Enabling Small-Signal Stability Analysis of Black-Box Voltage Source Converters in Large-Scale Modern Power Systems
For power system engineers, it enables stability analysis of black-box converters, overcoming a key limitation of existing identification-based techniques.
The paper presents a methodology (SSA-FITSS) for small-signal stability analysis of black-box voltage source converters, enabling accurate modal analysis in large-scale power systems. Validation on a grid-following converter and the New England system shows it reproduces dynamics and reveals stability limits under varying generation and converter penetration levels.
Modern power systems increasingly rely on power electronic converters, yet many of these devices are provided as black-box models, limiting the applicability of conventional small-signal analysis (SSA) tools. This work presents a unified multi-variable fitted state-space (SSA-FITSS) methodology that enables accurate small-signal modeling of black-box Voltage Source Converters (VSCs) using frequency-domain (FD) identification, adaptive pole-expansion, and reduced-order realization. The method includes an automated state-interpretation strategy that assigns fitted states to representative control-loop categories based on their dominant frequency ranges, providing an approximate but meaningful physical interpretation of the identified dynamics. This capability allows extensive modal analysis, including eigenvalue sensitivities and participation factors, in systems where internal converter details are unavailable. The methodology is validated on a grid-following (GFL) VSC and applied to the New England system, which contains multiple black-box converters operating in both GFL and grid-forming (GFM) modes. Results show that the SSA-FITSS models accurately reproduce converter and system dynamics, support full eigenvalue-based analysis, and reveal stability limits under varying synchronous generation and GFL penetration levels. The approach overcomes key limitations of existing identification-based techniques by enabling scalable, interpretable, and system-wide stability assessment.