A low-quality PMU data identification method with dynamic criteria based on spatial-temporal correlations and random matrices

With the high access of renewable energy, complex and changeable transmission networks, and frequent load interactions, the dynamic characteristics of low-carbon power systems have become more complex and random. The 10 ms-level dynamic measurement data of Phasor Measurement Unit are the basis for dynamic awareness, control and decision. However, the phenomenon of low-quality data usually exists in PMU measurements. Considering the spatial-temporal correlation reflected by the random matrix single-ring theorem and correlation coefficients, two system operating states and two types of low-quality PMU data are determined. Based on singular value decomposition and reconstruction, the distribution of the residuals between the original data and reconstructed data is analyzed to realize low-quality PMU data identification. To improve the identification accuracy, a dynamic threshold selection method of spatial-temporal correlation analysis is proposed for iden-tification criteria. The feasibility and applicability of this method has been verified in the simulation data of IEEE 39 bus system and PMU measured data of the practical power grid.