Godpromesse Kenné

An Online Simplified Rotor Resistance Estimator for Induction Motors

This brief presents an adaptive variable structure identifier that provides finite time convergent estimate of the induction motor rotor resistance under feasible persistent of excitation condition. The proposed rotor resistance scheme is based on the standard dynamic model of induction motor expressed in a fixed reference frame attached to the stator. The available variables are the rotor speed, the stator currents and voltages. Experiments show that the proposed method achieved very good estimation of the rotor resistance which is subjected to online large variation during operation of the induction motor. Also, the proposed online simplified rotor resistance estimator is robust with respect to the variation of the stator resistance, measurement noise, modeling errors, discretization effects and parameter uncertainties. Important advantages of the proposed algorithm include that it is an online method (the value of Rr can be continuously updated) and it is very simple to implement in real-time (this feature distinguishes the proposed identifier from the known ones).

Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer

In this paper, a new method for the identification of nonlinear systems with time-varying parameters using a sliding-neural network observer is investigated. The proof of the finite-time convergence of the estimates to their true values is achieved using Lyapunov arguments and sliding mode theories. An application example illustrated the effectiveness of the approach and the obtained results show high convergence rate and very satisfactory parameter estimation accuracy. The computing results under noisy condition also demonstrate that good state and parameter estimation can be achieved despite the disturbance (noise) in the system. The reduced number of hidden units and the small transient period demonstrate that the proposed method can be easily implementable in real-time.

Real-Time Speed and Flux Adaptive Control of Induction Motors Using Unknown Time-Varying Rotor Resistance and Load Torque

In this paper, an algorithm for direct speed and flux adaptive control of induction motors using unknown time-varying rotor resistance and load torque is described and validated with experimental results. This method is based on the variable structure theories and is potentially useful for adjusting online the induction motor controller unknown parameters (load torque and rotor resistance). The presented nonlinear compensator provides voltage inputs on the basis of rotor speed and stator current measurements, and generates estimates for both the unknown parameters and the nonmeasurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Experiments show that the proposed method achieved very good tracking performance within a wide range of the operation of the induction motor with online variation of the rotor resistance: up to (87%). This high tracking performance of the rotor resistance variation demonstrates that the proposed adaptive control is beneficial for motor efficiency. The proposed algorithm also presented high decoupling performance and very interesting robustness properties with respect to the variation of the stator resistance (up to 100%), measurement noise, modeling errors, discretization effects, and parameter uncertainties (e.g., inaccuracies on motor inductance values). The other interesting feature of the proposed method is that it is simple and easily implementable in real time. Comparative results have shown that the proposed adaptive control decouples speed and flux tracking while standard field-oriented control does not.

An application of Immersion and Invariance to transient stability and voltage regulation of power systems with unknown mechanical power

This paper presents the application of Immersion and Invariance (I&I) strategy to transient stability and voltage regulation of power systems with unknown mechanical power. The proposed controller is made adaptive with respect to the mechanical power by using a finite time estimator. Computing results show the effectiveness of the proposed adaptive nonlinear control strategy.

An Online Simplified Nonlinear Controller for Transient Stabilization Enhancement of DFIG in Multi-Machine Power Systems

An adaptive nonlinear controller for transient stability and voltage regulation of power systems based DFIG in multimachine configuration is presented using a standard third order dynamical model of the DFIG. Finite time estimators for the unmeasurable time derivative of the quadrature component of the DFIG stator current, mechanical input, unknown direct axis transient open circuit time constant (function of the rotor resistance) are presented. The main feature of the proposed control scheme is its robustness with respect to large perturbations and parameter variations. Numerical results are presented to illustrate the performance of the proposed control scheme and its robustness properties.

A simple and robust gray image encryption scheme using chaotic logistic map and artificial neural network

A robust gray image encryption scheme using chaotic logistic map and artificial neural network (ANN) is introduced. In the proposed method, an external secret key is used to derive the initial conditions for the logistic chaotic maps which are employed to generate weights and biases matrices of the multilayer perceptron (MLP). During the learning process with the backpropagation algorithm, ANN determines the weight matrix of the connections. The plain image is divided into four subimages which are used for the first diffusion stage. The subimages obtained previously are divided into the square subimage blocks. In the next stage, different initial conditions are employed to generate a key stream which will be used for permutation and diffusion of the subimage blocks. Some security analyses such as entropy analysis, statistical analysis, and key sensitivity analysis are given to demonstrate the key space of the proposed algorithm which is large enough to make brute force attacks infeasible. Computing validation using experimental data with several gray images has been carried out with detailed numerical analysis, in order to validate the high security of the proposed encryption scheme.

