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).
