From State Estimation to Network Reconstruction
For power grid operators, it provides a way to infer network structure from limited measurements, addressing scenarios where topology is unknown or changing.
The paper develops compressed sensing algorithms to reconstruct power grid topology and line parameters from nodal measurements, minimizing measurement resources by exploiting network sparsity and symmetry. The methods enable grid monitoring when both state and structure are uncertain.
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.