A Bayesian Approach to Bad Data Identification in Power System State Estimation

This paper addresses the problem of robust identification of gross errors affecting both measurements and network parameters in power system state estimation. The study is conducted within a steady-state framework and focuses on improving bad data identification in the presence of modeling and measurement uncertainties, explicitly accounting for the limited observability of gross errors. Building on an Extended Weighted Least Squares (EWLS) estimator and a theoretically refined eigenvalue-based clustering of dominant error components, a novel Bayesian identification framework is introduced. The proposed Bayesian approach assigns probabilities to competing gross error models, including scenarios involving multiple simultaneous errors, given the observed clusters of dominant errors. This probabilistic formulation enables a systematic and quantitative decision-making process for identifying the most likely sources of gross errors, extending existing deterministic or heuristic approaches. The methodology is evaluated through numerical simulations on the IEEE-14 bus test system, considering several gross error scenarios and significant parameter uncertainties. The results demonstrate that the proposed Bayesian framework enhances the interpretability and discriminative capability of gross error identification, highlighting its potential for robust bad data identification in power system state estimation.