Optimization of Sliding Mode Control for Doubly Fed Induction Generator Systems Using Particle Swarm and Grey Wolf Algorithms

This study proposes the utilization of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to optimize the gains in sliding mode control (SMC) for a doubly fed induction generator (DFIG) system. The primary aim is to enhance reference tracking, improve overall system performance, and ensure system stability. The controllers' performance is assessed by evaluating tracking performance and stability through the analysis of noise signals in the stator's active and reactive power controllers. The experiments involve 10 search agents in both PSO and GWO algorithms, with a maximum of 100 iterations. Statistical results reveal that the PSO algorithm demonstrates better convergence stability and yields lower fitness function values, particularly in the case of IAE. Simulations demonstrate that SMC tuning with PSO leads to satisfactory dynamics for active and reactive powers, characterized by fast response and no overshoot. Performance metrics, including IAE and ISE indices, are employed to assess the control strategies, consistently showing that the SMC with PSO controller outperforms the SMC with GWO controller in terms of criteria values. The proposed control strategy utilizing PSO optimization effectively enhances reference tracking and overall performance in DFIG systems.