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Optimized fractional-order PID controller based on nonlinear point kinetic model for VVER-1000 reactor

  • Riham M. Refeat and Rania A. Fahmy EMAIL logo
Published/Copyright: February 14, 2022
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Abstract

Nuclear reactor dynamics are nonlinear and time-varying, so the power level control is a challenging problem in nuclear power plants (NPPs) to ensure both its operation stability and efficiency. An important measure to improve the safety of the reactor core of NPP is the implementation of robust control for the core by adjusting the inserted reactivity of the control rods. Thus in the present paper, fractional-order PID (FOPID) controller is developed as it is well known for its simplicity and robustness against disturbances. A Genetic Algorithm (GA) is used to determine FOPID controller parameters to achieve the desired power level for the generation III+ reactor VVER-1000. Implementing the GA, a suitable objective function is proposed to search for the optimal FOPID parameters. The nonlinear model of the VVER-1000 nuclear reactor is presented based on the point kinetic equations with six delayed neutron groups and temperature feedback from lumped fuel and coolant temperatures. Two cases for the VVER-1000 reactor are investigated; the changes in the power loads and the control rod withdrawal that leads to reactivity disturbance. Moreover, the uncertainties that result from model parameters perturbation are added to examine the controller robustness. The simulation results show that the proposed optimized FOPID controller can track the desired power level of the VVER-1000 reactor and robustly cope with any load changes, disturbances, or any parameters uncertainties. Also, it proves the superiority of the proposed optimized FOPID controller over other PID controllers in ensuring the safe and effective operation of the VVER-1000 reactor.


Corresponding author: Rania A. Fahmy, Operational Safety and Human Factors Department, Egyptian Nuclear and Radiological Regulatory Authority (ENRRA), Cairo, Egypt, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Alavi, Z., Menhaj, M., and Eliasi, H. (2009). Model reference adaptive control of a nuclear reactor. In: 2009 International Conference on Mechatronics and Automation.10.1109/ICMA.2009.5246128Search in Google Scholar

Anglart. (2011). Nuclear reactor dynamics and stability. KTH Royal Institute of Technology, Stockholm.Search in Google Scholar

Ansarifar, G. and Rafiei, M. (2015). Higher order sliding mode controller design for a research nuclear reactor considering the effect of xenon concentration during load following operation. Ann. Nucl. Energy 75: 728–735, https://doi.org/10.1016/j.anucene.2014.09.021.Search in Google Scholar

Åström, K.J. and Hägglund, T. (1994). PID controllers theory, design and tuning, 2nd ed. Instrument Society of America, Research Triangle Park, North Carolina.Search in Google Scholar

Åström, K.J. and Hägglund, T. (2006). Advanced PID control. International Society of Automation, Research Triangle Park, North Carolina.Search in Google Scholar

Bhase, S.S. and Patre, B.M. (2014). Robust FOPI controller design for power control of PHWR under step-back condition. Nucl. Eng. Des. 274: 20–29, https://doi.org/10.1016/j.nucengdes.2014.03.041.Search in Google Scholar

Bongulwar, M. and Patre, B. (2017). Design of PIλDμ controller for global power control of pressurized heavy water reactor. ISA Trans. 69: 234–241, https://doi.org/10.1016/j.isatra.2017.04.007.Search in Google Scholar PubMed

Cao, J.-Y., Liang, J., and Cao, B.-G. (2005). Optimization of fractional order PID controllers based on genetic algorithms. In: 2005 International Conference on Machine Learning and Cybernetics, Vol. 9, Guangzhou, China, pp. 5686–5689.10.1109/ICMLC.2005.1527950Search in Google Scholar

Duderstadt, J.J. and Hamilton, L.J. (1976). Nuclear reactor analysis. John Wiley and Sons Inc., New York.Search in Google Scholar

Eliasi, H., Menhaj, M., and Davilu, H. (2012). Robust nonlinear model predictive control for a PWR nuclear power plant. Prog. Nucl. Energy 54: 177–185, https://doi.org/10.1016/j.pnucene.2011.06.004.Search in Google Scholar

Glasstone, S. and Sesonske, A. (1981). Nuclear reactor engineering. Van Nostrand Reinhold Company, United States.Search in Google Scholar

Jayachitra, A. and Vinodha, R. (2014). Genetic algorithm based PID controller tuning approach for continuous stirred tank reactor. Adv. Artif. Intell. 2014: 1–8, https://doi.org/10.1155/2014/791230.Search in Google Scholar

Johnson, M., Lucas, S., and Tsvetkov, P. (2010). Modeling of reactor Kinetics and dynamics. INL/EXT-10-19953. Idaho National Laboratory, United States.10.2172/989898Search in Google Scholar

