基于自关注机制的GIS局部放电数据增强方法

IF 1.9 Q4 ENERGY & FUELS Global Energy Interconnection Pub Date : 2023-10-01 DOI:10.1016/j.gloei.2023.10.007
Qinglin Qian , Weihao Sun , Zhen Wang , Yongling Lu , Yujie Li , Xiuchen Jiang
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引用次数: 0

摘要

地理信息系统局部放电故障诊断的可靠性对电网的安全稳定运行至关重要。本研究提出了一种基于自注意机制的数据增强方法,以优化VAE-GAN方法,解决局部放电样本不足和不同缺陷之间分布不平衡的问题。首先,使用非二次采样轮廓变换(NSCT)算法对超高频和光学局部放电信号进行融合,获得信息更丰富的光电融合相位分辨局部放电(PRPD)光谱。随后,将VAE结构引入到传统的GAN中,并利用VAE优异的隐层特征提取能力来指导GAN的生成。然后,将自注意机制集成到VAE-GAN中,并使用Wasserstein距离和梯度惩罚机制来优化网络损失函数,并将样本集扩展到平衡状态。最后,使用KAZE和极坐标分布熵方法提取扩展样本。将集合的特征向量代入长短期记忆(LSTM)网络进行局部放电故障诊断。实验结果表明,该方法的样本生成质量和故障诊断结果明显优于传统的数据增强方法。结构相似性指数测度(SSIM)指数分别提高了4.5%和21.7%,故障诊断的平均准确率分别提高了22.9%、9%、5.7%和6.5%。本研究提出的数据增强方法可为GIS局部放电故障诊断提供参考。
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GIS partial discharge data enhancement method based on self attention mechanism VAE-GAN

The reliability of geographic information system (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.

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来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
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