Observability analysis and bad date processing of state estimation using Hachtel's augmented matrix method

Abstract

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

Keywords

• Electric power system
• static state estimation
• error covariance sparse matrices
• least squares