Nonlinear systems parameter estimation using neural networks: Application to synchronous machines

This paper is devoted to state and parameter estimation for a large class of nonlinear systems using a radial basis function neural network predictor in the continuous time domain. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require the classical persistent excitation condition to be satisfied by the input signal. Comparisons between the results obtained and those of the method based on the sliding mode observer are also presented in the case of the estimation of the synchronous machine inductance parameters. The performance exhibited by the obtained results demonstrate that the proposed scheme can also work very well if the stator resistance varies due to the stator winding heating. The comparative results show globally that the proposed algorithm gives better performance than the method based on the sliding mode observer in terms of the convergence rate and the state/parameter accuracies.

A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system

ABSTRACT In this paper, a new hybrid method which combines radial basis function (RBF) neural network with a sliding-mode technique to take advantage of their common features is used to control a class of nonlinear systems. A real-time dynamic nonlinear learning law of the weight vector is synthesized and the closed-loop stability has been demonstrated using Lyapunov theory. The solution presented in this work does not need the knowledge of the perturbation bounds, neither the knowledge of the full state of the nonlinear system. In addition, the bounds of the nonlinear functions are assumed to be unknown and the proposed RBF structure uses reduced number of hidden units. This hybrid control strategy is applied to extract the maximum available energy from a stand-alone self-excited variable low-wind speed energy conversion system and design the dc-voltage and rotor flux controllers as well as the load-side frequency and voltage regulators assuming that the measured outputs are the rotor speed, stator currents, load-side currents and voltages despite large variation of the rotor resistance and uncertainties on the inductances. Finally, simulation results compared with those obtained using the well-known second-order sliding-mode controller are given to show the effectiveness and feasibility of the proposed approach.

Adaptive PI Control Strategy for a Self-Excited Induction Generator Driven by a Variable Speed Wind Turbine

In this paper, an adaptive proportional-integral (API) control strategy is developed to extract the maximum power from a variable wind speed turbine and to regulate the DC-link voltage, rotor flux and AC load voltage in a three-phase grid-connected self-excited induction generator (SEIG) system. The resulting controller associated to the flux-oriented control technique can be easily implemented in practice since finite time estimators for the unknown time-varying rotor resistance, rotor flux (nonmeasurable signal) and stator electrical angular position required for the online implementation of the proposed algorithm are provided. Comparative results with a conventional nonadaptive proportional-integral regulator have shown the superiority of the proposed strategy in terms of robustness with respect to online variation of the rotor resistance (up to 100%) and large varying load condition. The computing results are obtained using relatively low wind speed profile. Thus, the generating system with the proposed control strategy is suitable for variable wind speed turbine installation for grid-connected and remote-area power supply where the wind speed profile is relatively low.

Asymmetric Double Strange Attractors in a Simple Autonomous Jerk Circuit

The dynamics of a simple autonomous jerk circuit previously introduced by Sprott in 2011 are investigated. In this paper, the model is described by a three-time continuous dimensional autonomous system with an exponential nonlinearity. Using standard nonlinear techniques such as time series, bifurcation diagrams, Lyapunov exponent plots, and Poincare sections, the dynamics of the system are characterized with respect to its parameters. Period-doubling bifurcations, periodic windows, and coexisting bifurcations are reported. As a major result of this work, it is found that the system experiences the unusual phenomenon of asymmetric bistability marked by the presence of two different attractors (e.g., screw-like Shilnikov attractor with a spiralling-like Feigenbaum attractor) for the same parameters setting, depending solely on the choice of initial states. Among few cases of lower dimensional systems capable of such type of behavior reported to date (e.g., Colpitts oscillator, Newton–Leipnik system, and hyperchaotic oscillator with gyrators), the jerk circuit/system considered in this work represents the simplest prototype. Results of theoretical analysis are perfectly reproduced by laboratory experimental measurements.