Boubakeur Zegnini

Feedback linearisation control of an induction machine augmented by single-hidden layer neural networks

We consider adaptive output feedback control methodology of highly uncertain nonlinear systems with both parametric uncertainties and unmodelled dynamics. The approach is also applicable to systems of unknown, but bounded dimension. However, the relative degree of the regulated output is assumed to be known. This new control strategy is proposed to address the tracking problem of an induction motor based on a modified field-oriented control method. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modelling errors. The network weight adaptation rule is derived from the Lyapunov stability analysis, that guarantees boundedness of all the error signals of the closed-loop system. Computer simulations of an output feedback controlled induction machine, augmented via single-hidden-layer neural networks, demonstrate the practical potential of the proposed control algorithm.

Defects rotor identification by magnetic spectrum analyzing in a squirrel-cage asynchronous machine

This work presented the diagnosis of several defects types of the rotor bars for squirrel-cage asynchronous machine. The geometry of the model in tow dimensional is presented it consist of two sub-domain: stator and rotor. The Maxwell equations describe the local behavior in time and space of magnetic quantity, there are equations will be represented the most commune from Maxwell-Faraday, Maxwell-Ampere and equation of magnetic flux density conservation. Calculation well be done in the case of a healthy and unhealthy machine. In the first, the fractures are supposed to one, two and three broken bars adjacent, in the last we well take three different states for unhealthy machine, symmetries spaced of two broken bars, three broken bars and four broken bars. The finite element model used for solving Maxwells equations; in order to analyze the frequency spectrum of the magnetic flux density in the air gap. In fact, the influence of the number of broken bars adjacent and spaced on the functioning of the machine, and the influence of this last on the frequency spectrum will be studied.

A Least Squares Support Vector Machines (LS-SVM) approach for predicting critical flashover voltage of polluted insulators

Least Squares Support Vector Machines (LS-SVM) are a class of kernel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a Least Squares Support Vector Machines (LS-SVM). The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation of flashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover the performance of the proposed approach with other intelligence method based on ANN is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.

Electrical and optical analysis of the electric discharge, energized by AC voltage, on polluted insulating surface

This paper presents some results serving in modeling of flashover of polluted insulators, and also in the validity test of extension mechanisms of discharge. A flashover has been established on a laboratory model simulating a polluted insulator and has been read electrical and optical parameters in different points of the discharge path. Inspired by the previous works, optical results shown weak light intensity of the discharge comparable to streamers; these streamers could be used as forerunners of discharges that end to a flashover. This subject, besides its scientific relevance, is likely to have economic effects in the field of electrical energy distribution networks equipment.

Neural network-based adaptive control for induction motors

Neural network-based adaptive control scheme is developed to address the tracking problem of an induction motor (DVI) based on a modified version of field oriented control (FOC). In this paper, conventional PI controller is applied to regulate the speed and torque in the synchronous rotating coordinates. However, PI is simple but sensitive to parameter variations. Taking advantage of this fact, we aim to develop an adaptive control methodology that provides strong robustness to parameters variations, unmodelled dynamics and disturbance rejection. The obtained controller is then augmented by an online single hidden layer neural network (SHL NN) that is used to adaptively compensate for the partially known dynamics and unknown or varying system parameters. The network weights are adapted using a Lyapunov-based design. The effectiveness of the proposed controller is demonstrated through computer simulation.

Modeling flashover voltage (FOV) of polluted HV insulators using artificial neural networks (ANNs)

This paper attempts to apply artificial intelligent techniques in high voltage applications and especially to estimate the critical flashover voltage (FOV) for polluted insulators, using experimental measurements carried out in an insulator test station according to the IEC norm and a mathematical model based on the characteristics of the insulator: the diameter, the height, the creepage distance, the form factor and the equivalent salt deposit density and estimates the critical flashover voltage. Two types of artificial neural networks (ANNs) are designed to establish a nonlinear model between the above mentioned characteristics and the critical flashover voltage. The ANNs models, algorithms, and tools have been developed using the software package Matlab. The obtained results are promising and insure that artificial intelligent techniques can estimate the critical flashover voltage for new designed insulators with different operating conditions and constitute an indispensable models that can be used in field simulations of various parameters for polluted insulators. Further comparative analysis of the estimated results with the measured data collected from the site measurement amply demonstrate the effectiveness of the use of artificial intelligent techniques for modeling (ANNs) of FOV.

Feedback linearization control of nonlinear uncertain systems using single hidden layer neural networks

We develop an adaptive output feedback control methodology for highly uncertain nonlinear systems, in the presence of unstructured uncertainties, such as unmodelled dynamics, and unknown dimension of the regulated system. Given a smooth reference trajectory, the objective is to design a controller that forces the system measurement to track it with bounded errors. A linear in parameters neural network is introduced as an adaptive signal. A simple linear observer is proposed to generate an error signal for the adaptive laws. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded. The theoretical results are illustrated in the design of a controller for a fourth-order nonlinear system of relative degree two, and a tunnel diode circuit example having full relative degree.

Experimental and Numerical Investigations of Effect of Alternating Current Interference Corrosion on Neighboring Pipelines

High-voltage (HV) power lines sometimes share the same path as buried pipelines that are protected by an insulation coating and cathodic protection (CP). However, the neighboring HV power lines induce an alternating current (AC) that causes corrosion damage to metallic structures, which is known as the AC corrosion phenomenon. In this study, we conducted an experimental investigation on a laboratory model to realize electrochemical tests on a pipeline steel sample. Afterward, we performed numerical simulation studies of the electrochemical reactions involved in the corrosion, such as the anodic and cathodic processes, i.e., the iron oxidation and reduction of both oxygen and hydrogen. We also simulated the CP, AC corrosion, and deformation of the steel pipeline sample. Finally, to remedy the problem of AC corrosion damage, we developed a monitoring and correction program to optimize it. The main novelty of our work resides in our experimental and numerical simulation results, which were in good agreement, along with the development of the program for the automatic mitigation of AC corrosion.

Contribution to the study of piezomagnetic materials

In this paper we have presented a piezomagnetic model applied to a giant magnetostrictive material “Terfenol-D.” The presented model allows studying the total elongation through the interaction between the external magnetic field and the magnetic properties of materials. The resolution of the problem is performed using the finite element method in two-dimensional case. The obtained results showed the advantage of use of magnetostrictive materials in the design of actuators over conventional ferromagnetic materials. The total deformation due to the magnetostrictive effect is ten times higher in these materials than the conventional Fe-Si materials used in such applications.

2D and 3D modeling of the appearance of streamers in strong fields Trajectories tracing of charged particles

This work represents a simulation and a representation of aspects of electric discharge type streamer formation, and then the trajectories lines of charged particles in 2D and 3D on a laboratory model subjected to a high voltage. Our study is to compare the parameters that are the potential V, the electric field,the lines of electric field, the trajectories lines of the particles, the number of charged particles, their velocity and their density, the inter-electrodes distance and the minimum and maximum distances of trigger of the electric discharge type streamer on studied model. The steps followed are the electrostatic study and the path tracing of the charged particles. This study, yielded results that have shown that thousands of field lines could be analyzed simultaneously with reasonable computing efforts, the representation in 2D and 3D of streamer and its parameters is possible and gives clear and very precise information, the reduction in the inter-electrodes distance is a parameter that favors the formation of electric discharges type streamer and it is possible to determine the minimum distance and maximum distance trigger on studied model.