Mohammed Belkheiri

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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(More)
A new control approach is proposed to address the tracking problem of an induction machine based on a modified field-oriented control (FOC) method. In this approach, one relies first on a partially known model to the system to be controlled using a backstepping control strategy. The obtained controller is then augmented by an online neural network that(More)
LS-SVM (least squares support vector machines) 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(More)
The objective of this paper is to design and implement a third harmonic injection PWM strategy THIPWM on an FPGA, to control a two-level three phase PWM inverter, in order to generate a fundamental component, with variable amplitude and frequency of the inverter output voltage, by varying the modulation index and reference frequency. The developed strategy(More)
This paper presents the control of a photovoltaic distributed generation system based on dual-stage topology of DC-DC boost converter and three-level neutral-point-clamped (NPC) voltage source inverter (VSI). Decoupling control strategy of three-level VSI is proposed to control the current injected into the grid, reactive power compensation, and DC-link(More)
We aim to design in the present paper an adaptive output feedback control scheme to address the tracking problem of an uncertain system having full relative degree in the presence of neglected dynamics and modelling errors. Then, the obtained controller is augmented by an online radial basis function neural network (RBF NN) that is used to adaptively(More)
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