Generalized state-estimation algorithm using network solvability

Abstract

A generalized state-estimation algorithm is derived using linear weighted least-squares theory and concepts of network solvability for any arbitrary distribution of given or measured variables. With a Lagrange tree, the power system state variables become the nodal, or busbar, voltages and the input data the measured line flows. The algorithm is stable and accurate in both static and tracking modes of operation, and a recursive method is suggested for its incorporation into an online state-estimator scheme involving bad data detection and identification and estimation of network configuration.