Intelligent system for monitoring the temperature regime of the primary circuit of a nuclear power plant power unit based on fractal-cluster analysis
Abstract
Actuality. Ensuring the effective and safe operation of nuclear power units requires constant monitoring of the temperature parameters of the primary circuit. Of particular importance is the detection of non-standard coolant operating modes and the prevention of heat transfer crises that can lead to accidents. Modern methods of monitoring the temperature regime do not always take into account the complex spatial structure of thermal processes, which reduces their efficiency. Therefore, there is a need to improve the methods of analyzing the temperature field based on the latest approaches, in particular fractal-cluster analysis.
Purpose. Development and improvement of methods for analyzing the temperature field of the first circuit of the VVER-1000 reactor plant, taking into account fractal-cluster features, to increase the accuracy of monitoring, predict emergency operating modes, and optimize the information and control systems of the software and hardware complex of the automated process control system of the power unit of the nuclear power plant.
Research methods. The work uses fractal analysis methods to study temperature fluctuations in the primary circuit of the reactor. A correlation analysis was conducted to establish the relationship between the coolant temperature and the reactor power level. A comparative analysis of existing temperature monitoring systems was performed with further data generalization and formulation of proposals for the integration of new approaches into information and control systems of nuclear power plants.
Results. A close relationship between the temperature parameters of the coolant and the dynamics of the reactor plant operation was shown. A new monitoring method was proposed that takes into account the cluster structure of thermal processes and allows identifying potential crisis zones in heat transfer. The method provides increased sensitivity to changes in the temperature field and can be used for predictive analysis in real time. The advantages of integrating such an approach into the power unit control system were determined.
Conclusions. The proposed method of fractal-cluster analysis of the temperature field allows increasing the efficiency of control over thermal processes in the first circuit of the power unit of a nuclear power plant. It contributes to increasing the reliability of reactor equipment, reducing the risks of crisis situations, and extending the service life. The results obtained can be used to improve control systems and ensure the overall safety of nuclear power units.
Keywords: fractal cluster analysis, information and control systems, monitoring, forecasting, nuclear facility safety
Downloads
References
/References
S. Kiss, S. Lipcsei, “Analysis of propagating temperature perturbations in the primary circuit of PWRs,” Annals of Nuclear Energy, Vol. 85, pp. 1167–1174, 2015. https://doi.org/10.1016/j.anucene.2015.08.001
Y. Xu, H. Jing, L. Xu, Y. Han, L. Zhao, “Microstructural evolution and oxidation behaviour of CF8A austenitic stainless steel during corrosion fatigue in a simulated pressurised water reactor environment,” Corrosion Science, Vol. 163, p. 108286, 2020. https://doi.org/10.1016/j.corsci.2019.108286
S. M. Mostafavi, G. R. Ansarifar, “Pressurizer water level control with estimation of primary circuit coolant mass in nuclear power plants via robust observer based dynamic sliding mode control,” Annals of Nuclear Energy, Vol. 161, p. 108413, 2021. https://doi.org/10.1016/j.anucene.2021.108413
G. R. Ansarifar, “Control of the nuclear steam generators using adaptive dynamic sliding mode method based on the nonlinear model,” Annals of Nuclear Energy, Vol. 88, pp. 280–300, 2016. https://doi.org/10.1016/j.anucene.2015.11.014
J. Zhang, H. Xia, Y. Zhu, Y. Fu, “Research on sensor fault tolerance technology in nuclear power plant control system,” Annals of Nuclear Energy, Vol. 207, p. 110714, 2024. https://doi.org/10.1016/j.anucene.2024.110714
A. A. Amin, K. M. Hasan, “A review of Fault Tolerant Control Systems: Advancements and applications,” Measurement, Vol. 143, pp. 58–68, 2019. https://doi.org/10.1016/j.measurement.2019.04.083
