A Multi-Source Embedding-Based Named Entity Recognition Model for Knowledge Graph and Its Application to On-Site Operation Violations in Power Grid Systems
Lingwen Meng, Yulin Wang, Guobang Ban, Yuanjun Huang, Xinshan Zhu, Shumei Zhang · Electronics · 2025
With the increasing complexity of power grid field operations, frequent operational violations have emerged as a major concern in the domain of power grid field operation safety. To support dispatchers in accurately identifying and addressing violation risks, this paper introduces a profiling approach for power grid field operation violations based on knowledge graph techniques. The method enables deep modeling and structured representation of violation behaviors. In the structured data processing phase, statistical analysis is conducted based on predefined rules, and mutual information is employed to quantify the contribution of various operational factors to violations. At the municipal bureau level, statistical modeling of violation characteristics is performed to support regional risk assessment. For unstructured textual data, a multi-source embedding-based named entity recognition (NER) model is developed, incorporating domain-specific power lexicon information to enhance the extraction of key entities. High-weight domain terms related to violations are further identified using the TF-IDF algorithm to characterize typical violation behaviors. Based on the extracted entities and relationships, a knowledge graph of field operation violations is constructed, providing a computable and inferable semantic representation of operational scenarios. Finally, visualization techniques are applied to present the structural patterns and distributional features of violations, offering graph-based support for violation risk analysis and dispatch decision-making. Experimental results demonstrate that the proposed method effectively identifies critical features of violation behaviors and provides a structured foundation for intelligent decision support in power grid operation management.