelligent early-warning system.
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References
Gang, Z., Li, Y.: Advance in study of intelligent diagnostic method for nuclear power plant. J. At. Energy Sci. Technol. S1, 92–99 (2008)
Jianbo, L.: Research on Alarm Triggered Intelligent Fault Diagnosis Expert System of Nuclear Power Plant. Shanghai University of Electric Power, Shanghai (2015)
Xiaolong, W., Qi, C., Xiaoqi, Z.: Study on diagnostic technique for nuclear power plant primary coolant circuit system break characteristics based on multi-classification SVM. J. At. Energy Sci. Technol. 03, 462–468 (2014)
Liangyu, M., Yongguang, M., Bingshu, W.: A new approach to diagnose variable-degree faults under different operating points for high-pressure feedwater heater system. J. Proc. CSEE 02, 115–121 (2010)
Zhijie, Z., Jianbo, Y., Changhua, H.: Confidence Rule Base Expert System and Complex System Modeling. Science Press, Beijing (2011)
Hong, Q., Jianbo, L., Weixiao, J.: Research on alarm triggered fault-diagnosis expert system for U-shaped tube breaking accident of steam generators. J. Nucl. Power Eng. 36(1), 98–103 (2015)
Acknowledgements
This work was partially supported by National Natural Science Foundation of China, Grant No. 61503237; Shanghai Natural Science Foundation (No. 15ZR1418300); Shanghai Key Laboratory of Power Station Automation Technology (No. 13DZ2273800).
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Qian, H., Lin, S., Zheng, M., Zhang, Q. (2017). Research on Intelligent Early-Warning System of Main Pipeline in Nuclear Power Plants Based on Hierarchical and Multidimensional Fault Identification Method. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_20
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DOI: https://doi.org/10.1007/978-981-10-6364-0_20
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