Transformer Vibration Analysis Based on Double Branch Convolutional Neural Network

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The power transformer is one of the important pieces of equipment in the power grid system, and its normal operation is related to the safety and reliability of the whole power system. There are many factors influencing transformer vibration in operation, and its characteristics are complex, so it is difficult to be directly used for transformer state analysis. This paper proposes a method for vibration signal analysis based on a continuous wavelet time-frequency graph. The segmented samples of transformer vibration signals are selected by the time-domain sample segmentation method, and the segmented time sequence samples are transformed by continuous wavelet transform to obtain a two-dimensional time-frequency graph. The time-frequency graph is input into the two-branch convolutional neural network, and the transformer state classification is given based on the features extracted from the network. The simulation analysis on transformer vibration data measured by multiple measuring points shows that the proposed method has an average recognition accuracy of 98.3%. The work in this paper can provide a reference for the vibration analysis of the transformer.