Algorithms for the Adjustment of Technological Process Parameters in Power Installations.

S. Artiukh Ukrainian Engineering and Pedagogical Academy https://orcid.org/0000-0003-0804-6313
V. Kniazieva Ukrainian Engineering and Pedagogical Academy https://orcid.org/0000-0002-3106-4897
N. Antonenko Ukrainian Engineering and Pedagogical Academy https://orcid.org/0000-0002-3106-4897
H. Hrinchenko Ukrainian Engineering and Pedagogical Academy https://orcid.org/0000-0002-6498-6142

Keywords: algorithm, artificial neural networks, neuron, control system, software system

Abstract

DOI: https://doi.org/10.32820/2079-1747-2023-31-33-41

The article considers the possibility of developing algorithms for adapting the control systems of
technological plant parameters based on a neural network approach and designing this control system on
the basis of modern automatic control means. The development of algorithms and software and
hardware for real-time automatic control of power plants was carried out on the basis of the following
tasks: development of the structure of the automatic system and the structure of the control system;
implementation of the software and hardware implementation of the control system, namely the
implementation of the software structure and the software implementation of the proportional-integraldifferential controller. The system of adaptive training of the neural network was implemented.
To solve the tasks, the following methods were used: a neural network approach to solving
problems of finding the optimal parameters of a process controller in power plants; object-oriented
programming for the implementation of a control system. To search for the parameters of a
proportional-integral-differential controller, it is proposed to apply a neural network approach, in
which a neural network is embedded in the control system. The proportional-integral-differential
controller has the widest possibilities for imparting the necessary properties to the control system. It
is used in cases where it is necessary to obtain a high-quality automatic control system without high
costs for research on the synthesis of a more complex control law.
Based on the research results, we analyze the technological process taking place in a steam
boiler, generalize the algorithms for controlling parameters, and select an artificial neural network
apparatus for calculating the coefficients of a proportional-integral-differential controller.

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References

Hrinchenko, NS, Kovtun, OA, Mykolaiko, VV, Nesterenko, RO & Antonenko, NS 2022, “Zabezpechennia ekspluatatsiinoi bezpeky AES u ponadproektnyi termin v konteksti perekhodu do tsyrkuliarnoi ekonomiky: Yevropeiskyi Zelenyi Kurs”, [Ensuring AES operational safety beyond the design period in the context of the transition to a circular economy: European Green Deal], Mashynobuduvannia, iss. 30, pp. 61–72. DOI: https://doi.org/10.32820/2079-1747-2022-30-61-72.

Hrinchenko, N, Trisch, R, Burdeina, V & Chelysheva, S 2019, “Algorithm of technical diagnostics of the complicated damage to the continued resource of the circulation pipeline of the nuclear power plant” Problems of Atomic Science and Technology. Section: Physics of Radiation Effect and Radiation Materials Science, no 2 (120), pp. 104–110. DOI : https://doi.org/10.46813/2019-120-104 .

Hrinchenko, NS, Artiukh, SM, Hrinchenko, VV & Nehodov, SS 2022, “Unifikatsiia metodiv tekhnichnoi diahnostyky truboprovidnykh system z metoiu zabezpechennia bezpechnoi ekspluatatsii”, [Unification of methods for technical diagnostics of pipeline systems to ensure safe operation], Mashynobuduvannia, iss. 29, pp. 62–69. DOI : https://doi.org/10.32820/2079-1747-2022-29 .

Stoliar, OM 2016, “Alhorytmy rehuliuvannia parametriv tekhnolohichnoho protsesu v enerhetychnykh ustanovkakh”, [Algorithms for controlling process parameters in power units], Suchasni kompiuterni informatsiini tekhnolohii : materialy VI Vseukrain. shkoly-seminaru molodykh vchenykh i studentiv ASIT2016 (20-21 trav. 2016 r., m. Ternopil), Ternopil, pp. 51–54.

Podryhailo, M, Polianskyi, O, Podryhailo, N & Baitsur, M 2019, ‘Vplyv podatlyvosti lanok na KKD mekhanizmiv i mashyn’, [Influence of link flexibility on the efficiency of mechanisms and machines], Suchasni tekhnolohii v mashynobuduvanni ta transporti, no 1, pp. 113–120. DOI: https://doi.org/10.36910/automash.v1i12.57.

Yesaulov, S, Babichevа, O & Zakurdai, V 2022, ‘Synthesis of Thermal Diagnostic Expert Components with an Artificial Neuron: Array’ Municipal Economy of Cities, no 1(168), pр. 18–29. DOI : https://doi.org/10.33042/2522-1809-2022-1-168-18-29 .

Tripathi, ND, Reed, JH & Vanlandingham, HF 2001, ‘Fuzzy Logic and Neural Networks’ Radio Resource Management in Cellular Systems. The Springer International Series in Engineering and Computer Science, Vol. 618. DOI : https://doi.org/10.1007/0-306-47318-6_2 .

Pavlenko, Y & Parfyonova, O 2021, ‘The Future of Neural Networks’ Hraal nauky, no 4, pр. 297–298. DOI : https://doi.org/10.36074/grail-of-science.07.05.2021.053 .

Trishch, R, Maletska, O, Hrinchenko, H, Artiukh, S, Burdeina, V & Antonenko, N 2019, ‘Development and validation of measurement techniques according to ISO/IEC 17025:2017’ Proceedings of the 8th International Conference on Advanced Optoelectronics and Lasers (Sozopol, 6-8 September 2019), Sozopol, pр. 715–720.

Akkouchi, K, Rahmani, L & Lebied, R 2021, ‘New application of artificial neural network-based direct power control for permanent magnet synchronous generator’ Electrical Engineering & Electromechanics, no 6, pр. 18–24. DOI : https://doi.org/10.20998/2074-272X.2021.6.03 .

Mendis, N, Muttaqi, KM & Perera, S 2014, ‘Management of battery-supercapacitor hybrid energy storage and synchronous condenser for isolated operation of PMSG based variable-speed wind turbine generating systems’ IEEE Transactions on Smart Grid, Vol. 5, no. 2, pp. 944-953. Doi: https://doi.org/10.1109/tsg.2013.2287874.

IEC 61131-3:2013 Programmable controllers. Part 3: Programming languages 2013, viewed 11 March 2023, <https://webstore.iec.ch/publication/4552>.

Hassan, Adel A, Abo-Zaid, S & Refky, A 2015, ‘Improvement of direct torque control of induction motor drives using neuro-fuzzy controller’ Journal of Multidisciplinary Engineering Science and Technology, Vol. 2, no. 10, pp. 2913-2918.

Halvaei, Niasar A & Rahimi, V 2015, ‘Sensorless Direct Power Control of Induction Motor Drive Using Artificial Neural Network’ Advances in Artificial Neural Systems, Vol. 2015, pp. 1-9. Doi: https://doi.org/10.1155/2015/318589.

Benbouhenni, H 2021, ‘Robust direct power control of a DFIG fed by a five- level NPC inverter using neural SVPWM technique’ Tecnica Italiana-Italian Journal of Engineering Science, vol. 65, no. 1, pp. 119-128. Doi: https://doi.org/10.18280/ti-ijes.650118.