Comparison between conventional anc post-processing PMU-based state estimation to deal with bad data

Detection and analysis of bad data is one of the most important sector of static state estimation. This paper focuses on the comparison between a novel method for multi bad data detection and identification in PMU-based state estimation, namely post-processing PMU-based method for state estimation and the conventional PMU-based state estimation. To accomplish this object, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the second level of state estimation. The proposed algorithm of phasor measurements utilization in state estimation can prove that post-processing algorithm can detect and identify multi bad data in critical measurements, which it is not detectable by conventional methods. To validate simulations, IEEE 30 bus is implemented in PowerFactory and Matlab is used to solve proposed state estimation using post-processing of PMUs. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analysis of bad data are available by result of largest normalized residual test.