Bad Data Identification and Correction Algorithm of μPMUs

For precise monitoring and diagnosis of power grids, WAMS based on PMU is gradually being expanded and applied. KEPCO installed 35 μPMUs at Yeonggwang substation as part of its efforts to build and utilize a next-generation intelligent transmission grid operation system. However, a large amount of μPMU data can be damaged due to various causes such as communication failure, disturbance, and synchronization instability. In this paper, we propose an identification and correction algorithm of bad data, such as event duplicate and spike, for reliable and high-precision analysis of the RESs-linked substation. First, we outline a high-precision WAMS of the substation, which is connected to RESs. Second, we propose a μPMU bad data identification using slope and count targeting 35 μPMUs installed in a 154kV substation. A correction technique using average-based is presented and evaluated. Finally, the simulation results using 11 days of μPMU Raw data collected from the substation showed that the proposed algorithm is satisfactory.