Control parameter estimation encompassing time and frequency domain test cases using Particle Swarm Optimization

As the penetration of inverter-based resources (IBRs) in power grids increases, accurate models are required to ensure reliable planning and operation. This paper presents a framework using Particle Swarm Optimization (PSO) for control parameter estimation. Both Time and Frequency Domain cases are integrated, focused on calibrating a generic wind turbine model and six proportional-integral (PI) control parameters across power and voltage loops. The methodology leverages HIL setups, is adaptable to various data sources, and investigates sensitivity analysis for optimization. Results demonstrate calibration effectiveness and outline domain-dependent benefits of frequency data.