Publications Scientifiques

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Author: search.filters.author.Chetate, BoukhmisEnd date: 2009

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Now showing 1 - 10 of 18
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    Adaptive control of induction motor using artificial neural network with estimation of rotor flux
    (2007) Chetate, Boukhmis; Kabache, Nadir; Ladiguin, Anatoly Nikolaevitch
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    Minimum action time of a robust fuzzy speed controller for induction machine drive
    (Advances in Modelling and Analysis C, 2007) Chetate, Boukhmis; Bradai, Rafik
    In this paper, we propose a procedure to design an optimal fuzzy controller for indirect field oriented controlled induction machine drives. This controller has best possible performances with a minimum action time possible in a practical implementation. First, we design a fuzzy PI controller having the maximum of fuzzy sets (7 input/output membership functions), which show better static and dynamic performances. This controller is specific to speed close loop of an indirect field oriented induction machine drive. Then, in order to minimize its composition the ANFIS (Adaptive Network-Based Fuzzy Inference System) structure is applied to perform a structural and parametric optimization of this controller. We propose also, a procedure to reproduce the input/output mapping of this controller with an approximation using artificial neural networks (ANN)
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    Extended-Kalman-filter based sensorless speed vector control of induction motor taking iron loss into account
    (Advances in Modelling and Analysis C, 2007) Chetate, Boukhmis; Kheldoum, A.
    In vector controlled induction motor drives, the instantaneous rotor speed is measured using whether sensors or estimators. Since the basic Kaiman 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 an induction motor may be neglected. The paper shows the effect of iron loss on the extended Kaiman filter performance that is designed on the basis if the ironless induction machine 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 observer algorithm
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    Fuzzy logic-based controller for position regulation of electric drives
    (Advances in Modelling and Analysis C, 2007) Chermalikh, A.V.; Chetate, Boukhmis; Maidanski, I.I.; Kheldoum, A.
    Electrical drives are characterized by their natural non- linearity owing to their proper design and their time-varying mathematical models. When used to drive industrial systems, e.g. variable speed or variable position drives, conventional control methods are usually applied to design speed and position controllers. However, at certain performance level, these methods are not satisfied. The present paper combines fuzzy logic, mostly used to control system characterized by non-linearity and uncertainty, with new control structures to overcome difficulties listed earlier. The obtained results have proved the good foundation of the suggested method
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    Compensation for the iron loss effect in EKF-based speed estimation of vector controlled induction motors
    (IEEE, 2008) Kheldoun, Aissa; Chetate, Boukhmis
    In vector 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 an 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 if the ironless induction machine model. 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 observer algorithm
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    ANN based double stator asynchronous machine diagnosis taking torque change into account
    (IEEE, 2008) Khodja, Djalal Eddine; Chetate, Boukhmis
    In 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 diagnosis
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    Paramètres capacitifs et surtensions dans l’enroulement d’un moteur asynchrone, alimenté par un convertisseur MLI
    (2005) Belassel, M.T.; Bespalov, Victor; Chetate, Boukhmis
    Dans ce travail nous présentons une méthode de calcul des paramètres capacitifs et des surtensions dans un enroulement d’un moteur asynchrone à cage alimenté par un convertisseur M.L.I La détermination des paramètres capacitifs est l’une des étapes les plus importantes dans le processus de modélisation mathématique des processus ondulatoires dans les moteurs asynchrones (MA), de ces paramètres dépend ce qu’on appelle la distribution initiale des tensions dans les capacités de l’enroulement. La machine étudiée dans ce travail possède un enroulement concentrique à deux couches avec une distribution anarchique des conducteurs circulaires ronds dans l’encoche, elle est fabriquée par l’entreprise ELECTRO-INDUSTRIES d' AZAZGA (ALGERIE) avec un numéro d’identification 2234- □ 041
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    A new diagnostic method of faulty transistor in a three-phase inverter
    (2006) Benslimane, Tarak; Chetate, Boukhmis
    This 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 presented