Engineering, Energy, Transport AIC

Volodymyr MYKHALEVYCH – Doctor of Technical Sciences, Full Professor, Department of Electric Power Engineering, Electrical Engineering and Electromechanics, Vinnitsa National Agrarian University (3 Solnechna St., Vinnitsa, 21008, e-mail: mykhalevych@vntu.edu.ua, https://orcid.org/0000-0003-1557-7331)
Mykola KOLISNYK – Doctor of Philosophy in Materials Science, Senior Lecturer Department of Electric Power Engineering, Electrical Engineering and Electromechanics, Vinnitsa National Agrarian University (3, Solnechna str., Vinnitsa, 21008, Ukraine, email: kolisnik30@gmail.com, https://orcid.org/0000-0001-5502-6556). 
Leonid YAROSHENKO – Candidate of technical sciences, associate professor of the Department of Electric Power Engineering, Electrical Engineering and Electromechanics, Vinnitsa National Agrarian University (3, Solnechna str., Vinnitsa, 21008, Ukraine, email: volvinlv@gmail.com, https://orcid.org/0000-0002-0261-006X).
Anatolii SLOBODYANYK – Candidate of Technical Sciences, Associate Professor, Department of Mathematics, Physics and Computer Technologies, Vinnitsa National Agrarian University (3, Solnechna str., Vinnitsa, 21008, Ukraine, email: slobodaniktola7@gmail.com, https://orcid.org/0009-0006-2157-8188).

The article presents the results of a comprehensive study on the implementation of digital twins in control systems for energy facilities using modern hardware and software complexes. Special attention is devoted to analyzing various approaches to building integrated models that combine real physical processes with their virtual analogs in a unified cyber-physical environment. The rationale for selecting the technological base for the development of digital twins in automation systems is provided, focusing on the use of SIMATIC S7-300 programmable logic controllers, Siemens Micromaster 440 frequency converters, and the PROFIBUS industrial communication interface. Such a configuration ensures reliable two-way data exchange between virtual and physical components and enables real-time synchronization of operational parameters.
The principles of two-way synchronization between the digital twin and the physical object in real time are described in detail. Examples of hardware–software integration between PLCs, HMI panels, and communication modules are presented, illustrating the bidirectional flow of information and control signals. The requirements for the mathematical model of the energy process are defined, and a method for its implementation based on dq-coordinate transformation is proposed. This mathematical model allows accurate representation of the electromagnetic and mechanical behavior of an asynchronous motor under variable operating conditions, providing a realistic simulation environment for testing control algorithms before they are applied to the physical system.
The main stages of constructing a digital twin are outlined, including the development of the structural scheme of the control system, description of physical interconnections, selection of model parameters, and formulation of control algorithms. Special emphasis is placed on ensuring data consistency between the simulation environment and the real system, which is achieved through continuous information exchange via industrial networks such as PROFIBUS and MPI. The digital twin architecture developed in this study supports cyclic data updating with minimal delay, ensuring the stability of real-time control processes and the reliability of feedback loops.
The research also explores the practical use of digital twins to improve the operational efficiency of energy systems. It demonstrates their potential for real-time equipment diagnostics, prediction of failure conditions, and optimization of maintenance schedules. A specific example of implementing a digital twin in the control process of an asynchronous electric drive with frequency regulation is presented, highlighting the benefits of predictive monitoring, virtual commissioning, and parameter optimization. The study proves that digital twins can significantly enhance energy efficiency by 10–15%, reduce downtime by up to 30%, and increase the overall reliability and transparency of energy management systems.
The obtained results confirm that the integration of digital twin technology into control systems for energy facilities substantially improves regulation accuracy, reduces maintenance time, and provides a means for real-time visualization and data archiving. Furthermore, the proposed approach lays the groundwork for developing the next generation of intelligent energy systems that combine simulation, data analytics, and automation into a single adaptive framework. Such systems represent an essential step toward the realization of Smart Energy and Industry 4.0 paradigms, where each component of the infrastructure is represented by a dynamic, self-updating virtual counterpart.

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