Research on Intelligent Diagnosis of DC Magnetic Bias of Power Transformers Based on Vibration Signals and Improved 2DWT-CNN-Transformer Framework

DC bias will cause the magnetization working point of the transformer core to shift and cause local saturation, and generate abnormal vibration through the magnetostrictive effect, which threatens the safe operation of the transformer. Aiming at the problem that the time–frequency characteristics of transformer vibration signals under DC bias are complex and the adjacent bias levels are difficult to distinguish, this paper proposes a 2DWT-CNN-Transformer diagnostic method that combines two-dimensional discrete wavelet transform, a convolutional neural network, and Transformer Encoder. Firstly, the multi-physical-field finite element model of three-phase three-column transformer is established, and the L0–L5 six-class DC bias dataset is constructed. Secondly, the one-dimensional vibration signal is reconstructed into a two-dimensional matrix, and the multi-subband time–frequency features of LL, LH, HL, and HH are extracted by two-dimensional discrete wavelet transform. The local texture features are extracted by the CNN, and the multi-head self-attention mechanism of Transformer Encoder is introduced to establish the global dependence and enhance the discrimination ability of adjacent bias levels. Compared with the traditional time–frequency-feature deep learning model, the proposed method achieves higher accuracy, especially in the high-noise environment of 15 dB, where it can still maintain accuracy of 96.23%. The visualization results further show that the model can form a more compact intra-class aggregation and a clearer inter-class boundary. This also provides an effective solution for the identification and evaluation of transformer DC bias states based on vibration signals in complex environments in the future.