An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

Mohd Sabri Minhat; Nurul Adilla Mohd Subha; Fazilah Hassan; Norjulia Mohamad Nordin

doi:10.15282/jmes.14.1.2020.13.0498

Authors

Mohd Sabri Minhat Reactor Instrumentation and Control Engineering, Reactor Technology Centre, Malaysia Nuclear Agency, Bangi, 43000 Kajang, Selangor, Malaysia.
Nurul Adilla Mohd Subha School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia. Phone: +60189564289.
Fazilah Hassan School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia. Phone: +60189564289.
Norjulia Mohamad Nordin School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia. Phone: +60189564289.

DOI:

https://doi.org/10.15282/jmes.14.1.2020.13.0498

Keywords:

Control rod selection algorithm, control rod worth, core power control, feedback control algorithm, Reaktor TRIGA PUSPATI

Abstract

The 1 MWth TRIGA PUSPATI Reactor known as RTP undergoes more than 37 years of operation in Malaysia. The current core power control utilized Feedback Control Algorithm (FCA) and a conventional Control Rod Selection Algorithm (CRSA). However, the current power tracking performance suffers and increase the workload on Control Rod Drive Mechanism (CRDM) if the range between minimum and maximum rod worth value for each control rod has a significant difference. Thus, it is requiring much time to keep the core power stable at the power demand value within the acceptable error bands for the safety requirement of the RTP. In conventional CRSA, regardless of the rod worth value, the lowest position of the control rod is selected for up-movement to regulate the reactor power with 2% chattering error. To improve this method, a new CRSA is introduced named Single Control Absorbing Rod (SCAR). In SCAR, only one rod with highest reactivity worth value will be selected for coast tuning during transient and the lowest reactivity worth value will be selected for fine-tuning rod movement during steady-state. The simulation model of the reactor core is represented based on point kinetics model, thermal-hydraulic models and reactivity model. The conventional CRSA model included with control rod position dynamic model and actual reactivity worth curve data from RTP. The FCA controller is designed based on Proportional-Integral (PI) controller using MATLAB Simulink simulation. The core power control system is represented by the integration of a reactor core model, CRSA model and FCA controller. To manifest the effectiveness of the proposed SCAR algorithm, the results are compared to the conventional CRSA in both simulation and experimentation. Overall, the results shows that the SCAR algorithm offers generally better results than the conventional CRSA with the reduction in rising time up to 44%, workload up to 35%, settling time up to 26% and chattering error up to 18% of the nominal value.

References

Torabi M, Lashkari A, Masoudi SF, Bagheri S. Neutronic analysis of control rod effect on safety parameters in Tehran research reactor. Nuclear Engineering and Technology, 2018; 50(7): 1017–1023.

Malaysia Nuclear Agency. Safety analysis report for PUSPATI TRIGA reactor. NUKLEARMALAYSIA/L/2014/12(S); 2014.

Kim JH, Park SH, Na MG. Design of a model predictive load-following controller by discrete optimization of control rod speed for PWRs. Annals of Nuclear Energy, 2014; 71: 343–351.

Lee YK, Lee JH, Kim HW, Kim SK, Kim JB. Drop performance test of conceptually designed control rod assembly for prototype generation iv sodium-cooled fast reactor. Nuclear Engineering and Technology, 2017; 49(4): 855–864.

Shirazi SAM. The simulation of a model by SIMULINK of MATLAB for determining the best ranges for velocity and delay time of control rod movement in LWR reactors. Progress in Nuclear Energy, 2012; 54(1): 64–67.

Badgujar KD. System science and control techniques for harnessing nuclear energy. Systems Science and Control Engineering, 2016; 4(1): 138–164.

Andraws MS, Abd AA, Yousef AH, Mahmoud II, Hammad SA. Performance of receding horizon predictive controller for research reactor. In: 12th International Conference on Computer Engineering and System, Cairo, Egypt; 19-20 December, 2017.

