Abstract
For the sake of enhancing safety and achieving more economic benefits, it is very important to timely detect and identify the transients during the operation of nuclear power plants. There are thousands of monitoring signals in a nuclear power plant. It is unfeasible to detect the transients by monitoring all the related signals individually, as the thresholds for a large number of signals are hard to be determined one by one. Also, there might be too many alarms happening simultaneously when a transient occurs. In this case, the operators are hard to make a right judgment about what has happened. In this paper, a method based on principal component analysis (PCA) and T 2 statistic is proposed to detect the transients and the contribution plot is applied to identify the variables relevant to the transients. At last, the proposed method is applied with the sampling data from the simulator of High Temperature gas-cooled Reactor Pebble-bed Module (HTR-PM). The results from the application demonstrate that the proposed method is capable to detect the faults and identify the most relevant variables timely and correctly.
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Acknowledgments
This work has been supported by the Tsinghua University Initiative Scientific Research Program (Grant No. 20151080382 and no. 20151080380) and National Natural Science Foundation of China (Grant No. 71401169).
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Zhou, SQ., Guo, C., Huang, XJ. (2018). Transient Detection and Identification for HTR-PM Based on Principle Component Analysis. In: Xu, Y., Gao, F., Chen, W., Liu, Z., Gu, P. (eds) Nuclear Power Plants: Innovative Technologies for Instrumentation and Control Systems. SICPNPP 2017. Lecture Notes in Electrical Engineering, vol 455. Springer, Singapore. https://doi.org/10.1007/978-981-10-7416-5_9
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DOI: https://doi.org/10.1007/978-981-10-7416-5_9
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