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Improved reactor regulating system logical architecture using genetic algorithm

  • Shim, Hyo-Sub (KEPCO International Nuclear Graduate School (KINGS)) ;
  • Jung, Jae-Chun (KEPCO International Nuclear Graduate School (KINGS))
  • Received : 2016.10.19
  • Accepted : 2017.08.18
  • Published : 2017.12.25

Abstract

An improved Reactor Regulating System (RRS) logic architecture, which is combined with genetic algorithm (GA), is implemented in this work. It is devised to provide an optimal solution to the current RRS. The current system works desirably and has contributed to safe and stable nuclear power plant operation. However, during the ascent and descent section of the reactor power, the RRS output reveals a relatively high steady-state error, and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this work proposes to apply GA to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse engineering is implemented to build a Simulink-based RRS model. Reengineering is followed to produce a newly configured RRS to generate an output that has a reduced steady-state error and diminished overshoot level. A full-scope APR1400 simulator is used to examine the dynamic behaviors of RRS and to build the RRS Simulink model.

Keywords

References

  1. United States Nuclear Regulatory Commission, APR1400 Design Control Document [Internet]. 2014 [cited 2016 May 20]. Available from: http://www.nrc.gov.
  2. APR1400 FSAR (Advanced Power Reactor 1400 Final Safety Analysis Report) [Internet]. 2014 [cited 2016 May 20]. Available from: http://www.nrc.gov.
  3. M.G. Na, I.J. Hwang, Y.J. Lee, Design of a fuzzy model predictive power controller for pressurized water reactors, IEEE Trans. Nucl. Sci. 53 (2006) 1504-1514. https://doi.org/10.1109/TNS.2006.871085
  4. S. Saha, S. Das, R. Ghosh, B. Goswami, R. Balasubramanian, A.K. Chandra, S. Das, A. Gupta, Design of a fractional order phase shaper for iso-damped control of a PHWR under step-back condition, IEEE Trans. Nucl. Sci. 57 (2010) 1602-1612. https://doi.org/10.1109/TNS.2010.2047405
  5. H.W. Huang, C. Shih, S. Yih, M.H. Chen, Software failure events derivation and analysis by frame-based technique, Ann. Nucl. Energy 34 (2007) 307-318. https://doi.org/10.1016/j.anucene.2007.01.009
  6. M.H. Butterfield, Dynamics and Control in Nuclear Power Stations, Thomas Telford, London, UK.
  7. J.I. Choi, Y.J. Hah, U.C. Lee, Automatic reactor power control for a pressurized water reactor, Nucl. Technol. 102 (1993) 277-286. https://doi.org/10.13182/NT93-A34823
  8. E. Varin, J. Koclas, R. Roy, Modeling of the CANDU-6 reactor regulating system, Trans. Am. Nucl. Soc. 73 (1995) 428-430.
  9. W. Aleite, H.W. Bock, inventors; Siemens Aktiengesellschaft, assignee, Limit regulation system for pressurized water nuclear reactors, United States Patent US 3,933,580, 1976 Jan 20.
  10. A. Khegai, E. Krjuger, B. Maslenok, V. Nikolaev, V. Zlobin, inventors; Device for engaging and disengaging pinion and rack in drive of nuclear reactor regulating element, United States Patent US 3,778,345. 1973 Dec 11.
  11. D.G. Hurst, F.C. Boyd, Reactor licensing and safety requirements, 72-CAN-102, in: Proceedings, Canadian Nuclear Association Annual Conference, 18, 1972 Jun 11, p. 195.
  12. Albert Z, inventor; Siemens Ag, assignee, Regulating method and apparatus for nuclear reactors, United States Patent US 3,223,589. 1965 Dec 14.
  13. S. Kim, B. Song, S. Oh, S. Yu, Advanced Automatic Control, Bookshill, South Korea.
  14. K. Krishnakumar, D.E. Goldberg, Control system optimization using genetic algorithms, J. Guid. Control Dyn. 15 (1992) 735-740. https://doi.org/10.2514/3.20898
  15. L. Reis, R. Porto, F. Chaudhry, Optimal location of control valves in pipe networks by genetic algorithm, J. Water Resour. Plann. Manage 123 (1997) 317-326. https://doi.org/10.1061/(ASCE)0733-9496(1997)123:6(317)
  16. C.-J. Lin, A GA-based neural fuzzy system for temperature control, Fuzzy Sets Syst. 143 (2004) 311-333. https://doi.org/10.1016/S0165-0114(03)00126-X
  17. S.P. Ghoshal, Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system, Electric Power Syst. Res. 70 (2004) 115-127. https://doi.org/10.1016/j.epsr.2003.11.013
  18. T.-H.S. Li, M.-Y. Shieh, Design of a GA-based fuzzy PID controller for nonminimum phase systems, Fuzzy Sets Syst. 111 (2000) 183-197. https://doi.org/10.1016/S0165-0114(97)00404-1
  19. P.J. Fleming, R.C. Purshouse, Genetic Algorithms in Control Systems, Department of Automatic Control and Systems Engineering, University of Sheffield, UK, 2001.
  20. Western Services Corporation, 3KEYMASTER [Internet]. [cited 2016 June 10]. Available from: http://www.power-technology.com.
  21. H. Kim, G. Jin, S. Jeon, Genetic Algorithm Learning on Labview, Infinitybooks, South Korea.
  22. National Instruments, Labview [Internet]. [cited 2016 June 12]. Available from: http//www.ni.com.
  23. S. Kim, Control System Analysis and Simulink Applications, 2nd ed., Cheongmungak, South Korea
  24. Wikibooks, Control Systems/Block Diagrams [Internet]. [cited 2016 June 15]. Available from: https://en.wikibooks.org.
  25. Y. Choi, Simulink Comprehension and Actual Controller Design, Hong Reung Science, South Korea.
  26. Wikipedia, Root locus [Internet]. [cited 2016 June 18]. Available from: https://en.wikipedia.org.
  27. Wolfram MathWorld, Interpolating Polynomial [Internet]. [cited 2017 July 13]. Available from: http://mathworld.wolfram.com.