Кафедра "Електричні апарати"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/43

Офіційний сайт кафедри http://web.kpi.kharkov.ua/ea

Кафедра "Електричні апарати" була створена в 1931 році при Харківському електротехнічному інституті. Засновником, організатором і першим завідувачем кафедри був видатний фахівець в галузі електротехніки професор Вашура Борис Федорович.

Кафедра входить до складу Навчально-наукового інституту енергетики, електроніки та електромеханіки Національного технічного університету "Харківський політехнічний інститут", веде підготовку фахівців що мають глибокі знання з електромеханіки та різнобічні знання в області комп’ютерної техніки й інформаційних технологій.

У складі науково-педагогічного колективу кафедри працюють: 2 доктора технічних наук, 6 кандидатів технічних наук, 1 кандидат фізико-математичних наук; 5 співробітників мають звання доцента, 1 – старшого наукового співробітника.

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Автор: search.filters.author.Mellah, HaceneМістить файли: Так

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  • Ескіз
    Документ
    Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
    (Національний технічний університет "Харківський політехнічний інститут", 2021) Sahraoui, Hamza; Mellah, Hacene; Drid, Said; Chrifi-Alaoui, Larbi
    This article deals with the optimization of the energy conversion ofa grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a standalone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.
  • Ескіз
    Документ
    Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
    (Національний технічний університет "Харківський політехнічний інститут", 2021) Bouchaoui, Lahcene; Hemsas, Kamel Eddine; Mellah, Hacene; Benlahneche, Saadeddine
    Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods usedwithin the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions ofthe Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state ofhealth of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at Sétif province inEast North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value. The structure and specific features of power transformer winding insulation ageing and defect state diagnosis by the application of the artificial neural network (ANN) has been briefly given. MATLAB programs were then developed to automate the evaluation of each method. This paper presents another tool to review the results obtained by the delta X software widely used by the electricity company in Algeria.