Self-Tuning Genetic Algorithm-Based PI Controller for Optimizing Wind Energy Conversion Systems

Abstract

The current regulation of wind energy conversion systems, especially speed adjustment to achieve optimal power and rotor adjustment for the doubly-fed induction generator (DFIG), is crucial for protecting the machine and ensuring efficient electrical energy production from wind. This paper presents an innovative approach using an improved proportional-integral (PI) controller enhanced by a genetic algorithm (GA) to regulate speeds, rotor currents, and both active and reactive power produced by the DFIG in a wind energy conversion system (WECS). The system's performance in tracking setpoints has been analyzed and compared using MATLAB Simulink. The results demonstrate the effectiveness of this type of control in both steady-state and transient conditions.