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This research focuses on the evolving dynamics of the power grid, wheretraditional synchronous generators are being replaced by non-synchronous powerelectronic converter (PEC)-interfaced renewable energy sources. The non-lineardynamics must be accurately modeled to ensure the stability of futureconverter-dominated power systems (CDPS). However, obtaining comprehensivedynamic models becomes more complex and computationally intensive as the systemgrows. This study proposes a scalable and automated data-driven partitionedmodeling framework for CDPS dynamics. The method constructs reduced-ordereddynamic linear transfer function models using input-output measurements from aPEC switching model. Validation experiments were conducted on single-house andmulti-house scenarios, demonstrating high accuracy (over 97%) and significantcomputational speed improvements (6.5 times faster) compared to comprehensivemodels. This framework and modeling approach offers valuable insights forefficient analysis of power system dynamics, aiding in planning, operation, anddispatch.

Automated Data-Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function