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

Wenchao Song, Chao Lu, Junjie Lin, Chen Fang and Shu Liu

Applied Energy, 2023, vol. 343, issue C, No S0306261923005779

Abstract: 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 identification 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.

Keywords: Phasor measurement unit; Low-quality data; Random matrix; Spatial–temporal correlation; Singular value decomposition (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.apenergy.2023.121213

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