Сучасні проблеми металургії (ДМетІ)

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ENG: Modern problems of metallurgy (DMetI)

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    Dynamic Modeling of Induction Motor Performance under Power Quality Disturbances
    (Український державний університет науки і технологій, ННІ «Дніпровський металургійний інститут», ІВК ≪Системні технології≫, Дніпро, 2026) Kuznetsov, Vitalii V.; Nikolenko, Anatolii V.; Stopkin, Vasyl V.; Martyntsev, Viacheslav; Tuhushy, Roman; Teslenko, Ivan
    ENG: The paper presents a dynamic electromagnetic model of a three-phase squirrel-cage asynchronous motor developed to simulate its operation under real power quality disturbances. The relevance of this work is driven by the increasing impact of electromagnetic compatibility issues and energy losses in industrial systems exposed to voltage asymmetry and harmonic distortion–conditions typical for networks with nonlinear loads such as welding equipment, arc furnaces, and frequency converters. Traditional motor models, which assume ideal supply conditions, are not sufficient for accurately predicting performance degradation under such disturbances. To address this limitation, the proposed model is based on space-time complexes and an extended form of the Park–Gorev equations. A key feature of the model is the inclusion of magnetic core saturation, represented through a polynomial dependence of mutual inductance on magnetizing current, enabling more realistic simulation under high-load and unbalanced conditions. The model was tested on an MTKH 112-6 motor (5.3 kW) under two scenarios: ideal sinusoidal voltage and distorted asymmetric supply with harmonics up to the 10th order. The results showed that voltage distortion leads to increased losses in the stator (from 491.3 W to 498.3 W) and rotor (from 652.2 W to 661.5 W), a decrease in efficiency (from 81.4% to 81.2%), and a significant drop in power factor (from 0.98 to 0.90). Additionally, distorted current waveforms and torque pulsations confirmed higher electromagnetic stress. The model demonstrated strong agreement with experimental data (RMSE < 4%), confirming its reliability for applications in diagnostics, predictive maintenance, digital twins, and simulation environments. Unlike traditional Fourier-based approaches, the use of space-time complexes enables comprehensive modeling of both steady-state and transient processes without explicit harmonic decomposition. This research contributes to the development of energy-efficient and intelligent industrial systems. Future work will focus on incorporating stochastic elements to account for dynamic variations in power quality, supporting predictive control and advanced automation within Industry 4.0.