Research on Blind Source Separation of Transformer Vibration Signal Based on Full Convolution Time Domain Audio Separation Network

Abstract

The vibration signal on the surface of the transformer tank is formed by mixing the winding and the iron core. Blind source separation of vibration signals can diagnose the operating status of windings and iron cores separately, and improve the accuracy of using vibration signals to diagnose transformers. However, due to the high correlation between the vibration signals of the winding and the iron core, traditional blind source separation algorithms such as FastICA based on the assumption of signal independence have certain limitations. In this paper, a fully convolutional time-domain audio separation network (ConvTasNet) based on a deep learning model is used to blind source separation of transformer vibration signals. The training set and verification set are used to iterate the network. The training is completed when the network loss value is iterated to the specified requirements. This will realize the separation of the vibration signal of the winding and the iron core. This paper has carried out simulation experiments to verify the algorithm. Compared with the traditional FastICA method, the separation effect has been improved. It can more accurately separate the vibration signals of different sources, and the fault diagnosis of transformer windings and iron cores based on vibration signals Promotion and application are of great significance.