Кафедри
Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
Переглянути
17 результатів
Фільтри
Налаштування
Результати пошуку
Публікація Використання штучної нейронної мережі для обробки результатів експеримету(Національний технічний університет "Харківський політехнічний інститут", 2018) Погребняк, С. В.; Водка, Олексій ОлександровичДокумент Експертні системи(2023) Нікітіна, Людмила ОлексіївнаУ посібнику розглядаються основні поняття та перспективи розвитку прикладних систем штучного інтелекту в контексті їх використання в технологіях експертних систем. Проаналізовано методи подання й обробки знань, особливості побудови експертних систем, загальні відомості про методи набуття знань та інструментарії для розробки експертних систем. Здійснено теоретичний аналіз основних принципів та методів подання знань та їх моделей в експертних системах. Матеріал навчального посібника рекомендовано для студентів спеціальності 122 - Комп’ютерні науки.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, AminaIntroduction. 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.Документ Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique(Національний технічний університет "Харківський політехнічний інститут", 2022) Abid, Mimouna; Laribi, Souad; Larbi, M'hamed; Allaoui, TayebTo attain high efficiency and reliability in the field of clean energy conversion, power electronics play a significant role in a wide range of applications. More effort is being made to increase the dependability of power electronics systems. Purpose. In order to avoid any undesirable effects or disturbances that negatively affect the continuity of service in the field of energy production, this research provides a fault detection technique for insulated-gate bipolar transistor open-circuit faults in a three-level diode-clamped inverter of a wind energy conversion system predicated on a doubly-fed induction generator. The novelty of the suggested work ensures the regulation of power exchanged between the system and the grid without faults, advanced intelligence approaches based on a multilayer artificial neural network are used to discover and locate this type of defect; the database is based on the module and phase angle of three-phase stator currents of induction generators. The proposed methods are designed for the detection of one or two open-circuit faults in the power switches of the side converter of a doubly-fed induction generator in a wind energy conversion system. Methods. In the proposed detection method, only the three-phase stator current module and phase angle are used to identify the faulty switch. The primary goal of this fault diagnosis system is to effectively detect and locate failures in one or even more neutral point clamped inverter switches. Practical value. The performance of the controllers is evaluated under different operating conditions of the power system, and the reliability, feasibility, and effectiveness of the proposed fault detection have been verified under various open-switch fault conditions. The diagnostic approach is also robust to transient conditions posed by changes in load and speed. The proposed diagnostic technique's performance and effectiveness are both proven by simulation in the SimPower /Simulink® MATLAB environment.Документ New application of artificial neural network-based direct power control for permanent magnet synchronous generator(Національний технічний університет "Харківський політехнічний інститут", 2021) Akkouchi, Kamel; Rahmani, Lazhar; Lebied, RymaThis article proposes a new strategy for Direct Power Control (DPC) based on the use of Artificial Neural Networks (ANN-DPC). The proposed ANN-DPC scheme is based on the replacement of PI and hysteresis regulators by neural regulators. Simulation results for a 1 kW system are provided to demonstrate the efficiency and robustness of the proposed control strategy during variations in active and reactive power and in DC bus voltage. Methodology. Our strategy is based on direct control of instant active and reactive powers. The voltage regulator and hysteresis are replaced by more efficient and robust artificial neuron networks. The proposed control technique strategy is validated using MATLAB / Simulink software to analysis the working performances. Results. The results obtained clearly show that neuronal regulators have good dynamic performances compared to conventional regulators (minimum response time, without overshoots). Originality. Regulation of continuous bus voltage and sinusoidal currents on the network side by using artificial neuron networks. Practical value. The work concerns the comparative study and the application of DPC based on ANN techniques to achieve a good performance control system of the permanent magnet synchronous generator. This article presents a comparative study between the conventional DPC control and the ANNDPC control. The first strategy based on the use of a PI controller for the control of the continuous bus voltage and hysteresis regulators for the instantaneous powers control. In the second technique, the PI and hysteresis regulators are replaced by more efficient neuronal controllers more robust for the system parameters variation. The study is validated by the simulation results based on MATLAB / Simulink software.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, LarbiThis 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.Документ Дослідження сучасних систем комп'ютерного зору(Черкаський державний технологічний університет, 2019) Романча, А. П.; Борисов, Д. В.; Подорожняк, Андрій ОлексійовичДокумент Математична модель оцінки платоспроможності позичальника на базі нейронної мережі(Черкаський державний технологічний університет, 2019) Земляна, Г. В.; Подорожняк, Андрій ОлексійовичДокумент Дослідження системи екологічного моніторингу(Національний технічний університет "Харківський політехнічний інститут", 2019) Романча, А. П.; Подорожняк, Андрій ОлексійовичДокумент Мультиагентна система керування колективом транспортних роботів(Національний технічний університет "Харківський політехнічний інститут", 2014) Дмитрієнко, Валерій Дмитрович; Хавіна, Інна Петрівна