A Deep Learning Algorithm for Power Transformer Voiceprint Recognition in Strong-Noise and Small-Sample Scenarios

Transformer voiceprint recognition, which involves the analysis of transformer sounds to detect potential faults, has become a research hotspot in recent years. However, sound signals are susceptible to noise interference, and obtaining enough labeled transformer sound samples is challenging, thereby severely impacting the recognition accuracy. To this end, a deep learning algorithm for power transformer voiceprint recognition in strong-noise and small-sample scenarios is offered in this paper. Firstly, based on the frequency distribution characteristics of transformer body sounds, an improved spectrum method is proposed, selectively extracting transformer body frequency while suppressing environmental noise frequency. Secondly, ResVGG transformer voiceprint classification network is presented, which decouples training and inference to simultaneously enhance training accuracy and inference speed. Furthermore, a denoising network is introduced as a precursor to the classification network to further eliminate noise from the spectrum. Finally, self-supervised contrastive learning is employed, utilizing a large number of unlabeled sound samples for pretraining to enhance small-sample generalization and noise robustness. Experimental results demonstrate that the proposed method achieves a 10.6% improvement in accuracy compared to the baseline method, while also exhibiting satisfactory performance in inference time and memory usage.