Development of an innovative model for the inter-integration of the architecture of the intelligent computer environment of critical infrastructure facilities of the railway

Authors

DOI:

https://doi.org/10.15587/2706-5448.2023.291244

Keywords:

computer environment, facilities management, power supply system, innovative model, critical infrastructure of railway transport

Abstract

The object of research is the processes of intelligent management of the computer environment of data objects of the critical infrastructure of railway transport. When developing an innovative model of deep mutual integration of the architecture of an intelligent computer environment for the management of objects in the power supply system of railway transport, the only urgent task is to solve the problems of efficient and reliable power supply of electricity for train traction to ensure the transportation process. The approaches, mathematical models and methods that became the basis for the creation of an innovative model and a new structure of the management system, the integration of the architecture of the computer environment of data of critical objects of the infrastructure of railway transport, meet the requirements of modernity and the strategy of sustainable development of the transport infrastructure.

Management of an intelligent computer environment in the context of the railway power supply system is a complex process that includes the use of various technologies and strategies to optimize the functioning of systems, increase reliability, ensure efficient use of electricity, and ensure the transportation process. The basis of the creation of an intelligent computer environment is the principle of a single informational and synchronous space for the formation of primary information, which is an important concept in the development of intelligent control systems for the power supply system of railway transport. This principle requires that all parameters and data that are collected from different systems of power supply facilities should be combined in a single information space and be available for analysis and management in real time. This principle creates the basis for innovative solutions in the field of railway power supply system management, and the use of the Internet of Things, artificial intelligence, machine learning and deep learning allows the development of systems that meet modern requirements for the efficiency and reliability of energy systems.

The use of the approach of deep mutual integration of the architecture of the computer environment is key in the possibility of automating the processes of data collection and analysis, as well as in improving the interaction between the components of the systems of power supply facilities of railway transport, the reliability and efficiency of the system, which makes it more flexible and adaptive to changes in load and working conditions.

The research presented in the work can be used in practice in organizations, structural units and at the levels of the management system of critical infrastructure objects in railway transport, transport sector enterprises, which will allow a quick response to an emergency situation and switch to backup modes, ensuring reliability and availability of power supply in conditions of challenges.

Supporting Agency

  • The article was written within the framework of the Project 2022.01/0224 «Development of scientific principles for comprehensive improvement of safety, efficiency of operation and management of critical railway transport facilities in the conditions of post-war development of Ukraine» under the competition «Science for the reconstruction of Ukraine in the war and post-war periods» with the financial support of the National Research Foundation of Ukraine.

Author Biographies

Halyna Holub, State University of Infrastructure and Technologies

PhD, Associate Professor

Department of Automation and Computer-Integrated Technologies of Transport

Olexsandr Gorobchenko, State University of Infrastructure and Technologies

Doctor of Technical Sciences, Professor

Department of Electromechanics and Rolling Stock of Railways

Ivan Kulbovskyi, State University of Infrastructure and Technologies

PhD, Associate Professor

Department of Automation and Computer-Integrated Technologies of Transport

Sergey Goolak, State University of Infrastructure and Technologies

PhD, Associate Professor

Department of Electromechanics and Rolling Stock of Railways

Oles Haidenko, Kyiv Electromechanical Professional Pre-Higher College

PhD, Senior Lecturer

References

  1. Stasiuk, O., Kuznetsov, V., Zubok, V., Goncharova, L., Muntian, A. (2022). Mathematical Models of Effective Topology of Computer Networks for Electric Power Supply Control on Railway Transport. Communications – Scientific Letters of the University of Zilina, 24 (2), C27–C32. doi: https://doi.org/10.26552/com.c.2022.2.c27-c32
  2. Stasiuk, O. I., Goncharova, L. L. (2018). Mathematical Models and Methods for Analyzing Computer Control Networks of Railway Power Supply. Cybernetics and Systems Analysis, 54 (1), 165–172. doi: https://doi.org/10.1007/s10559-018-0017-0
  3. Holub, H., Kulbovskyi, I., Skliarenko, I., Bambura, O., Tkachuk, M. (2019). Research of methods for identification of emergency modes of power supply system in transport infrastructure projects. Technology Audit and Production Reserves, 5 (2 (49)), 34–36. doi: https://doi.org/10.15587/2312-8372.2019.182830
  4. Stasiuk, A. I., Tutik, V. L., Goncharova, L. L., Golub, G. M. (2015). Mathematical models and computer-oriented methods of monitoring and identification of electric traction networks emergency conditions. Informatsiino-keruiuchi systemy na zaliznychnomu transporti, 2, 7–13. Available at: http://nbuv.gov.ua/UJRN/Ikszt_2015_2_3
  5. Kyrylenko, O. V., Blinov, I. V. (2008). Vyznachennia poshkodzhen na liniiakh elektroperedachi z vykorystanniam shtuchnykh neironnykh merezh. Naukovi pratsi DonNTU. Elektrotekhnika i enerhetyka, 8, 9–12.
  6. Stohnii, B. S., Kyrylenko, O. V., Denysiuk, S. P. (2010). Intelektualni elektrychni merezhi elektroenerhetychnykh system ta yikhnie tekhnolohichne zabezpechennia. Tekhnichna elektrodynamika, 6, 44–50.
  7. EPRI Smart Grid Demonstration Initiative. Two year update (2010). Electric Power Research Institute (EPRI). California.
  8. Strategic Deployment document for Europe’s Electricity Networks of the Future (2010). European Technology Platform – Smart grids.
  9. Gayathri, K., Kumarappan, N. (2010). Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network. Expert Systems with Applications, 37 (12), 8822–8830. doi: https://doi.org/10.1016/j.eswa.2010.06.016
  10. Sukhodolia, O. M. (2022). Shtuchnyi intelekt v enerhetytsi: analitychna dopovid. Kyiv: NISD, 49. doi: https://doi.org/10.53679/niss-analytrep.2022.09
  11. Renewables 2022 (2022). International Energy Agency. Available at: https://www.iea.org/reports/renewables-2022
  12. How will Blockchain Benefit the Energy Industry? Consensys. Available at: https://consensys.net/blockchain-use-cases/energy-and-sustainability/
  13. Oleksiiovets, O. I., Kopishynska, K. O. (2023). Vprovadzhennia tsyfrovykh tekhnolohii enerhetychnymy pidpryiemstvamy dlia rozvytku vidnovliuvanoi enerhetyky. Biznes, innovatsii, menedzhment: problemy ta perspektyvy. Kyiv, 133–134.
Development of an innovative model for the inter-integration of the architecture of the intelligent computer environment of critical infrastructure facilities of the railway

Downloads

Published

2023-11-22

How to Cite

Holub, H., Gorobchenko, O., Kulbovskyi, I., Goolak, S., & Haidenko, O. (2023). Development of an innovative model for the inter-integration of the architecture of the intelligent computer environment of critical infrastructure facilities of the railway. Technology Audit and Production Reserves, 6(2(74), 20–27. https://doi.org/10.15587/2706-5448.2023.291244

Issue

Section

Systems and Control Processes