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Load control of the point model of a PWR-type nuclear reactor using a tuned controller with the DE algorithm

Document Type : Research Paper

Author

Department of Physics, Faculty of Basic Sciences, Payame Noor University, P.O.Box: 19395-4697, Tehran - Iran

Abstract

The load control of a nuclear reactor is important due to the nonlinear nature of its dynamics and the dependence of some parameters on the output power. the Proportional-Integral-Derivative controller (PID) is commonly regarded as an easy choice for reliable control. In this research, the relative neutron density in the point kinetics model of a Pressurized Water Reactor (PWR) is controlled by an optimized PID with the meta-heuristic Differential Evolution (DE) algorithm. The Integral of Time-Absolute Error (ITAE) performance index has been used for optimization with this algorithm. The simulation results show that the optimized control system with the DE algorithm has the appropriate efficiency and accuracy in response to power demand.

Highlights

  1. CMNA Pereira, CMF Lapa, Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem, Ann Nucl Energy, 30, 555–565 (2003).

 

  1. C.M.F. Lapa, C.M.N.A. Pereira, A. Mol AC de, Maximization of a nuclear system availability through maintenance scheduling optimization using a genetic algorithm, Nucl Eng Des, 196, 219–231 (2000).

 

  1. De Moura Meneses AA, Machado MD, Schirru R, Particle swarm optimization applied to the nuclear reload problem of a pressurized water reactor, Prog Nucl Energy, 51, 319–326 (2009).

 

  1. D. Babazadeh, M. Boroushaki, C. Lucas, Optimization of fuel core loading pattern design in a VVER nuclear power reactors using Particle Swarm Optimization (PSO), Ann Nucl Energy, 36, 923–930 (2009).

 

  1. Pereira CMNA, Lapa CMF, Mol ACA, Da Luz AF, A particle swarm optimization (PSO) approach for non-periodic preventive maintenance scheduling programming, Prog Nucl Energy, 52, 710-714 (2010).

 

  1. S.M.H. Mousakazemi, N. Ayoobian, G.R. Ansarifar, Control of the pressurized water nuclear reactors power using optimized proportional–integral–derivative controller with particle swarm optimization algorithm, Nucl Eng Technol. Elsevier, 50, 877–885 (2018).

 

  1. S.M.H. Mousakazemi, N. Ayoobian, G.R. Ansarifar, Control of the reactor core power in PWR using optimized PID controller with the real-coded GA, Ann Nucl Energy, Pergamon, 118, 107–121 (2018).

 

  1. R. RCoban, Computational intelligence-based trajectory scheduling for control of nuclear research reactors, Prog Nucl Energy, Elsevier, 52, 415-424 (2010).

 

  1. R. Coban, B. Can, A trajectory tracking genetic fuzzy logic controller for nuclear research reactors, Energy Convers Manag, Elsevier, 51, 587–593 (2010).

 

  1. F. Di Maio, S. Baronchelli, M. Vagnoli, E. Zio, Determination of prime implicants by differential evolution for the dynamic reliability analysis of non-coherent nuclear systems, Ann Nucl Energy, 102, 91–105 (2017).

 

  1. G.T.T. Phan, et al, Application of differential evolution algorithm for fuel loading optimization of the DNRR research reactor, Nucl Eng Des, 362,110582 (2020).

 

  1. G. Sun, et al, Loading pattern optimization method based on discrete differential evolution, Trans Am Nucl Soc, 121, 1518–1520 (2019).

 

  1. D.L. Hetrick, Dynamics of Nuclear Reactors, American Nuclear Society, La Grange Park, (1993).

 

  1. S.S. Khorramabadi, M. Boroushaki, C. Lucas, Emotional learning based intelligent controller for a PWR nuclear reactor core during load following operation, Ann Nucl Energy, 35, 2051-2058 (2008).

 

  1. S.M.H. Mousakazemi, Control of a PWR nuclear reactor core power using scheduled PID controller with GA, based on two-point kinetics model and adaptive disturbance rejection system, Ann Nucl Energy, Pergamon, 129, 487–502 (2019).

 

  1. R. Storn, On the usage of differential evolution for function optimization, Bienn Conf North Am Fuzzy Inf Process Soc - NAFIPS. 519-523 (1996).

