Izvestiya vuzov. Yadernaya Energetika

The peer-reviewed scientific and technology journal. ISSN: 0204-3327

Intelligent Decision Support System for the Management of Turbine Feed Pumps at the Kalinin NPP

9/20/2024 2024 - #03 Modelling processes at nuclear facilities

Gorbunov V.A. Lonshakov N.A. Teplyakova S.S. Mechtaeva M.N. Mineev P.A.

DOI: https://doi.org/10.26583/npe.2024.3.10

UDC: 621.039.4

The paper presents the results of developing an intelligent decision support system for analyzing the operating quality of turbine feed pumps at the Kalinin NPP. An algorithm is described for the intelligent system development based on the neural network simulation technology, which is based on the development of a digital twin of a turbine feed pump consisting of a steam turbine, a feed pump, a reducer, steam distribution devices, an automation circuit of control devices, a drive turbine condenser, and a booster pump. The software package was developed based on statistical data obtained in the process of the turbine feed pumps operation. The intelligent decision-making system is based on analyzing the efficiency of the turbine feed pump operation and rationalizing their operating modes in accordance with technically sound energy consumption standards. The computing components for this software package are designed individually for each turbine feed pump based on data from a passive industrial experiment, which makes it possible to take into account the technical condition and peculiarities of the operating mode of each individual element. To build a digital twin, target functions were proposed which reflect the energy efficiency of the pump operation, and variable parameters were investigated from among the number of independent ones. Based on a cluster analysis, the influence of groups of independent parameters on the target function was studied. The results of the intelligent decision support system development for the plant personnel are presented. The results of using the developed software package are presented, and the comparison of the results obtained for the turbine feed pumps of the V-338 and V-320 NPP units is analyzed. The technology application makes it possible to determine the effect of each parameter on the energy efficiency of turbine feed pumps in a given range of operating conditions with a high degree of accuracy. When using the optimization unit built into the computing complex, the intelligent decision-making system allows one to obtain optimal values of the turbine feed pump efficiency and specific heat consumption while varying the parameters set.

