Based on PCA and SSA-LightGBM oil-immersed transformer fault diagnosis method.

ABSTRACT: A fault diagnosis method for oil immersed transformers based on principal component analysis and SSA LightGBM is proposed to address the problem of low diagnostic accuracy caused by the complexity of current oil immersed transformer faults. Firstly, data on dissolved gases in oil is collected, and a 17 dimensional fault feature matrix is constructed using the uncoded ratio method. The feature matrix is then standardized to obtain joint features. Secondly, principal component analysis is used for feature fusion to eliminate information redundancy between variables and construct fused features. Finally, a transformer diagnostic model based on SSA-LightGBM was constructed, and the ten fold cross validation method was used to verify the classification ability of the model. The experimental res

SUBMITTER: Wang J 

PROVIDER: S-EPMC11838915 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature