Open Access

ARTICLE

Defect Identification Method of Power Grid Secondary Equipment Based on Coordination of Knowledge Graph and Bayesian Network Fusion

Jun Xiong^*, Peng Yang, Bohan Chen, Zeming Chen

State Grid Southwest Branch Dispatch Control Center, Chengdu, 610000, China

* Corresponding Author: Jun Xiong. Email:

Energy Engineering 2026, 123(1), . https://doi.org/10.32604/ee.2025.069438

Received 23 June 2025; Accepted 04 September 2025; Issue published 27 December 2025

Abstract

The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system. However, various defects could be produced in the secondary equipment during long-term operation. The complex relationship between the defect phenomenon and multi-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods, which limits the real-time and accuracy of defect identification. Therefore, a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed. The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology. The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework, combined with historical defect data, and introduced Noisy-OR nodes. The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment. Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph, and defect inference based on the Bayesian network. Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes, improving identification accuracy and efficiency.

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Keywords

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