References

  1. Vagin V.N., Eremeev A.P. Designing intelligent real-time decision support systems. Proc. of the international Conf. «Intelligent management: New Intelligent technologies in management tasks (ICIT’99)». Moscow, Nauka, Fizmatlit Publ., 1999, pp. 27 – 32 (in Russian).
  2. Gelovani V.A., Bashlykov A.A., Britkov V.B., Vyazilov E.D. Intelligent decision support systems in emergency situations. Moscow, Institute sistemnogo analiza RAN Publ., 2001, 304 p. (in Russian).
  3. Bocharova N.A., Voronov A.V., Slepov M.T. Ranking of information flows in the systems of technical diagnostics of the VVER-1200 power unit. Izvestiya vuzov. Yadernaya Energetika. 2022, no. 3, pp. 65 – 75, DOI: https://doi.org/10.26583/npe.2022.3.06 (in Russian).
  4. Trofimov M.A., Murachev E.G., Rogoza A.A., Egupov N.D. Mathematical modeling of the steam turbine automatic control system. Izvestiya vuzov. Yadernaya Energetika. 2021, no. 4, pp. 99 – 109, DOI: https://doi.org/10.26583/npe.2021.4.09 (in Russian).
  5. Draper N., Smith G. Applied regression analysis. In 2 books. Book 2. Moscow, Finansy i statistika Publ., 1986, 352 p. (in Russian).
  6. Gorbunov V.A., Lonshakov N.A., Nagornaya O.Yu., Belyakov A.A. Improving the accuracy of determining the efficiency parameters of turbine installations using neural networks. Vestnik IGEU. 2017, no. 4, pp. 5 – 12, DOI: https://doi.org/10.17588/2072-2672.2017.4.005-012 (in Russian).
  7. Shelegov A.S., Leskin S.T., Slobodchuk V.I. Pumping equipment of NPP: Textbook for universities. M.: NRNU MEPhI, 2011, 348 p. (in Russian).
  8. Gorbunov V.A., Lonshakov N.A. Optimization of energy consumption of turbochargers of the AES block of neural network modeling methods. Trudy NGTU im. R.E. Alekseeva. 2018, no. 3 (122), pp. 54 – 60, DOI: https://doi.org/10.46960/1816-210X_2018_3_54 (in Russian).
  9. Sozykin A.V. Review of deep neural network training methods. Vestnik YuUrGU. Series: Computational Mathematics and Computer Science. 2017, no. 3, pp. 28 – 59, DOI: https://doi.org/10.14529/cmse170303 (in Russian).
  10. Vasiliev V.A. Determination of the efficiency of a turbopump pumping unit by thermodynamic method. Vestnik YuUrGU. 2005, no. 1, pp. 162–167. Available at: https://elibrary.ru/download/elibrary_13011076_26031472.pdf (дата доступа 30.08.2023) (in Russian).
  11. Gmurman V.E. Probability theory and mathematical statistics: Textbook for universities. Ed. 9, stereo. Moscow, Vysshaya shkola Publ., 2003, 479 p. (in Russian).
  12. Dunaev V.A., Lonshakov N.A., Gorbunov V.A. On the issue of improving the efficiency and safety of operation of thermal and mechanical equipment of nuclear power plants. Global nuclear safety. 2015, no. 2(15), pp. 63 – 70 Available at: http://gns.mephi.ru/sites/default/files/journal/file/ru.2015.2-8.pdf (accessed 30.08.2023) (in Russian).
  13. Idelchik I. E. Handbook of hydraulic resistances. Edited by M.O. Steinberg. Ed.3, revised and enlarged. Moscow, Mashinostroenie Publ., 1992, 672 p. (in Russian).
  14. Karpenko A.P. Modern search engine optimization algorithms. Moscow, MGTU im. N.E. Baumana Publ., 2014, 446 p. (in Russian).
  15. Gorbunov V.A., Lonshakov N.A. Analysis of the efficiency of a turbine feed pump. The state and prospects of development of electrical and thermal technology (Benardos readings): collection of scientific papers based on the materials of the International (XX All-Russian) Scientific and Technical Conference, May 29-31, 2019. Ivanovo, IGEU Publ., 2019, vol. 2, pp. 77 – 80 (in Russian).
  16. Filipchuk Yu.D. Evaluation of the efficiency of pulsed unloading of turbines of NPP power units to increase dynamic stability. Energetika. Izvestiya vysshikh uchebnykh zavedeniy i energeticheskikh ob”edineniy SNG. 2012, no. 3, pp. 40 – 43 (in Russian).
  17. Volkov A.V., Parygin A.G., Khovanov G.P., Naumov A.V. Improving the efficiency of centrifugal pumps in operation. Novosti teplosnabzheniya. 2010, no. 10, p. 122 (in Russian).
  18. Gorbunov V.A., Teplyakova S.S., Lonshakov N.A., Andrianov S.G., Mechtaeva M.N., Mineev P.A. Methodology to assess impact of operational parameters on operation of turbine feed pump. Vestnik IGEU. 2022, no. 4, pp. 14 – 23. DOI: https://doi.org/10.17588/2072-2672.2022.4.014-023 (in Russian).

nuclear power plants turbine feed pump neural network simulation efficiency specific cost of thermal energy BOP energy

Link for citing the article: Gorbunov V.A., Lonshakov N.A., Teplyakova S.S., Mechtaeva M.N., Mineev P.A. Intelligent Decision Support System for the Management of Turbine Feed Pumps at the Kalinin NPP. Izvestiya vuzov. Yadernaya Energetika. 2024, no. 3, pp. 125-140; DOI: https://doi.org/10.26583/npe.2024.3.10 (in Russian).

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