Publications Scientifiques
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Item Artificial Neuron Network Based Faults Detection and Localization in the High Voltage Transmission Lines with Mho Distance Relay(IETA, 2020) Boumedine, Mohamed Said; Khodja, Djalal Eddine; Chakroune, SalimThis study offers the opportunity to extend the functioning of the most advanced protection systems. The faults which can arise on the power transmission lines are numerous and varied: Short-circuit; Overvoltage; Overloads, etc. In the context of short circuits, the conventional sensor as the Mho distance relay also known as the admittance relay is generally used. This relay will be discussed later in this study. By taking into account the preventive risks of the Mho relay and discover the new techniques of artificial intelligence, namely the neural network which can contribute to the precise and rapid detection of all types of short-circuit faults. The results of the simulation tests demonstrate the effectiveness of the methods proposed for the automatic diagnosis of faults.Item Fuzzy logic based broken bar fault diagnosis and behavior study of induction machine(International Information and Engineering Technology Association, 2020) Chouidira, I.; Khodja, Djalal Eddine; Chakroune, S.This study aims to display fuzzy logic (FL) technique for diagnosis of fault induction machine. This allows monitoring of fuzzy information from different signals to give more accurate judgment on the health of the engine, through using a multi-winding model of induction machine for the simulation of broken bars. This model allows study the influence of defects and appear the behavior of the machine in the different modes of running conditions (healthy and fault). In this work, we focus the application of a fuzzy logic technique based on the fast Fourier transformation (FFT) by analyzing the stator current for fault detection. The results of the simulation obtained allowed us to show the importance of the fuzzy logic approach based on classification of signals for detecting the faulty. © 2020 Lavoisier. All rights reservedItem Induction Machine Faults Detection and Localization by Neural Networks Methods(IIETA, 2019) Chouidira, Ibrahim; Khodja, Djalal Eddine; Chakroune, SalimThe objective of this study is to present artificial intelligence (AI) technique for detection and localization of fault in induction machine fault, through a multi-winding model for the simulation of four adjacent broken bars and three-phase model for the simulation of short-circuit between turns. In this work, it was found that the application of artificial neural networks (ANN) based on Root mean square values (RMS) plays a big role for fault detection and localization. The simulation and obtained results indicate that ANN is able to detect the faulty with high accuracyItem FPGA-based implementation of ANN for direct torque control of induction machine using Co-simulation(Educational Research Multimedia & Publications, 2014) Khodja, Djalal Eddine; Simard, Stéphane; Beguenane, Rachid; Kheldoun, AissaItem The use of root mean square values (RMS) for the shortcoming diagnostic of permanent magnet synchronous machine using artificial neurone network module(2008) Khodja, Djalal Eddine; Chetate, BoukhmisItem ANN based double stator asynchronous machine diagnosis taking torque change into account(IEEE, 2008) Khodja, Djalal Eddine; Chetate, BoukhmisIn this work the strategy of the artificial intelligence (neural networks) is used to detect and localize the defects of the double stator asynchronous machine. In fact, several neural networks have been applied to the detection of defects. Then, we used a selector which allows activating only one network at a time. In this case, the selected network detects only defects corresponding to the torque developed by asynchronous machine. Finally, the simulation results were presented to show the effectiveness of artificial neural networks for automatic fault diagnosisItem Torque based selection of ANN for fault diagnosis of wound rotor asynchronous motor-converter association(2010) Khodja, Djalal Eddine; Chetate, BoukhmisItem Sigmoid function approximation for ANN implementation in FPGA devices(2010) Khodja, Djalal Eddine; Kheldoun, Aissa; Refoufi, L.The objective of this work is the implementation of Artificial Neural Network on a FPGA board. This implementation aim is to contribute in the hardware integration solutions in the areas such as monitoring, diagnosis, maintenance and control of power system as well as industrial processes. Since the Simulink library provided by Xilinx, has all the blocks that are necessary for the design of Artificial Neural Networks except a few functions such as sigmoid function. In this work, an approximation of the sigmoid function in polynomial form has been proposed. Then, the sigmoid function approximation has been implemented on FPGA using the Xilinx library. Tests results are satisfactoryItem Sensorless speed field-oriented control of induction motor tacking core loss into account(2011) Kheldoun, Aissa; Khodja, Djalal Eddine; Refoufi, L.In field-oriented controlled induction motor drives, the instantaneous rotor speed is measured using whether sensors or estimators. Since the basic Kalman filter is a state observer, its use in vector controlled schemes has received much attention. However, these schemes are based on the assumption that the existence of iron loss in the induction motor may be neglected. The paper shows the effect of iron loss on the extended Kalman filter performance that is designed on the basis of the classical dq model. Original simulation results are carried out to demonstrate this effect as well as the effectiveness of the suggested approach to minimise the speed estimation error without modifying the EKF's algorithm.Item Application of new optimisation algorithm to self-excited induction generator analysis(IEEE, 2013) Kheldoun, Aissa; Refoufi, Larbi; Khodja, Djalal Eddine