Intelligent parameter recommendation for substation design using knowledge graph and graph neural networks

As power systems move toward digitalization and low-carbon transformation, improving the intelligence of substation design processes has become increasingly critical. Traditional equipment parameter selection relies heavily on manual experience and fragmented document retrieval, leading to inefficiencies, inconsistencies, and limited scalability. This paper proposes an intelligent parameter recommendation method tailored for substation engineering, integrating domain-specific knowledge graphs with adaptive graph neural networks (GNNs). The framework first extracts structured equipment information from multi-voltage substation design drawings using entity disambiguation, then constructs a hierarchical knowledge graph to represent inter-device relationships. A natural language interface captures user queries and encodes them into context-aware instruction vectors. These are used to guide a hybrid reasoning process that combines fuzzy rule matching and GNN-based relation inference. Case studies using real-world 10 kV/110 kV substation projects demonstrate that the proposed method significantly outperforms existing knowledge graph-based baselines in both accuracy and interpretability. The results show that this work is superior to the comparison baseline model in both ACC and AUC indicators, and support intelligent decision-making throughout the equipment lifecycle. This work provides a scalable solution for knowledge-driven substation design automation in the era of smart grids.