Iterative bad-data suppression applied to state estimators based on the augmented matrix method

The augmented matrix method for power system state estimation combines simple conception, good numerical behavior and computational efficiency. Concerning bad-data processing, however, the method presents a difficulty: the calculation of normalized residuals is not straightforward, so that the implementation of conventional bad-data identification procedures may become complicated.

This paper presents a technique for bad-data processing based on weighted residuals and a nonquadratic cost function to circumvent that problem. The weighted residuals are immediately available from the proposed formulation for the augmented matrix method. The non-quadratic cost function is piecewise quadratic-constant and the break points are varied through the iterations to allow proper bad-data identification. The application of a diakoptical technique avoids the need for costly refactorizations of the augmented matrix. The results from simulation studies carried out with the IEEE 14-bus and 30-bus test systems are presented.