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Improved RRS Logical Architecture using Genetic Algorithm

유전자 알고리즘 적용을 통한 향상된 RRS Logic 개발

  • Received : 2016.11.03
  • Accepted : 2016.12.20
  • Published : 2016.12.31

Abstract

An improved RRS (Reactor Regulating System) logic is implemented in this work using systems engineering approach along with GA (Genetic Algorithm) deemed as providing an optimal solution to a given system. The current system works desirably and has been contributed to the safe and stable NPP operation. However, during the ascent and decent 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 research proposes applying genetic algorithm to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse-engineering is implemented to build a Simulink-based RRS model and re-engineering is followed to apply the GA and to produce a newly-configured RRS generating an output that has a reduced steady state error and diminished overshoot level.

Keywords

References

  1. United States Nuclear Regulatory Commission, APR1400 Design Control Document, 2014. Available from: http://www.nrc.gov [cited 2016 May 20].
  2. Kim Sangjin, Song Byeonggeun, Oh Sejun, Yu Samsang, Advanced Automatic Control, 1st ed. ISBN 978-89-5526-832-4.
  3. K. KRISHNAKUMAR, DAVID E. GOLDBERG, Control System Optimization using Genetic Algorithms, Journal of Guidance, Control, and Dynamics 15 (1992) 735-740. https://doi.org/10.2514/3.20898
  4. Reis, L., Porto, R., and Chaudhry, F, 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)
  5. Cheng-Jian Lin, A GA-based Neural Fuzzy System for Temperature Control. Fuzzy Sets and Systems. 143 (2004) 311-333. https://doi.org/10.1016/S0165-0114(03)00126-X
  6. S.P. Ghoshal, Application of GA/GA-SA based Fuzzy Automatic Generation Control of a Multi-Area Thermal Generating System. Electric Power Systems Research. 70 (2004) 115-127. https://doi.org/10.1016/j.epsr.2003.11.013
  7. Tzuu-Hseng S. Li, Ming-Yuan Shieh, Design of a GA-based Fuzzy PID Controller for Non-Minimum Phase Systems. Fuzzy Sets and Systems. 111 (2000) 183-197. https://doi.org/10.1016/S0165-0114(97)00404-1
  8. P.J. FLEMING, R.C. PURSHOUSE, Genetic Algorithms in Control Systems. Department of Automatic Control and Systems Engineering, University of Sheffield, UK, 2001.
  9. Western Services Corporation, 3KEYMASTER. Available from: http://www.power-technology.com [Cited 2016 June 10].
  10. Kim Hyoil, Jin Ganggyu, Jeon Seunghwan, Genetic algorithm learning on Labview, 1st ed. ISBN 978-89-92649-80-3.
  11. National Instruments, Labview. Available from: http//www.ni.com [Cited 2016 June 12].
  12. Kim Seul, Control System Analysis and Simulink applications, 2nd ed. ISBN 978-89-6364-063-1.
  13. WIKIBOOKS, Control Systems / Block Diagrams. Available from: https://en.wikibooks.org [Cited 2016 June 15].
  14. WIKIPEDIA, Root locus. Available from: https://en.wikipedia.org [Cited 2016 June 18].
  15. Choi Yeonuk, Simulink comprehension and actual controller design, 1st ed. ISBN 979-11-5600-386-1.
  16. Requirements traceability. Available from: https://en.wikipedia.org/wiki/Requirements_traceability [Cited 2016 December 3].