Bad Data Detection and Identification for State Estimation: An Enhanced Strategy

Bad data analysis is an important part of both dynamic and static state estimations. This paper present novel algorithm of phase measurement unit (PMU)-based static state estimation to detect and identify multiple bad data in critical measurements, which is not possible with traditional static state estimations. To achieve this object largest normalized residual test (r^N_max) is applied to detect and analysis bad data in phasor measurements, power flow and power injections of buses used for the novel PMU-based state estimation. The main advantage of new PMU-based static state estimation is that phasor measurements can be added separately into the proposed state estimation. This paper proposes an ideal method to combine the phasor measurements into the conventional state estimator in a systematic way, so that no significant modification is necessary to the existing algorithm. The main advantage of this method is the use of phasor measurements to improve results of conventional state estimator and detect the bad data associated with critical measurements, which cannot be detected by the conventional sate estimation algorithm. This algorithm will be developed based on (r^N_max) test that shows that phasor measurements can be used to add redundancy to conventional measurements, which are critical in nature, hence bad data in such measurement can be identified. To validate simulations, IEEE 30 bus implemented in PowerFactory and Matlab is used to solve proposed state estimation using post-processing of PMUs. Bad data is generated manually and added into the phasor measurements profile. Finally, bad data is detected at the bus 16 and line 10-16 with amount normalised residue 4:17 by the result of largest normalized residual test.