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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2022 - 20242

Налаштування

Містить файли: ТакКлючові слова: search.filters.subject.diagnosis

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Зараз показуємо 1 - 2 з 2
  • 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.
  • Ескіз
    Документ
    Improvement of power transformer differential protection through detection and exploitation of the negative sequence currents
    (Національний технічний університет "Харківський політехнічний інститут", 2022) Zitouni, Mokhtar
    Power transformers are the most important and the most expensive equipment used in transport and distribution of electrical energy. Their failure results in huge economic losses. Despite the great advances in the design of power equipment in recent years, the feeble link in the chain remains the insulation weakness of coil turns of the power transformer. The novelty of the proposed research consists in the development of a new procedure for diagnosing and localizing the occurrence of turn to turn short-circuits in the windings of three-phase power transformer. The main problems of the current differential relay are short circuits of one or more turns of a transformer winding. Hence a new approach using' the amplitude comparison between the negative sequence currents' is developed and a digital discriminator internal / external fault is applied to discriminate turn to turn faults among the other ones. The proposed procedure is based on the exploitation of the negative sequence currents. The purpose of using this new procedure is to identify small faults inside power transformer coils and to distinguish inner faults from the outer faults by using an ameliorate circuit. The method used in this paper is a novel algorithm which based on the comparison between the negative sequence current amplitudes and to calculate the corresponding phase angle shifts. The performance of the proposed procedure has been confirmed by MATLAB/Simulink environment. The results of simulation reveal the efficiency of the suggested procedure, and indicate that this procedure can provide fast and sensitive approach for detecting low level turn-to-turn faults.