Електротехніка і Електромеханіка

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/62894

Офіційний сайт http://eie.khpi.edu.ua/

Журнал публікує оригінальні результати досліджень з аналітичного, чисельного та мультифізичного методів моделювання електрофізичних процесів в електротехнічних електромеханічних та електроенергетичних установках та системах, з розробки нових електротехнічних пристроїв і систем з поліпшеними техніко-економічними та екологічними показниками в таких сферах, як: теоретична електротехніка, інженерна електрофізика, техніка сильних електричних та магнітних полів, електричні машини та апарати, електротехнічні комплекси та системи, силова електроніка, електроізоляційна та кабельна техніка, електричний транспорт, електричні станції, мережі і системи, безпека електрообладнання.

Рік заснування: 2002. Періодичність: 6 разів на рік. ISSN 2074-272X (Print), ISSN 2309-3404 (Online).

Новини

Видання включене до Переліку наукових фахових видань України з технічних наук до найвищої категорії «А» згідно Наказу МОН України №1412 від 18.12.2018 р.

Переглянути

Результати пошуку

Зараз показуємо 1 - 2 з 2
  • Ескіз
    Документ
    Optimization of fractional PI controller parameters for enhanced induction motor speed control via indirect field-oriented control
    (Національний технічний університет "Харківський політехнічний інститут", 2025) Alnaib, I. I.; Alsammak, A. N.
    Induction Motors (IM) possess advantages such as stability, reliability, and ease of control, making them suitable for many purposes; the literature elucidates control methodologies for IM drives, primarily focusing on scalar and vector control techniques; the conventional method utilized in manufacturing is scalar control, which unfortunately demonstrates optimal performance solely in steady-state conditions. The absence of significant instantaneous torque control restricts flux and dissociated torque, resulting in subpar dynamic responsiveness. Indirect Field Oriented Control (IFOC) for IM drives has proven beneficial for various industrial applications, particularly electric vehicle propulsion. The primary advantages of this approach include the decoupling of torque and flux characteristics and its straightforward implementation. The novelty of the work consists of a proposal for a driving cycle model for testing the control system of electric vehicles in Mosul City (Iraq), and using a Complex Fractional Order Proportional Integral (CFOPI) controller to control IMs via IFOC strategies, the Artificial Bee Colony (ABC) algorithm was applied, which is considered to be highly efficient in finding the values of controllers. Purpose. Improvement IFOC techniques for the regulation of IM speed. Methods. Using the ABC algorithm in tuning the two unique CFOPI controller, and a Real Fractional Order Proportional Integral (RFOPI) controller, to regulate the speed of a three-phase IM via IFOC techniques. Results. The CFOPI controller outperforms the RFOPI controller in obtaining the best performance in controlling the IM. Practical value. The CFOPI controller demonstrates superiority over the RFOPI controller, as evidenced by the lower integral time absolute error in motor speed tracking during the driving cycle 2.1004 for the CFOPI controller compared to 2.1538 for the RFOPI controller.
  • Ескіз
    Документ
    Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions
    (Національний технічний університет "Харківський політехнічний інститут", 2024) Rouaibia, Reda; Djeghader, Yacine; Moussaoui, Lotfi
    Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine.