Designing a PI-PDN controlled DFIG system using a fresh objective function and the Starfish Optimizer.

Abstract

This study presents a novel approach to transient performance regulation in doubly fed induction generator (DFIG) systems by employing a cascaded proportional-integral (PI) and proportional-derivative with a filter (PDN) controller tuned via the starfish optimization algorithm (SFOA). To improve both transient and steady-state response, a custom scalar objective function is formulated, integrating rise time, settling time, overshoot, and steady-state error. The designed controller structure combines an outer PI loop and an inner filtered PDN loop, allowing precise reference tracking and enhanced robustness. The performance of the proposed SFOA-based controller is rigorously evaluated against well-established optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), and synergistic swarm optimization algorithm (SSOA). Statistical performance, optimal parameter values, and detailed time-domain simulations are provided for comparison. The results clearly demonstrate that the SFOA-tuned PI-PDN controller delivers superior consistency, minimal overshoot, and faster dynamic response. This performance is validated across multiple trials, highlighting the potential of SFOA as an effective tool for control system design in renewable energy applications.