Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Abstract

Most of the power transformer fault diagnostic researches so far focuses on its fault type diagnosis, but there are less related researches on fault positioning, and the diagnostic methods of which are still less intelligent. This paper proposes a two-dimensional Hilbert ID considering multi-window feature extraction for deep vision fault positioning of the transformer winding. Firstly, sweep frequency response data containing complex fault characteristics is obtained based on pspice simulation. Next, a multi-window feature extraction method with logarithmic constraints is introduced to process the original data to obtain feature sequences. Then the proposed Hilbert visualization is used to further highlight the graphic feature of the feature sequences, and obtain Hilbert ID (MAPE) dataset. Finally, it is used to conduct transfer learning on the convolutional neural network. Different intelligent positioning methods are compared, and the proposed deep vision fault positioning method is 6.51% higher than other methods on average. What's more, the positioning effects based on different data processing methods are also compared. The accuracy of the proposed Hibert ID (MAPE) dataset is 10.35% higher than the other data processing methods on average. Finally, the positioning accuracy of Hilbert ID (MAPE+CC) combining two feature sequences can reach 96.09%, having an increase of 2.50%.INDEX TERMS Convolutional neural network (CNN), deep transfer learning (DTL), fault positioning, Hilbert visualization, multi-window feature extraction, power transformer, sweep frequency response analysis (SFRA).