Correlation analysis technique of key parameters for transformer material inspection based on FP-tree and knowledge graph

As one of the key equipment in the distribution system, the distribution transformer directly affects the reliability of the user power supply. The probability of accidents occurring in the operation of transformer equipment is high, so it has become a focus of material inspection in recent years. However, the large amount of raw data from sample testing is not being used effectively. Given the above problems, this paper aims to mine the relationship between the unqualified distribution transformer inspection items by using the association rule algorithm based on the distribution transformer inspection data collected from 2017 to 2021 and sorting out the key inspection items. At the same time, the unqualified judgment basis of the relevant items is given, and the internal relationship between the inspection items is clarified to a certain extent. Furthermore, based on material and equipment inspection reports, correlations between failed inspection items, and expert knowledge, the knowledge graph of material equipment inspection management is constructed in the graph database Neo4j. The experimental results show that the FP-Growth method performs significantly better than the Apriori method and can accurately assess the relationship between failed distribution transformer inspection items. Finally, the knowledge graph network is visualized to provide a systematic knowledge base for material inspection, which is convenient for knowledge query and management. This method can provide a scientific guidance program for operation and maintenance personnel to do equipment maintenance and also offers a reference for the state evaluation of other high-voltage equipment.