From State Estimation to Network Reconstruction

Abstract

We develop methods to efficiently reconstruct the topology and line parameters of a power grid from the measurement of nodal variables. We propose two compressed sensing algorithms that minimize the amount of necessary measurement resources by exploiting network sparsity, symmetry of connections and potential prior knowledge about the connectivity. The algorithms are reciprocal to established state estimation methods, where nodal variables are estimated from few measurements given the network structure. Hence, they enable an advanced grid monitoring where both state and structure of a grid are subject to uncertainties or missing information.

8 pages, 6 figures

References in corpus (5)

• Revealing Network Connectivity From Dynamics
• Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems
• Revealing networks from dynamics: an introduction
• Model-free inference of direct network interactions from nonlinear collective dynamics
• Revealing physical interaction networks from statistics of collective dynamics

Cited by in corpus (1)

• Reconstructing Network Structures from Partial Measurements