**Data Analytics for Grid Situational Awareness**

With millions of sensors deployed across the power grid, system operators receive a massive amount of raw data. However, translating this data into actionable insights is essential for real-time decision-making. Techniques such as observability analysis, state estimation, anomaly detection, and forecasting play a crucial role in establishing grid situational awareness, particularly in low-observable distribution networks where real-time sensor data may be limited, delayed, or unreliable. Additionally, synchronizing data from heterogeneous sensors and ensuring accurate state estimation remain key challenges.

To address these challenges, we developed AI-assisted state estimation techniques to provide state variables in high-resolution. Additionally, we built fault localization models using deep learning and hybrid sensor data, including DFOS and uPMUs, to improve localization accuracy. Furthermore, we proposed deep learning-based load and voltage estimation frameworks to enable real-time grid situational awareness.