This invention discloses a real-time
power grid state
estimation method based on non-Euclidean geometry. The method maps
voltage phase angles to a compact manifold, constructing a
hybrid state space consisting of the Euclidean space of
voltage amplitudes and the
Cartesian product of the compact manifold of phase angles. The state is then mapped to the tangent space for prediction and
covariance propagation via logarithmic mapping, and then back to the manifold via exponential mapping. An innovation vector on the manifold is calculated, and a periodic normalization function is used to process the
phase angle measurement residuals, performing state correction in the manifold space. Furthermore, topology sensing is performed based on simplex theory, detecting topology events through Betti numbers and adaptively adjusting filters. Based on the estimated state, a continuous
reactive power control optimization problem is solved in the tangent space, and a smooth, bounded control quantity is obtained through Lie exponential mapping. This invention fundamentally solves the
phase angle jump problem, achieving
millisecond-level real-time state
estimation and significantly improving
estimation accuracy and adaptability to topology changes.