Stiffness-Aware Decentralized Dynamic State Estimation for Inverter-Dominated Power Systems
For power system operators, this method improves the practicality of monitoring inverter-dominated grids by reducing computational and communication burdens without sacrificing estimation accuracy.
The paper addresses the challenge of dynamic state estimation in inverter-dominated power systems with stiff multi-timescale dynamics. The proposed stiffness-aware decentralized method enables stable and accurate estimation at lower sampling rates, overcoming the numerical instability of conventional approaches.
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.