Coban R, Can B. A trajectory tracking genetic fuzzy logic controller for nuclear research reactors. Energy Conversion and Management, 2010; 51(3) 587–593.

Kaiba T, Žerovnik G, Jazbec A, Štancar Ž, Barbot L, Fourmentel D, Snoj L. Validation of neutron flux redistribution factors in JSI TRIGA reactor due to control rod movements. Applied Radiation and Isotopes, 2015; 104: 34–42.

Bhowmik PK, Dhar SK, Chakraborty S. Operation and control of TRIGA nuclear research reactor with PLC. International Journal of Information and Electronics Engineering, 2013; 3(6): 553–557.

Coban R. A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm. Nuclear Engineering and Design, 2011; 241(5): 1899–1908.

Coban R. Power level control of the TRIGA Mark-II research reactor using the multifeedback layer neural network and the particle swarm optimization. Annals of Nuclear Energy, 2014; 69: 260–266.

Borio Di Tigliole A, Cammi A, Gadan MA, Magrotti G, Memoli V. Study of a new automatic reactor power control for the TRIGA Mark II reactor at University of Pavia. In: 1st International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications, Marseille, France; 7-10 June, 2009.

Lee YJ. The control rod speed design for the nuclear reactor power control using optimal control theory. Journal of the Korean Nuclear Society, 1994; 26(4): 536–547.

Edwards RM, Lee KY, Schultz MA. State feedback assisted classical control : an incremental approach to control modernization of existing and future nuclear reactors and power plants. Nuclear Technology, 1990; 92: 167–185.

Edwards RM, Weng CK, Lindsay RW. Experimental development of power reactor advanced. In: 8th Power Plant Dynamics, Control and Testing Symposium, Tennessee, United States; 27-29 May, 1992.

Suzuki K, Shimazaki J, Shinohara Y. Application of H∞ control theory to power control of a nonlinear reactor model. Nuclear Science and Engineering, 1993; 115: 142–151.

Ansarifar GR, Rafiei M. Second-order sliding-mode control for a pressurized water nuclear reactor considering the xenon concentration feedback. Nuclear Engineering and Technology, 2015; 47(1): 94-101.

Arab-Alibeik H, Setayeshi S. An adaptive-cost-function optimal controller design for a PWR nuclear reactor Annals of Nuclear Energy, 2003; 30(6): 739–754.

Davijani NZ, Jahanfarnia G, Abharian AE. Nonlinear fractional sliding mode controller based on reduced order FNPK model for output power control of nuclear research reactors. IEEE Transactions on Nuclear Science, 2017; 64(1): 713–723.

Edwards RM, Lee KY, Ray A. Robust optimal control of nuclear reactors and power plants. Nuclear Technology, 1991; 98(2): 137–148.

Wang L, Wei X, Zhao F, Fu X. Modification and analysis of load follow control without boron adjustment for CPR1000. Annals of Nuclear Energy, 2014; 70: 317–328.

Pérez-Cruz JH, Poznyak A. Automatic startup of nuclear reactors using differential neural networks. IFAC Proceeding Volumes, 2007; 40(20): 112–117.

Rivero-Gutiérrez T, Benítez-Read JS, Segovia-De-Los-Ríos A, Longoria-Gándara LC, Palacios-Hernández JC. Design and implementation of a fuzzy controller for a TRIGA mark III reactor. Science and Technology of Nuclear Installations, 2012; 415805: 1-9.

Ansarifar GR, Esteki MH, Arghand M. Sliding mode observer design for a PWR to estimate the xenon concentration & delayed neutrons precursor density based on the two point nuclear reactor model. Progress in Nuclear Energy, 2015; 79: 104–114.

Cammi A, Ponciroli R, Borio Di Tigliole A, Magrotti G, Prata M, Chiesa D, Previtali E. A zero dimensional model for simulation of TRIGA Mark II dynamic response. Progress in Nuclear Energy, 2013; 68: 43–54.

