New Bad Data Rejection Algorithm using Nonquadratic Objective Function for State Estimation

This paper first illustrates a state estimator with a quadratic-constant objective function in which the detection and rejection of bad data, or faulty measurements, are merely consequences of the objective function form. Then, as countermeasures for multiple interacting gross bad data, a novel implementation of bad data rejection scheme is proposed and applied to this estimator. In this scheme, both bad and suspected measurements are removed from calculation so as to avoid deterioration of the state estimate, and thus avoid misidentifications of non-faulty measurements. Furthermore the optimal multiplier mu , calculated using a subroutine taking into account the bad and suspected measurements, is introduced to compensate for the reduced redundancy, improve convergence characteristics, and properly detect bad data. Numerical simulations are carried out using the IEEE 6, 30, and 118 bus test models to verify the validity of the proposed method.