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

TL;DR

This paper presents a scalable, automated data-driven framework for modeling inverter dynamics in power systems, achieving high accuracy and computational efficiency, crucial for stable future grid operations with renewable sources.

Contribution

It introduces a novel partitioned modeling approach that simplifies complex inverter dynamics into reduced-order models using input-output data, enhancing analysis speed and accuracy.

Findings

Achieved over 97% modeling accuracy.

Modeling process is 6.5 times faster than traditional methods.

Validated on single and multi-house scenarios.

Abstract

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offers valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.