Data-Driven State-Space Modeling for Small-Signal Stability Analysis of Black-Box Power Converters

This article introduces a data acquisition-based methodology to construct a state-space model of black-box Voltage Source Converters (VSCs) for application in small-signal stability analysis in modern power-systems using the Component Connection Method (CCM). The proposed approach determines the fitted state-space (FSS) model through the frequency-domain (FD) admittance matrix of the VSC, identified via the input-output responses of voltage and current at the point of connection (POC). By applying a multiple single-input single-output (SISO) vector fitting array, a stable FSS representation is determined with high accuracy. Order reduction methodologies such as Singular Value Decomposition (SVD) and Balanced Realization (BR) are then employed to generate the minimal representation of the VSC, eliminating redundant states and reducing computational burden. The presented approach is validated through pole analysis and comparison with electromagnetic transient (EMT) simulations for a grid-following (GFL) converter. The effectiveness and applicability of the methodology are further demonstrated through a small-signal analysis of a test system based on the IEEE 9-bus benchmark, incorporating a black-box VSC model.