Khoshahval, F. and Ahdavi, A. (2016). Determination of the maximum speed of WWER-1000 nuclear reactor control rods. Ann. Nucl. Energy 87: 58–68, https://doi.org/10.1016/j.anucene.2015.06.044.Search in Google Scholar

Krohling, R.A. and Rey, J.P. (2001). Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Trans. Evol. Comput. 5: 78–82, https://doi.org/10.1109/4235.910467.Search in Google Scholar

Lamba, R., Singla, S., and Sondhi, S. (2017). Fractional order PID controller for power control in perturbed pressurized heavy water reactor. Nucl. Eng. Des. 323: 84–94, https://doi.org/10.1016/j.nucengdes.2017.08.013.Search in Google Scholar

Li, G. and Zhao, F. (2013). Load following control and global stability analysis for PWR core based on multi-model, LQG, IAGA and flexibility idea. Prog. Nucl. Energy 66: 80–89, https://doi.org/10.1016/j.pnucene.2013.03.015.Search in Google Scholar

Liu, X. and Wang, M. (2014). Nonlinear fuzzy model predictive control for a PWR nuclear power plant. Math. Probl Eng. 2014: 1–10, https://doi.org/10.1155/2014/908526.Search in Google Scholar

Monje, C.A., Chen, Y.Q., Vinagre, B.M., Xue, D., and Feliu, V. (2010). Fractional-order systems and controls. Fundamentals and applications. Berlin/Heidelberg, Germany: Springer.10.1007/978-1-84996-335-0Search in Google Scholar

Mousakazemi, S. (2019). 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 129: 487–502, https://doi.org/10.1016/j.anucene.2019.02.019.Search in Google Scholar

Mousakazemi, S. (2020a). Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional–integral–derivative controller tuning of a pressurized water nuclear reactor. Ann. Nucl. Energy 136: 107019, https://doi.org/10.1016/j.anucene.2019.107019.Search in Google Scholar

Mousakazemi, S. (2020b). Comparison of the error-integral performance indexes in a GA-tuned PID controlling system of a PWR-type nuclear reactor point-kinetics model. Prog. Nucl. Energy 132: 103604, https://doi.org/10.1016/j.pnucene.2020.103604.Search in Google Scholar

Mousakazemi, S., Ayoobian, N., and Ansarifar, G. (2018). Control of the pressurized water nuclear reactors power using optimized proportional–integral–derivative controller with particle swarm optimization algorithm. Nucl. Eng. Technol. 50: 877–885, https://doi.org/10.1016/j.net.2018.04.016.Search in Google Scholar

Na, M. G., Hwang, I. L., and Lee, Y. J. (2006). Design of a fuzzy model predictive power controller for pressurized water reactors. IEEE Trans. Nucl. Sci. 53: 1504–1514, https://doi.org/10.1109/TNS.2006.871085.Search in Google Scholar

Nagaraj, B., Subha, S., and Rampriya, B. (2008). Tuning algorithms for PID controllers using soft computing techniques. Int. J. Comput. Sci. Netw. Secur. 8: 278–281.Search in Google Scholar

Podlubny, I. (1999). Fractional differential equations. An introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications, Vol. 198. Cambridge, MA, USA: Mathematics in Science and Engineering; Academic Press.Search in Google Scholar

Salehi, A., Safarzadeh, O., and Kazemi, M. (2019). Fractional order PID control of steam generator water level for nuclear steam supply systems. Nucl. Eng. Des. 342: 45–59, https://doi.org/10.1016/j.nucengdes.2018.11.040.Search in Google Scholar

Shah, P. and Agashe, S. (2016). Review of fractional PID controller. Mechatronics 38: 29–41, https://doi.org/10.1016/j.mechatronics.2016.06.005.Search in Google Scholar

Tabadar, Z., Hadad, K., Nematollahi, M., Jabbari, M., Khaleghi, M., and Hashemi-Tilehnoee, M. (2012). Simulation of a control rod ejection accident in a VVER-1000/V446 using RELAP5/Mod3.2. Ann. Nucl. Energy 45: 106–114, https://doi.org/10.1016/j.anucene.2012.02.018.Search in Google Scholar

Tejado, I., Vinagre, B., Traver, J., Prieto-Arranz, J., and Nuevo-Gallardo, C. (2019). Back to basics: meaning of the parameters of fractional order PID controllers. Mathematics 7: 530, https://doi.org/10.3390/math7060530.Search in Google Scholar

Visioli, A. (2001). Optimal tuning of PID controllers for integral and unstableprocesses. IEE Proc. Control Theor. Appl. 148: 180–184, https://doi.org/10.1049/ip-cta:20010197.10.1049/ip-cta:20010197Search in Google Scholar

Received: 2021-05-15
Published Online: 2022-02-14
Published in Print: 2022-02-23

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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