Study and implementation of a non-conventional control of the doubly-fed induction machine for wind energy production

Abstract

This thesis aims to enhance the control of Doubly Fed Induction Generators (DFIG) used in variable speed wind turbines, which are vital for maximizing energy extraction in varying wind conditions. It introduces a fractional-order fuzzy PD+I (FO Fuzzy PD+I) regulator, optimized with the social spider optimizer algorithm, representing a significant advancement over traditional PI regulators. The FO Fuzzy PD+I regulator combines fuzzy logic with fractional-order control, improving robustness and performance in controlling DFIG current while effectively managing uncertainties in parameters and wind speed variations. Through simulations across various operational scenarios, the proposed regulator consistently outperforms the traditional PI regulator in key performance metrics such as integral time absolute error (ITAE), peak overshoot, settling time, and total harmonic distortion (THD). This research makes a significant contribution to DFIG control, providing a more effective solution for wind turbine operation and enhancing power quality and grid integration capabilities.