Publications Scientifiques
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Item Adaptive control of induction motor using artificial neural network with estimation of rotor flux(2007) Chetate, Boukhmis; Kabache, Nadir; Ladiguin, Anatoly NikolaevitchItem A new and best approach for early detection of rotor and stator faults in induction motors coupled to variable loads(Higher Education Press, 2015) Allal, Abderrahim; Chetate, BoukhmisItem A new diagnostic method of faulty transistor in a three-phase inverter(2006) Benslimane, Tarak; Chetate, BoukhmisThis paper describes a method of detection and identification of transistor base drive open-circuit fault of 3-phase voltage source inverter (VSI), feeding an open loop controlled induction motor. The detection mechanism is based on a novel technique of wavelet transform. In this method, the stator currents will be used as an input to the system. No direct access to the induction motor is required. The simulation results are presentedItem A method of minimizing the power losses in an induction motor with a squirrel-cage with vector control(2004) Chetate, Boukhmis; Kheldoun, AissaAn approach to optimizing the flux linkage of the rotor of an induction motor is considered when the motor operates in a vector control mode with indirect orientation in the direction of the field. In this system, the expression for the frequency of the rotor e.m.f. contains the rotor winding impedance; this impedance must therefore be precisely estimated in real time. It is proposed that this should be done using a fuzzy-logic adaptation mechanism. The results of using such a mechanism in a physical model confirm its effectiveness. Key words: induction motor, rotor, vector control, fuzzy logicItem Adaptive control of induction motor with unknown motor resistance(2007) Chetate, Boukhmis; Bradai, Rafik; Kabache, NadirIn this paper, a new approach for induction motor rotor resistance variation estimate is presented by using adaptive neural networks. Indeed, the proposed neural networks are endowed with adaptive rules that allow them to estimate the true values of the necessary nonlinear state feedbacks for the input-output feedback linearization control of an induction motor. A comparison between the nonlinear state feedbacks provided by neural networks and those calculated through the nominal model of the induction motor with nominal parameters allows us to estimate the variation in the rotor resistance