Special Session III: Data-AI Driven State Estimation and Stability Analysis

Session Chair: Asst. Researcher, Peng Wang, Shandong University, China	Co-Chair: Assoc. Prof. Yiyan Li, Shanghai Jiao Tong University, China

Information: With the large-scale integration of renewable energy resources, power electronic devices, and multi-source sensing infrastructures, modern power systems are becoming increasingly stochastic, highly coupled, and dynamically time-varying. These features impose new requirements on accurate state estimation, online stability assessment, and early-warning technologies. This special session focuses on data- and AI-driven methods for state awareness, dynamic modeling, stability analysis, and online risk prediction. Topics include multi-source heterogeneous measurement fusion, physics-informed and data-driven modeling, robust and distributed state estimation, low-frequency and wideband stability analysis, impedance identification, digital twins, and AI-assisted control. The session aims to promote the application of data-AI technologies in the secure and stable.

Topics of interest include, but are not limited to:

1. Static and dynamic state estimation methods for power systems with high penetration of renewable energy resources;
2. Multi-source information fusion and state awareness based on PMU, SCADA, WAMS, edge measurements, and equipment operation data;
3. Robust state estimation methods considering measurement noise, bad data, communication delays, and missing data;
4. Power system state identification and dynamic prediction based on graph neural networks, deep learning, reinforcement learning, and foundation models;
5. Physics-informed and data-driven methods for stability assessment, stability margin evaluation, and stability boundary identification;
6. Low-frequency oscillation, wideband oscillation, and impedance-based stability analysis of renewable-energy-integrated power systems;
7. Impedance identification, modal analysis, and oscillation source location methods for converter-dominated power systems;
8. Digital twins, model updating, and real-time simulation methods for online stability assessment and early warning;
9. Data- and AI-assisted methods for stability control, oscillation damping, and operation optimization;
10. Applications of trustworthy AI, explainable AI, and data security technologies in state estimation and stability analysis.

Keywords: Artificial Intelligence; Data-Driven Methods; Stability Analysis; Multi-Source Measurement Fusion; Wideband Oscillation; Impedance Identification; Digital Twin

Submission Deadline: September 20, 2026