Data-Driven Distributed Stability Certification for Power Systems via Input-State Trajectories
For power system operators, this provides a model-free method to certify stability using only trajectory data, addressing the challenge of unknown or complex dynamics.
The paper proposes a data-driven framework to certify system-wide stability of interconnected power systems by verifying output differential passivity (ODP) conditions from measured input-state trajectories, without requiring physical models. Simulations demonstrate effectiveness in both offline and online scenarios.
This article proposes a data-driven framework to verify the distributed conditions that guarantee the system-wide stability for interconnected power systems. To guarantee system wide stability, the dynamics of each bus are required to satisfy an output differential passivity (ODP) condition with a sufficient index. These ODP indices uniformly quantify the impacts on the system-wide stability of individual bus dynamics and the coupling strength from the power network. To obtain these indices without explicit physical models, we derive a data-driven linear matrix inequality (LMI) criterion based exclusively on measured input-state trajectories. Furthermore, extracting the optimal ODP index is formulated as a convex semi-definite programming (SDP) problem. Simulations verify the effectiveness of the proposed method under both single-device offline evaluation and system-wide online certification scenarios.