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, the need for accurate models becomes important to ensure reliable grid planning and operation. Electromagnetic transient (EMT) studies using detailed models can capture important system dynamics, but these models often lack availability or are computationally demanding. To address these challenges, standardized and flexible generic models have been proposed, though their calibration remains a key issue. This paper presents a parameter estimation framework designed to improve the calibration of generic IBR models, employing Particle Swarm Optimization (PSO). The focus is on integrating data from both Time and Frequency Domain testing to capture the system dynamics, and use these data to calibrate key control parameters. While the framework is mainly conceptualized to utilize data collected from Hardware-in-the-Loop (HIL) setups, it is flexible enough to be adapted to different cases, e.g., including other model parameters, and leveraging different types of data, such as offline simulation or measurement data. The proposed framework is demonstrated through a Type-4 generic wind turbine model, estimating six proportional-integral (PI) control parameters across active power, reactive power, and RMS voltage control loops. The study explores the use of different test cases and sensitivity analysis to optimize the calibration process. Results from proof-of-concept experiments showed that Time Domain data alone could effectively calibrate these parameters, providing a close match to reference values. Due to the overlap between control bandwidths of this specific subset, Frequency Domain testing was not advantageous in this case. The findings suggest that although Frequency Domain data might offer benefits, its application is context-dependent, especially when overlapping control dynamics are present. Future work will expand the framework to integrate all key grid-following control parameters and further explore the benefits of combining Time and Frequency Domain data for parameter calibration.