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Документ Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks(Національний технічний університет "Харківський політехнічний інститут", 2023) Mimouni, Amina; Laribi, Saadi Souad; Sebaa, Morsli; Allaoui, Tayeb; Bengharbi, Abdelkader AzzeddineIntroduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches.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.