Développement d'une stratégie intelligente de contrôle et de gestion d'un système hybride connecté au réseau

Abstract

This thesis focusses on the development and of advanced intelligent control and management strategies for a grid-connected hybrid renewable energy system, integrating photovoltaic (PV) , exchange membrane fuel cells (PEMFCs), and wind energy conversion systems (WECS). , the mentioned sources face several challenges, low conversion efficiency, particularly under weather conditions, and the nonlinear characteristics of their output power and current. Therefore, the primary objective is to optimize power extraction, enhance grid integration, and ensure system stability under dynamic operating conditions. maximum power point tracking (MPPT) algorithms are proposed for PV systems, including a high-order sliding mode Super-Twisting (STA) to chattering effects, a two-level artificial neural network-based model control (ANN-), and a double-stage ANN-finite control set model control (ANN-FSC-MPC). Moreover, this research also focuses on the modeling and control design of variable-speed wind turbines. The aim is to extraction from the wind below the rated power range, while controlling electrical power output above the rated power range, all reducing transient disturbances.