GCN-based Local ID Mapping Scheme for Power Equipment using Topological Features

Accurate mapping of local IDs across heterogeneous power system applications is essential for data integration but remains challenging due to inconsistent naming and data models. This paper proposes a topology-based graph convolutional neural network (GCN) method for automatic local ID mapping of power equipment. The power network is modeled as a graph, and six topological features are used to represent node characteristics. A multi-layer GCN learns topology-aware node embeddings, and the Hungarian algorithm is applied to obtain optimal one-to-one correspondences based on embedding similarity. Simulation results on a 1500-node test network with loops representing symmetric structures demonstrate that the proposed method achieves over 95% accuracy and edge consistency with the help of GCN trainings. In addition, margin analysis confirms improved separability between correct and incorrect mappings, indicating high robustness. The proposed approach provides an effective and scalable solution for topology-aware ID mapping in power systems.