Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators

Abstract

This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system’s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems.