Fault diagnosis method for voltage transformers based on wavelet threshold filtering and graph neural networks

Voltage transformers are an important component of power systems. However, transformers work in complex environments such as high noise and strong magnetic interference for a long time, resulting in problems such as multiple types of faults, difficulty in extracting fault characteristics, and low accuracy in fault diagnosis. Based on this, this article proposes a fault diagnosis method based on adaptive local iterative filtering (ALIF)-fuzzy dispersion entropy (FDE)-adaptive wavelet threshold filtering and improved graph neural networks (GNNs). Firstly, ALIF is used to perform modal decomposition on the original signal, and FDE is employed to eliminate noise-dominant modes and extract optimal voltage sample feature vectors. Secondly, an adaptive wavelet threshold function based on FDE features is proposed to suppress residual noise. By dynamically adjusting parameters, it synchronously suppresses excessive high-frequency smoothing and pseudo-Gibbs phenomenon, achieving noise suppression and feature fidelity of the signal. Finally, a multi-layer bridged high-order GNN integrating dynamic attention mechanisms and contrastive normalization is constructed. This network enhances the ability to capture long-range dependencies in voltage signals through hierarchical feature aggregation. Experimental results show that the fault diagnosis algorithm proposed in this paper exhibits high noise robustness and diagnostic accuracy. Compared to fixed threshold functions and traditional GNNs, it improves the signal-to-noise ratio by 6.4%, reduces the mean squared error by 42.5%, increases the diagnostic accuracy by 2.61%, and enhances the F1 score by 3.60%, achieving high-precision fault diagnosis for voltage transformers.