Estimation of the Nuclear Power Peaking Factor Using In-core Sensor Signals

  • Published : 2004.10.01

Abstract

The local power density should be estimated accurately to prevent fuel rod melting. The local power density at the hottest part of a hot fuel rod, which is described by the power peaking factor, is more important information than the local power density at any other position in a reactor core. Therefore, in this work, the power peaking factor, which is defined as the highest local power density to the average power density in a reactor core, is estimated by fuzzy neural networks using numerous measured signals of the reactor coolant system. The fuzzy neural networks are trained using a training data set and are verified with another test data set. They are then applied to the first fuel cycle of Yonggwang nuclear power plant unit 3. The estimation accuracy of the power peaking factor is 0.45% based on the relative $2_{\sigma}$ error by using the fuzzy neural networks without the in-core neutron flux sensors signals input. A value of 0.23% is obtained with the in-core neutron flux sensors signals, which is sufficiently accurate for use in local power density monitoring.

Keywords

References

  1. Final Safety Analysis Report for YGN Unit 3 & 4, Korea Electric Power Company
  2. W. B. Terney, J. L. Biffer, C. O. Dechand, A. Josson, and R. M. Versluis, 'The C-E CECOR Fixed In-core Detector Analysis System,' Trans. Am. Nucl. Soc. 44, 542 (1983)
  3. 'Overview Description of the Core Operation Limit Supervisory System (COLSS),' CEN-312-P, Revision 01-P, ABB Combustion Engineering Inc., Nov. (1986)
  4. Final Safety Analysis Report for Wolsung Unit 1, Korea Electric Power Company
  5. Tang, T. L., et al., 'Analytical Design of the CANDU-600 On-line Flux Mapping System,' TDAI-152, Atomic Energy of Canada Limited (1978)
  6. H. C. Kim and S. H. Chang, 'Development of a Back Propagation Network for One-Step Transient DNBR Calculations,' Annals of Nuclear Energy, 24, pp. 1437-1446, (1997) https://doi.org/10.1016/S0306-4549(97)00051-0
  7. J. K. Lee and B. S. Han, 'Modelling of core protection and monitoring system for PWR nuclear power plant simulator,' Annals of Nuclear Energy, 25, pp. 409-420, (1998) https://doi.org/10.1016/S0306-4549(97)00075-3
  8. S. Han, U. S. Kim, and P. H. Seong, 'A Methodology for Benefit Assessment of Using In-Core Neutron Detector Signals in Core Protection Calculator System (CPCS) for Korea Standard Nuclear Power Plants (KSNPP),' Annals of Nuclear Energy, 26, pp. 471-488, (1999) https://doi.org/10.1016/S0306-4549(98)00063-2
  9. M. G. Na, 'Application of a Genetic Neuro-Fuzzy Logic to Departure from Nucleate Boiling Protection Limit Estimation,' Nuclear Technology, 128, pp. 327-340, (1999)
  10. M. G. Na, 'DNB Limit Estimation Using an Adaptive Fuzzy Inference System,' IEEE Trans. Nucl. Sci., 47, pp. 1948-1953, (2000) https://doi.org/10.1109/23.914476
  11. W. K. In, D. H. Hwang, Y. J. Yoo, and S. Q. Zee, 'Assessment of Core Protection and Monitoring Systems for an Advanced Reactor SMART,' Annals of Nuclear Energy, 29, pp. 609-621, (2002) https://doi.org/10.1016/S0306-4549(01)00058-5
  12. G. C. Lee, W. P. Baek, and S. H. Chang, 'Improved Methodology for Generation of Axial Flux Shapes in Digital Core Protection Systems,' Annals of Nuclear Energy, 29, pp. 805-819, (2002) https://doi.org/10.1016/S0306-4549(01)00076-7
  13. R. Jang, 'ANFIS : Adaptive network-based fuzzy inference system,' IEEE Trans. Syst., Man, Cybern., Vol.23, pp.665-685, May-June, 1993 https://doi.org/10.1109/21.256541
  14. T. Takagi and M. Sugeno, 'Fuzzy Identification of Systems and Its Applications to Modeling and Control,' IEEETrans.System,Man,Cybern., 1, pp. 116-132, (1985)
  15. B. O. Cho, et al., MASTER-2.0: Multipurpose Analyzer for Static and Transient Effects of Reactors. KAERI, KAERI/TR-1211/99,(1999)