Smail Bachir

Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines

In this paper, the authors give a new model of squirrel-cage induction motors under stator and rotor faults. First, they study an original model that takes into account the effects of interturn faults resulting in the shorting of one or more circuits of stator-phase winding. They introduce, thus, additional parameters to explain the fault in the three stator phases. Then, they propose a new faulty model dedicated to broken rotor bars detection. The corresponding diagnosis procedure based on parameter estimation of the stator and rotor faulty model is proposed. The estimation technique is performed by taking into account prior information available on the safe system operating in nominal conditions. A special three-phase induction machine has been designed and constructed in order to simulate true faulty experiments. Experimental test results show good agreement and demonstrate the possibility of detection and localization of previous failures.

Modeling and Characterization of Oscillator Circuits by Van der Pol Model using Parameter Estimation

In this paper, we present a new method for the modeling and characterization of oscillator circuit with a Van Der Pol (VDP) model using parameter identification. We also discussed and investigated the problem of estimation in nonlinear system based on time domain data. The approach is based on an appropriate state space representation of Van der Pol oscillator that allows an optimal parameter estimation. Using sampled output voltage signal, model parameters are obtained by an iterative identification algorithm based on Output Error method. Normalization issues are fixed by an appropriate transformation allowing a quickly global minimum search. Finally, the proposed estimation method is tested and validated using simulation data from a 1 GHz oscillator circuit in GaAs technology.

NEW IDENTIFICATION PROCEDURE FOR CONTINUOUS-TIME RADIO FREQUENCY POWER AMPLIFIER MODEL

In this paper, we present a new method for characterization of radio frequency Power Amplifier (PA) in the presence of nonlinear distortions which affect the modulated signal in Radiocommunication transmission system. The proposed procedure uses a gray box model where PA dynamics are modeled with a MIMO continuous filter and the nonlinear characteristics are described as general polynomial functions, approximated by means of Taylor series. Using the baseband input and output data, model parameters are obtained by an iterative identification algorithm based on Output Error method. Initialization and excitation problems are resolved by an association of a new technique using initial values extraction with a multi-level binary sequence input exciting all PA dynamics. Finally, the proposed estimation method is tested and validated on experimental data.

CONTINUOUS-TIME MODEL IDENTIFICATION USING REINITIALIZED PARTIAL MOMENTS - APPLICATION TO POWER AMPLIFIER MODELING

Abstract An application of continuous-time system identification to the modeling of a power amplifier used in mobile communication is proposed. Generally, electronic components modeling uses discrete-time black-box models. In order to keep physical knowledge and easy interpretation of estimated model, a continuous-time transfer function model is chosen to represent amplifier transmittance. Hence, parameter estimation is performed by the reinitialized partial moments method. This estimation method has been chosen because of its insensitivity to initial conditions and rough system a priori knowledge. Then, power amplifier continuous-time model allows immediate physical interpretation to the electronics engineer.

A comparison of several gradient based optimization algorithms for PAPR reduction in OFDM systems

The aim of this paper is to evaluate and compare different optimization algorithms for Peak to Average Power Ratio (PAPR) reduction in Orthogonal Frequency Division Multiplexing (OFDM) systems. Based on Tone Reservation (TR) method, we exploit the unused subcarriers of the studied standard to generate the peak canceling signal without data rate loss. Gradient, Conjugate-Gradient with two directions search and QuasiNewton methods have been investigated and evaluated on the basis of spectral regrowth, convergence speed and ability to improve the high peak-to-average reduction in multicarriers systems. As an example, the simulations are performed in the case of Local Area Network WLAN (IEEE 802.11a standard). Simulation results show that a PAPR reduction gain around 3 dB can be achieved.

Stator faults diagnosis in induction machines under fixed speed

In this paper, authors proposes a new monitoring technique of inter turns short circuit windings under fixed speed. This method is based on introduction of sinusoidal extra-signals to input voltage delivered by power inverter. For diagnosis, we proposed model of squirrel cage induction motors under stator faults. This model takes into account the effects of inter turn faults resulting in the shorting of one or more circuits of stator phase winding. The stator asymmetries caused in the faulty case are explained by the introduction of the short-circuit elements in the conventional Parks model. Each short-circuit element allows faults detection and localization in the three stator windings. The estimation technique is performed by taking into account prior information available on the safe system operating in nominal conditions. Experimental test results show the good agreement between the real faults and their estimations and demonstrate the detection and localization possibilities of this predictive failure detection technique

Linearization of RF Power Amplifiers Using Adaptive Kalman Filtering Algorithm

In this paper, a new linearization algorithm of power amplifier (PA), based on Kalman filtering theory is proposed for obtaining fast convergence of the adaptive digital predistortion. The proposed method uses the real-time digital processing of baseband signals to compensate the nonlinearities and memory effects in radio-frequency power amplifier. To reduce the complexity of computing in classical Kalman filtering, a sliding time-window has been inserted which combines offline measurement and online parameter estimation with high sampling time to track the changes in the PA characteristics. The performance of the proposed linearization scheme is evaluated through simulation and experiments. Using digital signal processing, experimental results with commercial power amplifier are presented for multicarrier signals to demonstrate the effectiveness of this new approach.

Kalman filtering algorithm for on-line memory polynomial predistortion

A new adaptive technique for digital predistortion is presented. The proposed method uses the real-time digital processing of baseband signals to compensate the nonlinearities and memory effects in radio-frequency power amplifier. Kalman filtering algorithm with sliding time-window is adapted to track the changes in the PA characteristics. Simulation and measurement results, using digital signal processing, are presented for multicarrier signals to demonstrate the effectiveness of this new approach.

Behavioral Modeling and Digital Predistortion of RF Power Amplifiers

Modeling accurately the nonlinear behavior of radio frequency power amplifiers is a key point in the design and development of linearizer. In this paper we presented a predistorsion technique based on DSP circuit and the identification of continuous time model describing the power amplifier associated to quadrature modulator and demodulator circuits. The model is composed by two blocks, the first uses polynomial functions to model the nonlinear amplitude and phase distortion, the second one is based on a MIMO continuous filter to describe the PA dynamics. Parameters are determined using identification method based on output error technique and a pseudo random binary sequence as signal excitation. Using the previous solution, the non-linear inverse function is calculated and implemented in the DSP. Experimental results of digital predistorsion show an improvement of 10 dB of the ACPR for QPSK and pi/4 DQPSK modulated signals

Modeling and identification of continuous-time system for RF amplfiers

In this paper, we present a new identification procedure for radio frequency power amplifier (PA) in the presence of nonlinear distortion and memory effects. The proposed procedure uses a continuous-time model where PA dynamics are modeled with a multivariable filter and a general polynomial function. Using the baseband input and output data, model parameters are obtained by an iterative identification algorithm. Finally, the proposed estimation method is tested and validated on experimental data by comparison of the quadrature IQ signals in time domain.