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.

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20242

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ORCID: search.filters.orcid.https://orcid.org/0000-0002-7097-7808

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  • Ескіз
    Документ
    Contribution of using a photovoltaic unified power quality conditioner in power quality improvement
    (Національний технічний університет "Харківський політехнічний інститут", 2024) Bousnoubra, C.; Djeghader, Y.; Belila, H.
    With the increasing complexity of power systems and the integration of diverse energy sources, issues such as voltage sags, swells, and signal distortions have emerged as critical challenges. These power quality problems can result in equipment malfunction, production downtime, and financial losses for industries, as well as inconvenience and potential damage to electrical appliances in households. There is an urgent need for enhanced system efficiency. Methods. This objective is effectively achieved through the utilization of the newly proposed power theory, which is rooted in solar photovoltaic (PV) control, in conjunction with the Unified Power Quality Conditioner (UPQC). Purpose. The proposed method incorporates a modified synchronous reference frame scheme, coupled with a phase-locked loop mechanism. This control strategy enables the UPQC to effectively mitigate power quality issues. Novelty. PV-UPQC is utilized to uphold power integrity in the presence of diverse current and voltage distortions. This device, known as a multi-objective power conditioning apparatus, serves the purpose of maintaining power quality. PV-UPQC incorporates both a shunt and series voltage source converter, which are interconnected through a shared DC-link. Additionally, the PV system is interconnected at the DC-link of the UPQC in order to supply power to the load. Results. In this study, a novel approach is presented for controlling the UPQC, aiming to address power quality concerns such as unbalanced grid voltage and harmonic distortions and enabling us to control active and reactive power.
  • 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.