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Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
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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 Increase the aviation efficiency of UAVs using artificial neural networks(NTU "KhPI", 2017) Kurdi, Saadi T.; Reja, Ahmed Hameed; Al-Ashmati, Akram Fathi HusseinPurpose. It is known that the flight of the UAV is conducted by sensors that transmit the performance of the UAV and on the basis of this information is controlled on the UAV and give themthe orders which are necessary to perform the task of flying UAV and normal these faults occur during the flight of unmanned air vehicle (UAV), according to the concepts of aviation is a very critical situation that affects the completion of the mission.These faults are mainly due to failure in the sensors,which canbe divided into. Flight Situation is about the flying situation of the aircraft, such as heading, altitude, airspeed, and vertical speed and angle of attack sensors. And Flight Control Situation, this is about the flight control surfaces such as (rudder, aileron, and elevator deflection), pitch attitude, and roll attitude sensors.This paper presents an effective technique to ensure that the sensors can operate with high efficiency. Methods. Two different approaches are used in this work.The first approach is Neural Network (NN) based tool for the modeling, simulation and analysis of aircraft (SFDIA), sensorsfailure, detection, and identification and accommodation problem.The second approachis Neural Network trained with the (EMRAN) algorithm swhich is a set of conditions that decide how the (EMRAN) structure should be adapted to better suit the training data. Results. The results from the modeling process and analysis of aircraft sensors showed that the neural network based tool (SFDIA) and the (EMRAN) algorithms are able to show high-resolution results in the behavior of sensors and hence in the (UAV) behavior. Conclusions. The capabilities of (SFDIA) are a consequence of the extensive modularity of the whole simulation tool. It allows an easy change of unmanned air vehicle (UAV), dynamics and feedback control law as well as Neural Network (NN) estimators and (SFDIA) scheme.