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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Фільтри

2019 - 20204

Налаштування

Автор: search.filters.author.Bouktir, Tarek

Результати пошуку

Зараз показуємо 1 - 4 з 4
  • Ескіз
    Документ
    Slime mould algorithm for practical optimal power flow solutions incorporating stochastic wind power and static VAR compensator device
    (Національний технічний університет "Харківський політехнічний інститут", 2020) Kouadri, Ramzi; Slimani, Linda; Bouktir, Tarek
    This paper proposesthe application procedure of a new metaheuristic technique in a practical electrical power system to solve optimal power flow problems, this technique namely the slime mould algorithm (SMA) which is inspired bythe swarming behavior and morphology of slime mould innature. This study aims to test and verify the effectiveness of the proposed algorithm to get good solutions for optimal power flowproblems by incorporating stochastic wind power generation and static VAR compensators devices. In this context, different cases are considered in order to minimize the total generation cost, reductionof active power losses as well as improving voltage profile.
  • Ескіз
    Документ
    Simultaneous allocation of multiple distributed generation and capacitors in radial network using genetic-salp swarm algorithm
    (Національний технічний університет "Харківський політехнічний інститут", 2020) Djabali, Chabane; Bouktir, Tarek
    In recent years, the problem of allocation of distributed generation and capacitors banks has received special attention from many utilities and researchers. The present paper deals with single and simultaneous placement of dispersed generation and capacitors banks in radial distribution network with different load levels: light, medium and peak using genetic-salp swarm algorithm. The developed genetic-salp swarm algorithm (GA-SSA) hybrid optimization takes the system input variables of radial distribution network to find the optimal solutions to maximize the benefits of their installation with minimum cost to minimize the active and reactive power losses and improve the voltage profile. The validation of the proposed hybrid genetic-salp swarm algorithm was carried out on IEEE 34-bus test systems and real Algerian distributed network of Djanet (far south of Algeria) with 112-bus. The numerical results endorse the ability of the proposed algorithm to achieve a better results with higher accuracy compared to the result obtained by salp swarm algorithm, genetic algorithm, particle swarm optimization and the hybrid particle swarm optimization algorithms.
  • Ескіз
    Документ
    Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy
    (Национальный технический университет "Харьковский политехнический институт", 2019) Rahmani, Ala eddine; Slimani, Linda; Bouktir, Tarek
    Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind power generator.
  • Ескіз
    Документ
    On-line voltage stability evaluation using neuro-fuzzy inference system and moth-flame optimization algorithm
    (Национальный технический университет "Харьковский политехнический институт", 2019) Bourzami, Arif; Amroune, Mohammed; Bouktir, Tarek
    In recent years, the problem of voltage instability has received specialattention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model takes the voltage magnitudes and their phases obtained from the weak buses in the system as input variables. The weak buses identification is formulated as an optimization problem considering the operating cost, the real power losses and the voltage stability index. The recently developed Moth-Flame Optimization (MFO) algorithm was adapted to solve this optimization problem. The validation of the proposed on-line voltage stability assessment approach was carried out on IEEE30-bus and IEEE 118-bus test systems. The obtained results show that the proposed approach can achieve a higher accuracy compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks.