Recursive Least-Square-Based Parameter Estimation for Dynamic State Estimation in Power Grids

Nowadays, Dynamic State Estimation (DSE) has become integral to power system control and real-time contingency analysis. The efficacy of DSE in wide-area power systems is contingent upon several factors, most notably the accuracy of information regarding the system topology, generation, and load. Given that these parameters can vary dynamically, it becomes imperative to accurately estimate ongoing changes; since misestimations can compromise control and protective actions within power grids. This paper introduces a Recursive Least Squares (RLS) approach, which is grounded on the inverse power flow problem, to effectively estimate the reduced admittance matrix (Y-bus) for DSE applications based on the measurements received from Phasor Measurement Units (PMUs). The proposed RLS estimation technique offers reliable estimates of system parameters despite their dynamic behavior (both smooth and sudden changes) in the presence of measurement noise. The efficacy of the proposed method is validated on the IEEE 14-bus test system using diverse DSE scenarios.