Robust algorithm for state estimation in electrical networks

A state estimation algorithm is proposed which rejects bad data in the presence of noise. This rejection is accomplished during convergence by means of reweighting, the final solution being the least squares estimate based on the remaining good data. The reweighting is also used to improve convergence with nonlinear measurements. A new 2×2 submatrix formulation has been used allowing full active/reactive power coupling of the estimator with greatly reduced computational overheads. Including coupling effects greatly improves bad data detection and identification