T. Guilbaud, C. Fiorina, S. Lorenzi, A. Scolaro, F. Carminati, D. Maire, A. Pautz, “Investigating the Functional Mock-up Interface as a Coupling Framework for the multi-fidelity analysis of nuclear reactors,” Progress in Nuclear Energy, Vol. 169, p. 105022, 2024. https://doi.org/10.1016/j.pnucene.2023.105022
H. Shao, Z. Gao, X. Liu, K. Busawon, “Parameter-varying modelling and fault reconstruction for wind turbine systems,” Renewable Energy, Vol. 116(B), pp. 145–152, 2018. https://doi.org/10.1016/j.renene.2017.08.083
A. F. Flores, M. Rzehulka, G. Mazzini, “Influence of mixed core in the radionuclides releases and hydrogen production for VVER-1000,” Nuclear Engineering and Design, Vol. 417, p. 112858, 2024. https://doi.org/10.1016/j.nucengdes.2023.112858
J. Bae, G. Kim, S. J. Lee, “Real-time prediction of nuclear power plant parameter trends following operator actions,” Expert Systems with Applications, Vol. 186, p. 115848, 2021. https://doi.org/10.1016/j.eswa.2021.115848
A. Baybulatov, A. Poletikin, M. Byvaykov, “NPP Upper Level Control System Databases: The Problem of Making Changes,” 17th International Conference on Management of Large-Scale System Development, pp. 1–5, 2017. https://doi.org/10.1109/MLSD61779.2024.10739603
A. Ayodeji, M. Mohamed, L. Li, A. Buono, I. Pierce, H. Ahmed, “Cyber security in the nuclear industry: A closer look at digital control systems, networks and human factors,” Progress in Nuclear Energy, Vol. 161, p. 104738, 2023. https://doi.org/10.1016/j.pnucene.2023.104738
P. Budanov, E. Khomiak, I. Kyrysov, K. Brovko, S. Kalnoy, O. Karpenko, “Building a model of damage to the fractal structure of the shell of the fuel element of a nuclear reactor,” Eastern-European Journal of Enterprise Technologies, Vol. 4(8(118)), pp. 60–70, 2022. https://doi.org/10.15587/1729-4061.2022.263374
P. Budanov, Yu. Oliinyk, A. Cherniuk, K. Brovko, “Dynamic Fractal Cluster Model of Informational Space Technological Process of Power Station,” Lecture Notes on Data Engineering and Communications Technologies, Vol. 221, pp. 141–155, 2024. https://doi.org/10.1007/978-3-031-71801-4_11
P. Budanov, Yu. Oliinyk, A. Cherniuk, K. Brovko, “Fractal approach for the researching of information emergency features of technological parameters,” AIP Conference Proceedings, p. 040015, 2024. Iraq, Al-Samawa: Al-Furat Al-Awsat Technical University. https://doi.org/10.1063/5.0191648
Z. Chen, H. Wu, Y. Chen, L. Cheng, B. Zhang, “Patrol robot path planning in nuclear power plant using an interval multi-objective particle swarm optimization algorithm,” Applied Soft Computing, Vol. 116, p. 108192, 2022. https://doi.org/10.1016/j.asoc.2021.108192
D. A. Ejigu, X. Liu, “Dynamic modeling and intelligent hybrid control of pressurized water reactor NPP power transient operation,” Annals of Nuclear Energy, Vol. 173, p. 109118, 2022. https://doi.org/10.1016/j.anucene.2022.109118
E. Jharko, E. Abdulova, K. Chernyshov, “Some Aspects of Intelligent Human-Operators Decision Support Systems for NPP,” IFAC-PapersOnLine, Vol. 55(9), pp. 204–209, 2022. https://doi.org/10.1016/j.ifacol.2022.07.036
J. Li, M. Lin, Y. Li, X. Wang, “Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions,” Energy, Vol. 254(B), p. 124358, 2022. https://doi.org/10.1016/j.energy.2022.124358
K. S. Son, J. W. Lee, S. H. Seong, “Study on the digitalization of trip equations including dynamic compensators for the Reactor Protection System in NPPs by using the FPGA,” Nuclear Engineering and Technology, Vol. 55(8), pp. 2952–2965, 2023. https://doi.org/10.1016/j.net.2023.05.006
Kiss S., Lipcsei S. Analysis of propagating temperature perturbations in the primary circuit of PWRs, Annals of Nuclear Energy, v. 85, 2015, рр 1167-1174. https://doi.org/10.1016/j.anucene.2015.08.001
Xu Y., Jing H., Xu L., Han Y., Zhao L. Microstructural evolution and oxidation behaviour of CF8A austenitic stainless steel during corrosion fatigue in a simulated pressurised water reactor environment. Corrosion Science. 2020. V. 163. P. 108286. https://doi.org/10.1016/j.corsci.2019.108286
Mostafavi S.M., Ansarifar G.R. Pressurizer water level control with estimation of primary circuit coolant mass in nuclear power plants via robust observer based dynamic sliding mode control. Annals of Nuclear Energy. 2021. V. 161. P. 108413. https://doi.org/10.1016/j.anucene.2021.108413
