Commande par Backstepping d’un Système de Conversion d’Énergie Eolienne Multi-machine (Backstepping Control of a Multi-Machine Wind Energy System)

Abstract

This thesis presents a methodology to improve the performance of a multi-machine wind power generation system (WPGS) by incorporating nonlinear and intelligent control techniques. The primary objective is to optimize the system's performance, which utilizes permanent magnet synchronous generators (PMSGs) connected to the electrical grid. To enable this grid interconnection, a novel back-to-back converters (NBTBCs) configuration is employed, comprising a fifteen-switch rectifier (FSR) and a conventional inverter, with control implemented using pulse width modulation (PWM). The control algorithm has been meticulously designed to efficiently control the system while concurrently minimizing fluctuations in both active and reactive power. Initially, a thorough model of the wind power generation system is presented. This is then followed by a comprehensive exposition of the fuzzy backstepping control (FBC) law, which seamlessly incorporates the Lyapunov stability technique. The utilization of fuzzy logic (FL) serves to adaptively adjust the gains within the backstepping (BC) framework, thereby ensuring that the control system is capable of effectively responding to disturbances and variations in system parameters. Consequently, this adaptive control strategy significantly enhances the overall efficiency of the system in both static and dynamic operational modes. The conducted simulation tests employing MATLAB have yielded a comparative analysis of the proposed strategy against the traditional backstepping control (BC) approach. The findings demonstrate that the fuzzy backstepping control (FBC) methodology exhibits robust performance and superior reference tracking capabilities, successfully mitigating speed overshoot under diverse wind conditions. These results validate the effectiveness and advantages of the proposed control strategy over conventional methods in managing the inherent complexities associated with wind power generation systems (WPGSs).