Graph topology estimation of power grids using pairwise mutual information of time series data

The topology of a power grid is estimated using an information theoretic approach. By modeling the grid as a graph and using voltage magnitude data of individual nodes in the grid, the mutual information between pairs of nodes is computed using different approximation methods. Using the well-known Chow-Liu algorithm, a maximum spanning tree based on mutual information is computed to estimate the power grid topology. This manuscript explores the application of this method to different datasets and explores the domain of applicability. The data quality, precision, time windows, frequency and the method for calculating the mutual information are varied to see the effect on the successful reconstruction of the graph and it's leaf nodes. Success is shown for IEEE networks generated with MATPOWER and data generated using GridLAB-D. The algorithm is then cross-validated on IEEE networks.