Identification model of distribution equipment insulation aging enhancement based on SCADA knowledge graph

With the continuous advancement of scientific and technological integration in power facilities, higher requirements have been raised for identifying the insulation aging state of distribution equipment. At present, Supervisory Control and Data Acquisition (SCADA) systems face bottlenecks due to the limited information dimensions of single-sensor data and the heavy computational burden of complex models, which restrict their deployment and application in practical scenarios. To address these challenges, a multimodal data fusion framework is introduce and collaborative analysis and feature extraction are performed on monitoring signals from different physical characteristics. Furthermore, a lightweight Knowledge Graph-Enhanced Dynamic Graph Neural Network (KGE-DGNN) is innovatively proposed by integrating an adaptive feature weighting module. This model can autonomously enhance the contribution of key modalities while maintaining efficient computational logic, significantly reducing resource consumption and improving the overall performance of insulation aging identification. Experimental results demonstrate that the recognition accuracy reaches 98.5%, which is 8% higher than that of the baseline method. The computational efficiency achieves an average single recognition time of 120 ms. Moreover, the peak memory occupancy remains below 350 MB, which fully validates its application potential in real-time diagnostic scenarios and considerably improves the balance between accuracy and efficiency. Thus, the proposed method provides a novel and reliable intelligent diagnosis tool for early fault warning in distribution equipment. Its technical approach holds great value in promoting the development of condition-based maintenance toward precision and intelligence.