Open AccessResearch on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm
Author(s) -
Mingyu Wang,
Rui Cheng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012008
Subject(s) - dbscan , cluster analysis , computer science , data mining , transformer , outlier , grid , discretization , pattern recognition (psychology) , artificial intelligence , cure data clustering algorithm , correlation clustering , engineering , mathematics , electrical engineering , mathematical analysis , geometry , voltage
With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.