Parameter Identification of Power Distribution System Based on Dynamic Graph Topology

Because of the high permeability of renewable energy, achieving the real-time state of the distribution system, including the topology, essential parameters and operation condition, is now crucial to its delicate analysis and determination. In the active distribution system, the frequently changing topology of the network impedes the accurate identification of the line parameters, which is sensitive to the natural disaster factors. Inspired by the latest progress of deep learning, we propose a dynamic graph fusion network (DGFN) architecture to tackle the topology and line parameter joint identification problem. The DGFN model is based on the graph deep learning method, which constructs a new topology according to the changes of node features through the radius graph method, and forms the corresponding adjacency matrix, which helps to improve the line parameters identification accuracy. The architecture can be used for any distribution system, only the adjacency matrix and node feature data need to be inputted. Masses experiments show that the effectiveness and robustness of the proposed approach under the distribution system topology changes.