Data-Driven Distributed Stability Certification for Power Systems via Input-State Trajectories

TL;DR

This paper introduces a data-driven method to verify stability conditions in power systems using input-state trajectories, avoiding explicit models and enabling system-wide stability certification.

Contribution

It develops a novel data-driven LMI criterion for stability verification and formulates the ODP index extraction as a convex SDP problem.

Findings

Effective stability certification demonstrated through simulations.

Applicable to both offline device evaluation and online system certification.

Avoids reliance on explicit physical models by using measured trajectories.

Abstract

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.