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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2019 - 201912020 - 20221

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Ключові слова: search.filters.subject.adaptive neuro-fuzzy inference system

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  • Ескіз
    Документ
    Improvement of voltage stability for grid connected solar photovoltaic systems using static synchronous compensator with recurrent neural network
    (Національний технічний університет "Харківський політехнічний інститут", 2022) Praveen Kumar, Thota ; Ganapathy, Somaskandan; Manikandan, Mani
    This article proposes a new control strategy for static synchronous compensator in utility grid system. The proposed photovoltaic fed static synchronous compensator is utilized along with recurrent neural network based reference voltage generation is presented in grid system network. The novelty of the proposed work consists in presenting a Landsman converter enhanced photovoltaic fed static synchronous compensator with recurrent neural network algorithm, to generate voltage and maintain the voltage-gain ratio. Methods. The proposed algorithm which provides sophisticated and cost-effective solution for utilization of adaptive neuro-fuzzy inference system as maximum power point tracking assures controlled output and supports the extraction of complete power from the photovoltaic panel. Grid is interconnected with solar power, voltage phase angle mismatch, harmonic and voltage instability may occur in the distribution grid. The proposed control technique strategy is validated using MATLAB/Simulink software and hardware model to analysis the working performances. Results. The results obtained show that the power quality issue, the proposed system to overcome through elimination of harmonics, reference current generation is necessary, which is accomplished by recurrent neural network. By recurrent neural network, the reference signal is generated more accurately and accordingly the pulses are generated for controlling the inverter. Originality. Compensation of power quality issues, grid stability and harmonic reduction in distribution network by using photovoltaic fed static synchronous compensator is utilized along with recurrent neural network controller. Practical value. The work concerns the comparative study and the application of static synchronous compensator with recurrent neural network controller to achieve a good performance control system of the distribution network system. This article presents a comparative study between the conventional static synchronous compensator, static synchronous compensator with recurrent neural network and hardware implementation with different load. The strategy based on the use of a static synchronous compensator with recurrent neural network algorithm for the control of the continuous voltage stability and harmonic for the distribution network-linear as well as non-linear loads in efficient manner. The study is validated by the simulation results based on MATLAB/Simulink software and hardware model.
  • 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.