Pérez-cruz JH, Poznyak A. Design of a sliding mode neurocontroller for a nuclear research reactor. In: 8th International IFAC Symposium on Dynamics and Control of Process Systems, Cancún, Mexico; 4-6 June, 2007.

Li G, Liang B, Wang X, Li X. Multivariable modeling and nonlinear coordination control of nuclear reactor cores with/without xenon oscillation using H∞ loop shaping approach. Annals of Nuclear Energy, 2018; 111: 82–100.

Ravivarman R, Palaniradja K, Prabhu Sekar R. Performance enhancement of normal contact ratio gearing system through correction factor. Journal of Mechanical Engineering and Sciences, 2019; 13(3): 5242–5258.

Eom M, Chwa D, Baang D. Robust disturbance observer-based feedback linearization control for a research reactor considering a power change rate constraint. IEEE Transactions on Nuclear Science, 2015; 62(3): 1301–1312

Akin HL, Altin V. Rule-based fuzzy logic controller for a PWR-type nuclear power plant. IEEE Transactions on Nuclear Science, 1991; 38(2): 883–890.

Rojas-Ramírez E, Benítez-Read JS, Segovia-De-Los Ríos A. A stable adaptive fuzzy control scheme for tracking an optimal power profile in a research nuclear reactor. Annals of Nuclear Energy, 2013; 58: 238–245.

Shamseldin MA, Sallam M, Bassiuny AM, Abdel Ghany AM. Real-time implementation of an enhanced nonlinear PID controller based on harmony search for one-stage servomechanism system. Journal of Mechanical Engineering and Sciences, 2018; 12(4):4161–4179.

Khoshahval F, Aziz A. Determination of the maximum speed of WWER-1000 nuclear reactor control rods. Annals of Nuclear Energy, 2016; 87(2): 58–68.

Arab-Alibeik H, Setayeshi S. Improved temperature control of a PWR nuclear reactor using an LQG / LTR based controller. IEEE Transactions on Nuclear Science, 2003; 50(1): 211–218.

Wang G, Wu J, Zeng B, Xu Z, Wu W, Ma X. Design of a model predictive control method for load tracking in nuclear power plants. Progress in Nuclear Energy, 2017; 101(B): 260–269.

Chen CT. Linear System Theory and Design. 3rd ed. Oxford University Press, Inc; 1999.

Minhat MS, Selamat H, Mohd Subha NA. Adaptive control method for core power control in TRIGA Mark II reactor. IOP Conference Series: Materials Science and Engineering, 2018; 298(012028): 1-15.

Baang D, Suh Y, Kim SH. Power controller design and application to research reactor. In: Transactions of the Korean Nuclear Society Autumn Meeting, Gyeongju, Korea; 26-27 October, 2017.

Baang D, Suh Y, Park C. Feedback power control for TRIGA-II research reactor. In: Transactions of the Korean Nucear Society Spring Meeting, Jeju, Korea; 18–19 May, 2017.

Rabir MH, Jalal Bayar AM, Hamzah NS, Mustafa KA, Abdul Karim J, Mohamed Zin MR. RTP core measurement and simulation of thermal neutron flux distribution in the RTP core. IOP Conference Series: Materials Science and Engineering, 2018; 298(012029): 1–10.

Malaysia Nuclear Agency. PUSPATI TRIGA reactor functional requirement for data acquisition and control system. Re-30-KJ-150-001; 2014.

Qaiser SH, Bhatti AI, Iqbal M, Qadir J. System identification and robust Controller design for pool type research reactor. In: 13th IEEE IFAC International Conference on Methods Models in Automation and Robotics, Szczecin, Poland; 27-30 August; 2007.

Do VD, Dang XK. The fuzzy particle swarm optimization algorithm design for dynamic positioning system under unexpected impacts. Journal of Mechanical Engineering and Sciences, 2019; 13(3): 5407–5423.

Zhang Q, Hu Z, Deng B, Xu M, Guo Y. A simple iterative method for compensating the response delay of a self-powered neutron detector. Nuclear Science and Enginerring, 2017; 186(3): 1–10.