Browsing by Author "Spirintsev, Dmytro V."

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Analysis and Design of the Interval Optimal Electromechanical Systems
(ScientificWorld-NetAkhatAV, Karlsruhe, Germany, 2025) Voliansky, Roman; Kuznetsov, Vitaliy V.; Metelenko, Natalia; Kovalenko, Viktor; Spirintsev, Dmytro V.; Babyak, Mykola O.; Tryputen, Mykola; Tkalenko, Oleksandr; Halushko, Dmytro
ENG: The monograph has been prepared as part of comprehensive scientific research aimed at improving energy efficiency, digitalization, and the development of intelligent control systems. The materials presented reflect the results of many years of interdisciplinary research carried out with the participation of specialists from various scientific schools and universities. They provide a holistic understanding of modern trends in electric drive system control and form a scientific and practical basis for introducing innovative solutions in industry and transport. The discussed concepts of optimization, robust control, and the integration of renewable energy sources are closely linked to the transition to sustainable energy, automation of production processes, and infrastructure digitalization. Special attention is given to interaction with international research programs, which allows the use of advanced experience from Germany, Japan, the USA, and other countries in the field of digital twins, the industrial Internet of Things (Industrial IoT), andcyber-physical control systems.
Homomorphic Filtering in Digital Multichannel Image Processing
(Dnipro University of Technology, Dnipro, Ukraine, 2023) Hnatushenko, Volodymyr V.; Spirintseva, O. V.; Spirintsev, V. V.; Kravets, O. V.; Spirintsev, Dmytro V.
ENG: Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.
Impact of Power Quality on a Synchronous Motor Performance: A Dynamic Model Approach
(Видавництво МДПУ ім. Б. Хмельницького, Запоріжжя, 2025) Kuznetsov, Vitalii V.; Spirintsev, Dmytro V.; Shlykov, S. Yu.; Herashchenko, A.; Kryvenko, O. V.
ENG: The paper presents an advanced dynamic electromagnetic model of a three-phase squirrel-cage asynchronous motor designed to simulate performance under real-world power quality disturbances. The motivation stems from the growing need to address electromagnetic compatibility issues and energy losses in industrial systems exposed to asymmetric voltages and harmonic distortion-conditions common in environments with nonlinear loads such as welding equipment, arc furnaces, and frequency converters. Conventional motor models, typically assuming ideal supply conditions, are inadequate for predicting performance degradation due to poor power quality. To overcome this, the proposed model utilizes space-time complexes and an extended version of the Park-Gorev equations. A key innovation is the introduction of nonlinear magnetic saturation, modeled via a polynomial relationship between mutual inductance and the magnetizing current. This feature enables a more accurate representation of core material behavior under high-load or unbalanced conditions. The simulation was conducted for an MTKH 112-6 asynchronous motor rated at 5.3 kW under two scenarios: (1) ideal sinusoidal three-phase voltage, and (2) real distorted voltage with significant asymmetry and harmonic components up to the 10th order. The analysis revealed that even moderate distortions led to increased stator and rotor losses (from 491.3 W to 498.3 W and 652.2 W to 661.5 W, respectively), a drop in overall efficiency (from 81.4% to 81.2%), and a marked reduction in power factor (from 0.98 to 0.90). Furthermore, current waveform analysis showed visible harmonic deformation, and torque pulsation diagrams indicated increased electromagnetic stress on the motor structure. The proposed model demonstrated a high degree of agreement with experimental data (RMSE < 4%), validating its applicability for use in diagnostics, predictive maintenance, digital twin platforms, and educational simulation environments. Unlike Fourier-based harmonic analysis, the use of space-time complexes allows the system to be modeled holistically, capturing transient behavior and steady-state responses without needing individual harmonic decomposition. This work contributes to the broader field of smart manufacturing and energy-efficient industrial automation. Future improvements include incorporating stochastic modeling to account for dynamic grid variations, enabling probabilistic forecasting and automated control strategies within Industry 4.0 frameworks.