Efficient Prediction of Admittance of Inverter-Based Resources Across Operating Points: A Comprehensive Test

This paper presents a comprehensive comparative study of two admittance prediction algorithms - data-driven and analytical - applied to Inverter-Based Resources (IBRs). The performance of these algorithms is compared using a set of white-box IBR models, with the same training and test points employed for both to ensure a consistent basis for comparison. The study evaluates the effectiveness of these methods in accurately predicting the admittance of IBRs, emphasizing the influence of Operating Point (OP) variations on prediction accuracy. Performance evaluation is conducted through a "goodness of fit"metric. Additionally, this paper conducts a sensitivity analysis of the Analytical Prediction Method (APM), examining its adaptability across different control structures and parameters. Ultimately, this paper validates the APM using a generic black-box model, underlining its applicability and potential in real-world scenarios.