Diagnosis method of transformer winding mechanical deformation fault based on sliding correlation of FRA and series transfer learning

Frequency response analysis (FRA) is a common method for diagnosing transformer winding faults. There are many ways to interpret FRA, however, extracting fault features from FRA still faces challenges because the FRA signature of each transformer is different, which can make the generalization of FRA diagnosis difficult. Based on this, this paper proposes a diagnosis method based on FRA sliding correlation and series transfer learning. The study collected FRA data from three transformers, two obtained through simulation, while the third obtained through experiment. During the simulation, the synthetic minority over-sampling technique (smote) algorithm was used to expand the data set to solve the problem of unbalanced and insufficient data sets for different fault types. The FRA signature was processed by sliding correlation to extract fault features, and the FRA sequence data was transformed into a heat map with correlation coefficient values distinguished by color. Next, the AlexNet network was transferred twice using the simulation data, and the experimental data was used for the third transfer learning. The experimental results showed that the diagnosis method has high accuracy and strong generalization.