  Estimation of the operational topology of the power grid is necessary for
optimal market settlement and reliable dynamic operation of the grid. This
paper presents a novel framework for topology estimation for general power
grids (loopy or radial) using time-series measurements of nodal voltage phase
angles that arise from the swing dynamics. Our learning framework utilizes
multivariate Wiener filtering to unravel the interaction between fluctuations
in voltage angles at different nodes and identifies operational edges by
considering the phase response of the elements of the multivariate Wiener
filter. The performance of our learning framework is demonstrated through
simulations on standard IEEE test cases.
