Advancements in Static and Dynamic State Estimation Techniques for Modern Distribution Systems

This work advances both static and dynamic state estimation (SE) for modern electricity distribution systems, aiming to deliver practical, scalable, and accurate methods for real-time monitoring and operational decision-making. The proposed SE solutions are implemented using the Modified Augmented Nodal Analysis (MANA) framework, which ensures numerical stability, modularity, and seamless integration of diverse device models and measurements. The proposed SE algorithm results in three key contributions : 1- Enhanced Steady-State SE By incorporating load power factor (PF) pseudo-measurements, this approach addresses the coupling between active and reactive power (P and Q), reducing biases from noise and limited data; 2- A dynamic SE algorithm leverages Weighted Least Squares (WLS) with forecasted states (via Single Exponential Smoothing (SES), Adaptive SES, and Holt’s Double Smoothing) as pseudo-measurements. Using a sparse Hachtel formulation this approach avoids full matrix inversion, maintaining scalability for large systems; 3- An Iterative Extended Kalman Filter