Observability analysis and bad data processing for state estimation using Hachtel´s augmented matrix method

Triangular-factorization-based observability analysis and normalized-residual-based bad-data processing are extended to state estimation using Hachtel´s augmented matrix method, which is numerically robust, computationally efficient, and reasonable in extra storage requirement. It is shown that the observability analysis can be carried out in the course of triangular factorization of the augmented coefficient matrix used in Hachtel´s method. The normalized residuals can be obtained using the sparse inverse of this augmented matrix. The algorithms have been successfully incorporated in the state estimation program developed at the Norwegian State Power Board. Test results on an IEE-14 bus system and a 99-bus system consisting of the main grid of southern Norway are presented. The results confirm that Hachtel´s approach to state estimation provides an attractive alternative to the standard normal equations approach