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Your search keyword '"BENTOUNSI, Amar"' showing total 24 results
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24 results on '"BENTOUNSI, Amar"'

10. A new analytical model of variable reluctance machine coupled to GA for an optimal design

Author

Bentounsi Amar, Benalla Hocine, and Mouellef Sihem

Subjects
Optimal design, Magnetic circuit, Direct torque control, Control theory, Computer science, Rotor (electric), law, Magnetic reluctance, Torque, Flux linkage, Finite element method, law.invention
Abstract

This paper presents a comprehensive program based on a novel analytical model for a variable reluctance machine (VRM) developed in Matlab-language to compute the value of the flux linkage, coenergy and instantaneous electromagnetic torque as functions of rotor position and winding currents, taking into account the non-linearity of the magnetic circuit. The models are compared with the ones obtained, for the same motor, via a 2D finite elements analysis (FEA) using Flux-2D software. The genetic algorithms (GA) and analytical model are able to determine optimal geometriy of a doubly salient 6/4 VRM with the objective function maximizing the torque with a rate of undulation maintained constant. The obtained results are discussed and shows the effectiveness of the proposed approach.

Published
2013

11. Optimal Design of Switched Reluctance Motor Using PSO Based FEM-EMC Modeling

Author

Bentounsi Amar, Benalla Hocine, and Mouellef Sihem

Subjects
Optimal design, Hardware_MEMORYSTRUCTURES, General Computer Science, Rotor (electric), Stator, Computer science, Particle swarm optimization, Switched reluctance motor, law.invention, Magnetic circuit, Direct torque control, law, Control theory, Torque, Torque ripple, Electrical and Electronic Engineering
Abstract

This paper aims to optimize the design of a prototype of a 6/4 Switched Reluctance Motor (SRM) using the Particle Swarm Optimization (PSO) algorithm. The geometrical parameters to optimize are the widths of the stator and rotor teeth due to their significant effects on the prototype design and the performances in terms of increased average torque and reduced torque ripple. The studied 3kW SRM is modeled using a numerical-analytical approach based on a coupled Finite Element Method with Equivalent Magnetic Circuit (FEM-EMC). The simulations are performed under MATLAB environment with user-friendly software. The optimal results found are discussed, compared against those obtained by the Genetic Algorithms (GA) and showed a significant improvement in average torque.

Published
2015

12. Modeling and detection of eccentricity fault in Switched Reluctance Motor

Author

Lebaroud Abdesselam, Rebbahi Fares, Batoun Bachir, Bouchahdane Mouhamed, Bouchareb Ilhem, and Bentounsi Amar

Subjects
Engineering, business.industry, media_common.quotation_subject, Short-time Fourier transform, Fault (power engineering), Fault detection and isolation, Switched reluctance motor, Time–frequency analysis, Time–frequency representation, Control theory, Eccentricity (behavior), business, Induction motor, media_common
Abstract

This paper presents the effects of dynamic air-gap eccentricity (DE) on the performances of a 6/4 Switched Reluctance Machine (SRM) through finite Element Analysis by means of Finite Element Method Magnetics (FEMM). The Time-Frequency Representation (TFR) is carried out to study the effect of DE on the output torque signal. Time-frequency representation is an appropriate tool for detecting the mechanical failures from the torque analysis. Relatively few works study the influence of eccentricity on the dynamic response of SRM. Various time-frequency methods as Wigner-Ville distribution (WVD) and Short Time Fourier Transform (STFT) are used and illustrated to fault detection. TFR method allows an accurate detection independent from the type of fault. Finally, simulation results of healthy and faulty cases are presented.

Published
2011

24. Fault detection and diagnosis in a set "inverter-switched reluctance motor" based on pattern recognition using Kalman filter prediction.

Author

Bouchareb, Ilhem, Bentounsi, Amar, and Lebaroud, Abdesselam

Subjects
*KALMAN filtering, *RELUCTANCE motors, *PATTERN recognition systems, *OPEN-circuit voltage, *ELECTRICAL engineering
Abstract

In the case of one phase failure, the switched reluctance motor (SRM) will behave nearly the same, both in open circuit and in short circuit failure. This means, that the machine will understand the two faults in the same way which makes the SRM faults detection and diagnosis a more challenging task. This paper presents a diagnosis method based on pattern recognition analysis to detect and to classify automatically the electrical faults, short- and open-circuit under any level of load of the studied system: redundant three-phase power converter fed 6/4 SRM. The phases making a pattern recognition diagnosis of SRM, the training and the decision. The training phase consists in determining the pattern vector and the optimal kernels design (the separating classes) by Time-Frequency Representation (TFR). The training data is carried out using a set of fault scenarios, between healthy, single and combined faults, in terms of torque measurement at different load level, in order to deduce the fault severity. The second phase, consists in associating an unknown pattern with one of the defined classes, according to the "knearest neighbors" (knn) decision rule, associated with Kalman estimator to tracking of various operating modes and to predict the evolution of the call out of the knowledge database for a given operating mode in order to realize a preventive maintenance. The experimental results prove the efficiency of pattern recognition methods in condition monitoring of reluctance machine. [ABSTRACT FROM AUTHOR]

Published
2014
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