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 - 20233

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Зараз показуємо 1 - 3 з 3
  • Ескіз
    Документ
    Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines
    (Національний технічний університет "Харківський політехнічний інститут", 2023) Sakhara, S.; Brahimi, M.; Nacib, L.; Layadi, T. M.
    Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.
  • Ескіз
    Документ
    Wavelet packet analysis for rotor bar breakage in an inverter induction motor
    (Національний технічний університет "Харківський політехнічний інститут", 2023) Abu Ibaid, O. Z. I.; Belhamdi, S.; Abid, M.; Chakroune, S.; Mouassa, S.; Al-Sagar, Z. S.
    In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the lowload, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.
  • Ескіз
    Документ
    Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks
    (Національний технічний університет "Харківський політехнічний інститут", 2022) Bengharbi, Abdelkader Azzeddine; Laribi, Saadi Souad; Allaoui, Tayeb; Mimouni, Amina
    Introduction. This research work focuses on the design and experimental validation of fault detection techniques in grid-connected solar photovoltaic system operating under Maximum Power Point Tracking mode and subjected to various operating conditions. Purpose. Six fault scenarios are considered in this study including partial shading, open circuit in the photovoltaic array, complete failure of one of the six IGBTs of the inverter and some parametric faults that may appear in controller of the boost converter. Methods. The fault detection technique developed in this work is based on artificial neural networks and uses discrete wavelet transform to extract the features for the identification of the underlying faults. By applying discrete wavelet transform, the time domain inverter output current is decomposed into different frequency bands, and then the root mean square values at each frequency band are used to train the neural network. Results. The proposed fault diagnosis method has been extensively tested on the above faults scenarios and proved to be very effective and extremely accurate under large variations in the irradiance and temperature.