AWL-GCN: Branch Parameter Identification Considering Grid Spatial Structure Constraints

Abstract

Parameter identification plays an important role in power system. The existing parameter identification methods usually have two limitations: (1) The existing methods only consider the information of single branch and ignore the influence of adjacent branch information, and do not effectively use the topological structure constraints of the power grid. (2) The measurement data has the problems of poor numerical stability, numerical divergence and noise interference. Data contamination has become an important factor restricting the prediction accuracy of branch parameters. In order to solve these problems, this work proposes an Automatic Weighted Loss-Graph Convolution Networks model combined with the spatial structure of power grid. Based on the graphical modeling, the correlation between power grid branches is effectively used, and the adjacent branches are used to provide more effective information for the parameter identification of a branch, which improves the identification accuracy. In addition, the model enhances the local characteristics through the power grid structure information constraints, realizes the local optimal fitting, and constructs a self tradeoff loss function to weaken the impact of pollution data (data noise and data loss) on the model. The test results show that compared with the traditional method, this method has higher accuracy and stronger robustness.