Data-driven estimation of impedance of inverter-based resources for efficient stability evaluation

Abstract

Inverter-based resources (IBRs) are key enabling technologies for integrating renewable energy sources and providing ancillary services in modern power systems. However, their dynamic behavior, defined by output impedance models, can pose a threat to power system stability. The primary challenge is that impedance models, typically derived at specific operating points, exhibit limited accuracy under varying conditions. Additionally, the lack of detailed vendor information on commercial IBR control structures complicates the accurate derivation of these models. To address these issues, this paper first investigates the effects of grid parameters and variations in IBR operating points on IBR’s impedance model. Afterwards, a data-driven algorithm using Gaussian process regression (GPR) is then proposed to predict impedance models in the dq reference frame, achieving accurate results with a minimal dataset, thus reducing the cost and complexity of data collection for stability evaluation. The proposed approach is validated through case studies that compare predicted impedance models with analytical solutions for various IBR configurations and grid scenarios, including both grid-following and grid-forming inverters. Its superiority over artificial neural network (ANN)-based approaches is demonstrated using the same training dataset. The predicted impedance model is employed to evaluate IBR stability in the frequency domain, with findings validated through time-domain simulations using an electromagnetic transient (EMT) model when connected to grids of varying strengths. A promising application of the proposed GPR-based impedance modeling is its integration into IBR-based power system stability analysis and simulation tools, facilitating the study of emerging low-frequency oscillation phenomena.