Automatic Graph Modeling of Power Transformer Management Data

Data for electrical components is maintained by different information systems in China. Currently, the relational database is commonly used to obtain and manage data for equipment maintenance purposes. However, as the modern power system develops, there is a growing need for massive data processing from different sources, which poses challenges to relational databases of its limitations in model flexibility, query efficiency, and scalability. Compared to traditional relational databases, graph databases handle fast-changing, inter-connected data well, offer flexibility, and have higher query efficiency. This paper introduces a graph modeling technique for transformer equipment to quickly acquire and maintain data for transformer equipment operation and management purposes. An initial data screening is performed to select data with different sources, structures, and characteristics. Word2vec and K-means are adapted to define and cluster model candidate sets. TextRank is applied to perform disambiguation for those transformer model sets. The transformer management graph model is then optimized based on its business scenario. The automatic resulting model provides comprehensive transformer data management, which opens transformer equipment operation and maintenance scenarios. This work significantly improves transformer automatic modeling and data management efficiency and compatibility. The results demonstrate the importance and potential of applying graph database in electrical component data management.