Identification of power line parameters based on spatial characteristics of aggregated power grid
The identification of power grid line parameters is of great significance.However,factors such as the increasingly complex grid structure and data pollution have negative effects on parameter identification,and traditional parameter identification methods cannot fully meet the current needs.In response to this problem,this paper proposes a multi-task Self-attention Graph Convolution(SAGCN)model that combines graph convolution operations and Transformers to deeply mine the spatial characteristics of power grid lines from different perspectives.Chebyshev graph convolution preprocesses spatial feature information,and Graph Former realizes global information interaction and adjacency reconstruction.By comparing traditional algorithms and noise interference experiments,the results show that the proposed model not only improves the accuracy of multi-target and multi-branch parameter recognition,but also has good robustness.
power lineparameter identificationgraph convolution operationTransformersupervisory control and data acquisition(SCADA)
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