 

  1. R. Storn, K. Price, Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J Glob Optim, 11, 341–59 (1997).

 

  1. F.G. Martins, Tuning PID controllers using the ITAE criterion, Int J Eng Educ, 21, 867–873 (2005).

 

  1. K.J. Astrom, T. HÄgglund, Advanced PID control. IEEE Control Syst. ISA-The Instrumentation, Systems and Automation Society, (2006).

Keywords

References
  1. CMNA Pereira, CMF Lapa, Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem, Ann Nucl Energy, 30, 555–565 (2003).

 

  1. C.M.F. Lapa, C.M.N.A. Pereira, A. Mol AC de, Maximization of a nuclear system availability through maintenance scheduling optimization using a genetic algorithm, Nucl Eng Des, 196, 219–231 (2000).

 

  1. De Moura Meneses AA, Machado MD, Schirru R, Particle swarm optimization applied to the nuclear reload problem of a pressurized water reactor, Prog Nucl Energy, 51, 319–326 (2009).

 

  1. D. Babazadeh, M. Boroushaki, C. Lucas, Optimization of fuel core loading pattern design in a VVER nuclear power reactors using Particle Swarm Optimization (PSO), Ann Nucl Energy, 36, 923–930 (2009).

 

  1. Pereira CMNA, Lapa CMF, Mol ACA, Da Luz AF, A particle swarm optimization (PSO) approach for non-periodic preventive maintenance scheduling programming, Prog Nucl Energy, 52, 710-714 (2010).

 

  1. S.M.H. Mousakazemi, N. Ayoobian, G.R. Ansarifar, Control of the pressurized water nuclear reactors power using optimized proportional–integral–derivative controller with particle swarm optimization algorithm, Nucl Eng Technol. Elsevier, 50, 877–885 (2018).

 

  1. S.M.H. Mousakazemi, N. Ayoobian, G.R. Ansarifar, Control of the reactor core power in PWR using optimized PID controller with the real-coded GA, Ann Nucl Energy, Pergamon, 118, 107–121 (2018).

 

  1. R. RCoban, Computational intelligence-based trajectory scheduling for control of nuclear research reactors, Prog Nucl Energy, Elsevier, 52, 415-424 (2010).

 

  1. R. Coban, B. Can, A trajectory tracking genetic fuzzy logic controller for nuclear research reactors, Energy Convers Manag, Elsevier, 51, 587–593 (2010).

 

  1. F. Di Maio, S. Baronchelli, M. Vagnoli, E. Zio, Determination of prime implicants by differential evolution for the dynamic reliability analysis of non-coherent nuclear systems, Ann Nucl Energy, 102, 91–105 (2017).

 

  1. G.T.T. Phan, et al, Application of differential evolution algorithm for fuel loading optimization of the DNRR research reactor, Nucl Eng Des, 362,110582 (2020).

 

  1. G. Sun, et al, Loading pattern optimization method based on discrete differential evolution, Trans Am Nucl Soc, 121, 1518–1520 (2019).

 

  1. D.L. Hetrick, Dynamics of Nuclear Reactors, American Nuclear Society, La Grange Park, (1993).

 

  1. S.S. Khorramabadi, M. Boroushaki, C. Lucas, Emotional learning based intelligent controller for a PWR nuclear reactor core during load following operation, Ann Nucl Energy, 35, 2051-2058 (2008).

 

  1. S.M.H. Mousakazemi, Control of a PWR nuclear reactor core power using scheduled PID controller with GA, based on two-point kinetics model and adaptive disturbance rejection system, Ann Nucl Energy, Pergamon, 129, 487–502 (2019).

 

  1. R. Storn, On the usage of differential evolution for function optimization, Bienn Conf North Am Fuzzy Inf Process Soc - NAFIPS. 519-523 (1996).

 

  1. R. Storn, K. Price, Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J Glob Optim, 11, 341–59 (1997).

 

  1. F.G. Martins, Tuning PID controllers using the ITAE criterion, Int J Eng Educ, 21, 867–873 (2005).

 

  1. K.J. Astrom, T. HÄgglund, Advanced PID control. IEEE Control Syst. ISA-The Instrumentation, Systems and Automation Society, (2006).
Journal of Nuclear Science, Engineering and Technology (JONSAT)
Volume 44, Issue 3 - Serial Number 105
October 2023
Pages 28-37
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