Knowledge Graph and ST-GCN Based Intelligent Framework for Proactive Outage Detection and Service Classification in Distribution Networks
The rapid proliferation of distributed energy resources and the increasing complexity of modern distribution networks have imposed unprecedented challenges on power supply service command systems. Traditional approaches suffer from data fragmentation across heterogeneous business systems, shallow semantic understanding of customer complaints, and delayed outage perception. This paper proposes an integrated intelligent framework that addresses these challenges through three synergistic modules: (1) a lightweight multi-source heterogeneous data fusion engine that constructs a multi-level distribution network Knowledge Graph (KG) spanning User-Meter Box-Branch-Transformer Area-Feeder hierarchies; (2) a fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based natural language processing module for automatic tri-level (Red/Orange/Yellow) sensitivity classification of power service work orders; and (3) a Spatio-Temporal Graph Convolutional Network (ST-GCN) that leverages the KG topology as spatial adjacency for proactive outage detection at the meter-box level. Experimental results on simulated datasets demonstrate that the proposed framework achieves 94.7% F1-score in work order classification and reduces outage detection latency from tens-of-minutes level to minute-level, with 91.3% F1-score in proactive outage detection. The framework provides a viable pathway toward data-intelligent transformation of power supply service command operations.