Browsing by Author "Kadri, Farid"

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    Fuzzy Direct Torque Control of Electric Vehicle with Dual Induction Motors Fed by Five Leg Inverter
    (University of Eloued جامعة الوادي, 2022-01-24) Kadri, Farid; Hamida, Mohamed Assaad
    Thre are several possible EV configurations regarding the electric propulsion and the energy sources. In conventional dual-motor configuration, tow three phase VLSI inverters are associated to dual induction motors drives. A novel structure is to associate only Five Legs Inverter (FLI) to drive the dual induction motors of the electric vehicle. In order to improve performance setting, a Fuzzy Direct Torque Control technique has been applied by using five legs inverter instead of six legs inverter to control the electric vehicle. The proposed Fuzzy Direct Torque Control can ensure the decoupling control and distribute the required torques to 1.5-kW dual induction motors drives. Simulations in SIMULINK/MATLAB environment are carried out to show that the developed fuzzy control is effective and provides a simple configuration with good performance in terms of speed and torque responses.
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    Multiple Fuzzy Diagnosis for Voltage Source Inverter Open Circuit Fault in Torque Direct Control Induction Motor Drive
    (University of Eloued جامعة الوادي, 2022-01-24) Tamissa, Younes; Kadri, Farid; Charif, Fella; Benchabane, Abderrazak
    This article aims to examine the possibility of fault detection and diagnosis in an induction motor powering a three-phase inverter using artificial intelligence (AI) techniques. Due to the weakness of the switches contained in a three phase inverter. Early detection of these fault switches that may occur in the inverter will be very important in order to find a way to allow us to control the operation and the protective action to avoid regular failures. We present the simulation results of a fault diagnosis system using an artificial intelligence technique with direct torque control (FDTC) of fuzzy logic of the induction motor. In this article, we provide a detailed explanation of the multiple open circuit switching faults in the inverter with a simple feature extraction technique to investigate the possibility of detecting and diagnosing these faults. The search and identification of faulty switches is recognized in limited current periods. Classification performance for multiple defects is improved by the use of a fuzzy logic detection approach.