全球能源互联网（英文）2023，Vol.6Issue(5)：601-613,13.DOI:10.1016/j.gloei.2023.10.007
"基于自注意力机制VAE-GAN的GIS局部放电数据增强方法"
GIS partial discharge data enhancement method based on self attention mechanism VAE-GAN
钱庆林 1孙炜昊 1王真 1路永玲 1李玉杰 1江秀臣1
作者信息
摘要
Abstract
The reliability of gas-insulated switchgear(GIS)partial discharge fault diagnosis is crucial for the safe and stable operation of power grids.This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects.First,the non-subsampled contourlet transform(NSCT)algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge(PRPD)spectrum with richer information.Subsequently,the VAE structure was introduced into the traditional GAN,and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN.Then,the self-attention mechanism was integrated into the VAE-GAN,and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state.Finally,the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples.The eigenvectors of the sets were substituted into the long short-term memory(LSTM)network for partial discharge fault diagnosis.The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method.The structure similarity index measure(SSIM)index is increased by 4.5%and 21.7%,respectively,and the average accuracy of fault diagnosis is increased by 22.9%,9%,5.7%,and 6.5%,respectively.The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.关键词
局部放电/数据增强/VAE-GAN/自注意力机制/NSCT/故障诊断Key words
Partial discharge/Data augmentation/VAE-GAN/Self-attention/NSCT/Fault diagnosis引用本文复制引用
钱庆林,孙炜昊,王真,路永玲,李玉杰,江秀臣.."基于自注意力机制VAE-GAN的GIS局部放电数据增强方法"[J].全球能源互联网（英文）,2023,6(5):601-613,13.
