Stiffness-Aware Decentralized Dynamic State Estimation for Inverter-Dominated Power Systems

TL;DR

This paper introduces a stiffness-aware decentralized dynamic state estimation method for inverter-dominated power systems, enabling stable and accurate estimation at lower sampling rates by using local linear surrogate models and matrix-exponential discretization.

Contribution

It proposes a novel decentralized DSE approach that handles stiff system dynamics efficiently using statistical linearization and matrix-exponential discretization.

Findings

The method maintains stability with coarser sampling intervals.

Numerical results show improved estimation accuracy over conventional methods.

Stiff dynamics can destabilize traditional DSE, but the proposed approach mitigates this.

Abstract

Dynamic state estimation (DSE) is becoming increasingly important for monitoring inverter-dominated power systems. Due to their cascading control structures, inverter-based resources (IBRs) exhibit multi-timescale dynamics, leading to stiff system models that pose significant challenges for conventional DSE methods. In particular, explicit discretization schemes often require impractically small sampling intervals to maintain numerical stability, increasing computational and communication burdens. To address this issue, this paper proposes a stiffness-aware decentralized DSE method for inverter-dominated power systems. The statistical linearization is used to construct a local linear surrogate model for the nonlinear dynamics, which allows matrix-exponential discretization to enable analytical uncertainty propagation in discrete time, rather than relying on explicit integration schemes. This enables stable DSE at lower sampling rates. Numerical results reveal the mechanism by which stiff dynamics destabilize conventional DSE and demonstrate that the proposed method achieves efficient and accurate estimation under coarse sampling conditions.