Data-Driven Distributed Stability Certification for Power Systems via Input-State Trajectories
Xiaohui Zhang, Liaoyuan Yang, Peng Yang
- Year
- 2026
- Access
- Open access
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.
Keywords
Related papers
A dual-loop framework for manufacturability-aware topology optimization of electric vehicle structures via wire arc additive manufacturing
Qiang Cui, Chuan Yu, Daoqian Yang +2 more
Robotics and Computer-Integrated Manufacturing · 2026
Geometric digital twin: A digital and intelligent model for aero-engine assembly accuracy prediction
Ke Shang, Xin Jin, Teli Xu +4 more
Robotics and Computer-Integrated Manufacturing · 2026
Revolutionizing Industries Through AI-Driven Robotics
Aryan Chaudhary
Recent Advances in Computer Science and Communications · 2026
Design and dynamic performance prediction of a novel large-aperture offset-feed deployable antenna
Chuang Shi, Tianming Liu, Ning Xue +6 more
Aerospace Science and Technology · 2026