Graph approach for observability analysis in power system dynamic state estimation

TL;DR

This paper introduces a novel graph-based method for dynamic state estimation in power systems that is computationally efficient and scalable, enabling faster observability analysis compared to traditional methods.

Contribution

It presents the first graph-based approach for dynamic-state estimation observability analysis, achieving linear time complexity and significant speed improvements.

Findings

Linear time complexity in graph size.

1440x reduction in computation time.

Effective in both decentralized and centralized scenarios.

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.