Multistage Phasor-aided Bad Data Detection and Identification

This paper proposes a multistage Phasor-aided Bad Data Detection and Identification (PBDDI) method to improve state estimation accuracy. This method defines a special kind of innovation- the difference between estimated SCADA measurements (calculated from valid PMU state estimates) and raw SCADA measurements, then proposed statistical residual test is used in a cooperative effort for identifying bad data. The proposed approach has the advantages of handling various kinds of bad data all at once, including bad data smearing effect elimination, critical measurements detection. Besides, the adequate measurements replacement strategy can avoid the multiple round identification process, commonly adopted by the estimation residual analysis of hybrid estimators, resulting in computation reduction. Numerical tests on the IEEE-14 system under various cases verify the effectiveness of proposed method.