Constrained Robust Estimation of Power System State Variables and Transformer Tap Positions Under Erroneous Zero-Injections

This paper presents an equally constrained robust estimator of both the state and the transformer tap positions of a power system able to withstand all types of outliers, including bad leverage points and erroneous zero-injections. The statistical robustness of the estimator stems from the application of the Schweppe-type Huber GM estimator (SHGM) while its numerical robustness originates from the use of an orthogonal iteratively re-weighted least-squares algorithm together with the Van Loan's method for processing the equality constraints. The good performance of the new estimator, termed EC-SHGM estimator for short, is demonstrated on a small test system and on the Brazilian Southern power system with increasing size ranging from 139 buses to 1916 buses. It is shown that it exhibits superior convergence properties in all tested cases while the WLS method may suffer from numerical instabilities or even divergence problems when large weights are assigned to zero power injections modeling false information.