Construction and application of distribution network fault disposition knowledge graph based on multi-source data

Abstract

As digital grid development in Guangdong Province advances, the accumulation of multi-source heterogeneous data and the low efficiency of fault disposition in distribution networks have become increasingly prominent issues. To facilitate the effective use of multi-source heterogeneous data and efficient decision-making for distribution network fault disposition, this paper proposes a method for constructing a fault disposition knowledge graph based on multi-source heterogeneous data. First, the characteristics of multi-source heterogeneous data in distribution networks were analyzed, and a framework for constructing a fault disposition knowledge graph was designed accordingly. Then, named entities were extracted from unstructured fault texts using EBERT-BiLSTM-CRF. Based on the recognized entities, BiLSTM-GA was used to identify semantic relations and generate knowledge triples. Finally, fault data from a distribution network in a specific region of Guangdong Province were used in a case study, and the resulting knowledge graph was visualized and managed using the GridGraph graph database. Within the proposed knowledge graph construction framework, the complete BiLSTM-CRF-GA extraction configuration achieved a mean precision of 91.16%, a mean recall of 92.94%, and a mean F1-score of 92.06% across five matched data splits, supporting its effectiveness in extracting fault-disposition knowledge from multi-source data.

Keywords

distribution network fault disposition / knowledge graph / multi-source heterogeneous data / knowledge extraction / neural network

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