Ansarifar G.R. Control of the nuclear steam generators using adaptive dynamic sliding mode method based on the nonlinear model. Annals of Nuclear Energy. 2016. V. 88. Pp. 280–300. https://doi.org/10.1016/j.anucene.2015.11.014
Zhang J., Xia H., Zhu Y., Fu Y. Research on sensor fault tolerance technology in nuclear power plant control system. Annals of Nuclear Energy. 2024. V. 207. P. 110714. https://doi.org/10.1016/j.anucene.2024.110714
Amin A.A., Hasan K.M. A review of Fault Tolerant Control Systems: Advancements and applications. Measurement. 2019. V. 143. Pp. 58–68. https://doi.org/10.1016/j.measurement.2019.04.083
Guilbaud T., Fiorina C., Lorenzi S., Scolaro A., Carminati F., Maire D., Pautz A. Investigating the Functional Mock-up Interface as a Coupling Framework for the multi-fidelity analysis of nuclear reactors. Progress in Nuclear Energy. 2024. V. 169. P. 105022. https://doi.org/10.1016/j.pnucene.2023.105022
Shao H., Gao Z., Liu X., Busawon K. Parameter-varying modelling and fault reconstruction for wind turbine systems. Renewable Energy. 2018. V. 116(B). Pp. 145–152. https://doi.org/10.1016/j.renene.2017.08.083
Flores A.F., Rzehulka M., Mazzini G. Influence of mixed core in the radionuclides releases and hydrogen production for VVER-1000. Nuclear Engineering and Design. 2024. V. 417. P. 112858. https://doi.org/10.1016/j.nucengdes.2023.112858
Bae J., Kim G., Lee S.J. Real-time prediction of nuclear power plant parameter trends following operator actions. Expert Systems with Applications. 2021. V. 186. P. 115848. https://doi.org/10.1016/j.eswa.2021.115848
Baybulatov A., Poletikin A., Byvaykov M. NPP Upper Level Control System Databases: The Problem of Making Changes. 17th International Conference on Management of Large-Scale System Development. 2017. Pp. 1–5. https://doi.org/10.1109/MLSD61779.2024.10739603
Ayodeji A., Mohamed M., Li L., Buono A., Pierce I., Ahmed H. Cyber security in the nuclear industry: A closer look at digital control systems, networks and human factors. Progress in Nuclear Energy. 2023. V. 161. P. 104738. https://doi.org/10.1016/j.pnucene.2023.104738
Budanov P., Khomiak E., Kyrysov I., Brovko K., Kalnoy S., Karpenko O. Building a model of damage to the fractal structure of the shell of the fuel element of a nuclear reactor. Eastern-European Journal of Enterprise Technologies. 2022. V. 4 (8 (118)). Pp. 60–70. https://doi.org/10.15587/1729-4061.2022.263374
Budanov P., Oliinyk Yu., Cherniuk A., Brovko K. Dynamic Fractal Cluster Model of Informational Space Technological Process of Power Station. Lecture Notes on Data Engineering and Communications Technologies. 2024. V. 221. Pp. 141–155. https://doi.org/10.1007/978-3-031-71801-4_11
Budanov P., Oliinyk Yu., Cherniuk A., Brovko K. Fractal approach for the researching of information emergency features of technological parameters. AIP Conference Proceedings. 2024. Iraq, Al-Samawa: Al-Furat Al-Awsat Technical University. P. 040015. https://doi.org/10.1063/5.0191648
Chen Z., Wu H., Chen Y., Cheng L., Zhang B. Patrol robot path planning in nuclear power plant using an interval multi-objective particle swarm optimization algorithm. Applied Soft Computing. 2022. V. 116. P. 108192. https://doi.org/10.1016/j.asoc.2021.108192
Ejigu D.A., Liu X. Dynamic modeling and intelligent hybrid control of pressurized water reactor NPP power transient operation. Annals of Nuclear Energy. 2022. V. 173. P. 109118. https://doi.org/10.1016/j.anucene.2022.109118
Jharko E., Abdulova E., Chernyshov K. Some Aspects of Intelligent Human-Operators Decision Support Systems for NPP. IFAC-PapersOnLine. 2022. V. 55(9). Pp. 204–209. https://doi.org/10.1016/j.ifacol.2022.07.036
Li J., Lin M., Li Y., Wang X. Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions. Energy. 2022. V. 254(B). P. 124358. https://doi.org/10.1016/j.energy.2022.124358
Son K.S., Lee J.W., Seong S.H. Study on the digitalization of trip equations including dynamic compensators for the Reactor Protection System in NPPs by using the FPGA. Nuclear Engineering and Technology. 2023. V. 55(8). Pp. 2952–2965. https://doi.org/10.1016/j.net.2023.05.006
