Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model - Katanic, Milos; Lygeros, John; Hug, Gabriela (IET generation, transmission & distribution, 2024)
Power systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates. This article proposes the Iterated Extended Kalman Filter for the optimal state estimation for general DAE power system models. The article also introduces a graphical method for placing real‐time measurements to achieve the unique state reconstruction.