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

• Published in: Electric power systems research 1991-03, Vol.20 (3), p.205-213
• Main Authors: Simões Costa, A., Rolim, J.G., Aitchison, P.W.
• Format: Article
• Language: English
• Subjects: Applied sciences; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Power networks and lines; Theory. Simulation
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/0378-7796(91)90065-U

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.