Switchgear Health Monitoring Based on Ripplenet and Knowledge Graph

High-voltage switchgear is an important component of the power system, and its operation safety will directly affect the reliability of the power supply of the power system. At present, the operation and maintenance decision-making of the switchgear mainly relies on manual work, which has problems such as low efficiency and poor reliability of judgment results. Therefore, this paper proposes an intelligent operation and maintenance auxiliary method for high-voltage switchgear based on the combination of the Ripplenet algorithm and knowledge graph, which ensures high efficiency while improving the reliability of the results. Among them, the knowledge graph is mainly based on the Bidirectional Encoder Representations from Transformers-Whole Word Masking (BERT-wwm) algorithm, and it is constructed in a bottom-up and top-down manner. It consists of 240 nodes and 960 relationships. Based on this knowledge graph, the intelligent operation and maintenance auxiliary method of high-voltage switchgear based on Ripplenet is studied. Based on textual information such as on-site information and fault reports, the judgment reasoning of the fault type of the high-voltage switchgear and recommendations for operation and maintenance solutions are realized. The diagnostic accuracy of this method for high-voltage switchgear faults can reach 95.96%.