Graph approach for observability analysis in power system dynamic state estimation
Akhila Kandivalasa, Marcos Netto
TL;DR
The paper tackles the computational bottleneck of observability analysis in dynamic state estimation for power systems by introducing a graph-based approach. It constructs a digraph from the nonlinear state-space equations and uses root strongly connected components to determine structural observability, with a linear-time algorithm based on Kosaraju-Sharir. The method yields results equivalent to the traditional Lie-derivative (L) framework but with dramatic speedups (e.g., ~$1440\times$ faster in centralized DSE for a 3-machine, 9-bus system), and it is parameter-agnostic and scalable to large grids. This work enables scalable DSE observability analysis and supports future optimization tasks such as PMU placement and integration of inverter-based resources.
Abstract
The proposed approach yields a numerical method that provably executes in linear time with respect to the number of nodes and edges in a graph. The graph, constructed from the power system model, requires only knowledge of the dependencies between state-to-state and output-to-state variables within a state-space framework. While graph-based observability analysis methods exist for power system static-state estimation, the approach presented here is the first for dynamic-state estimation (DSE). We examine decentralized and centralized DSE scenarios and compare our findings with a well-established, albeit non-scalable, observability analysis method in the literature. When compared to the latter in a centralized DSE setting, our method reduced computation time by 1440x.