Power Transformer Winding Fault Diagnosis Method Based on Time–Frequency Diffusion Model and ConvNeXt-1D

To address the challenges of insufficient transformer winding fault samples and the effective fusion of heterogeneous multi-source data, this study proposes an intelligent fault diagnosis method based on a time–frequency diffusion model and ConvNeXt-1D. First, data augmentation is performed on the original signals using the time–frequency diffusion model. Through a forward noise injection and reverse denoising process, the limited time-series samples are expanded. By alternately applying time-domain noise addition and frequency-domain blurring, the signals are jointly enhanced in the time–frequency domain, improving sample diversity and feature representation. Next, a ConvNeXt-1D network is constructed for multi-scale feature extraction and fault classification, incorporating an attention mechanism to efficiently fuse multi-source features and achieve precise fault identification. Finally, the proposed method is validated using dynamic model experiments. The results indicate that under typical fault conditions—such as inter-turn short circuits, winding deformation, and arc discharge—the proposed method achieves a diagnostic accuracy of 99.23 ± 0.29%. Compared with other classical models, the proposed approach demonstrates stronger classification capability and higher stability under small-sample data conditions.