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Stiff dynamics continue to pose challenges for power system dynamic state estimation. In particular, models of inverters with control schemes designed to support grid voltage and frequency, namely, grid-forming inverters (GFMs), are highly prone to numerical instability. This paper develops a novel analytical modeling technique derived from two cascading subsystems, namely synchronization and dq-frame voltage control. This allows us to obtain a closed-form discrete-time state-space model based on the matrix exponential function. The resulting model enables a numerically stable and decentralized dynamic state estimator that can track the dynamics of GFMs at standard synchrophasor reporting rates. In contrast, existing dynamic state estimators are subject to numerical issues. The proposed algorithm is tested on a 14-bus power system with a GFM and compared with the standard algorithm whose process model is discretized using well-known Runge-Kutta methods. Numerical results demonstrate the superiority of the proposed method under various conditions.
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This content will become publicly available on July 27, 2026
On the Analytical Modeling and Discretization of Grid-Forming Inverters
The stiff dynamic model of grid-forming inverters (GFMs) presents numerical challenges in time discretization for real-time simulation, estimation, and control. To address this challenge, this paper proposes modeling GFMs as two cascading subsystems: a reference-frame-synchronization system and an in-reference-frame system. This paper demonstrates that these two subsystems are approximately linear, allowing their time discretization to be derived in closed form using the matrix exponential. The proposed discretization method is tested on the IEEE 14-bus power system with a GFM connected and compared with second- and fourth-order Runge-Kutta methods. The numerical results validate the proposed method and demonstrate its potential for discretization with longer step sizes.
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- Award ID(s):
- 2328241
- PAR ID:
- 10652259
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1-5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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