Bad Data Suppression in Power System Static State Estimation

Abstract
The presence of bad data points may severely degrade the performance of any of the power system static state estimators currently being proposed. This paper discusses a BDS (bad data suppression) estimator which is based on a non-quadratic cost function but which reduces to the weighted least squares estimator in the absence of bad data. In the presence of bad data, the BDS algorithm provides state estimates comparable to those provided by the weighted least squares method when all data is good. Computer storage and computational requirements and convergence time are equivalent for the BDS and weighted least squares estimators.

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