基于可变形卷积与自监督对比学习的GIS局部放电诊断方法

GIS Partial Discharge Diagnosis Method Based on Deformable Convolution and Self-supervised Contrastive Learning

在高压变电站现场,气体绝缘组合电器(gas insulated switchgear,GIS)局部放电诊断准确率受到有标签样本数量的制约。为解决常规局部放电诊断方法中无法使用无标签数据、难以克服训练样本与待测试样本差异等问题,提出了一种基于可变形卷积与自监督对比学习的GIS局部放电诊断方法。首先通过比较未标注数据样本之间的相似性与差异性训练特征提取网络,得到输入数据的特征表示,之后利用有标签数据训练分类器,学习不同局放数据特征表示的缺陷类别,最后将待测试样本输入模型,实现局部放电诊断。为了进一步提高模型在特征提取过程中的感知能力,引入可变形卷积神经网络和空间变换模块,增强卷积核对特征图的适应性。结果表明:使用自监督对比学习可以充分利用无标签数据,实现高效特征捕捉,在有标签数据量不充足的情况下,通过无标签数据进行预训练的模型在局部放电诊断准确率上平均提高9.34%。该文提出的自监督对比学习方法可以为局部放电缺陷诊断提供一种新的解决方案。

In high-voltage substation,the accuracy of gas insulated switchgear(GIS)partial discharge diagnosis is restricted by the number of labeled samples.In order to solve the problem that unlabeled data cannot be used in conventional partial discharge diagnosis methods,and the difference between training samples and samples to be tested cannot be overcome,a GIS partial discharge diagnosis method based on deformable convolution and self-supervised contrastive learning is proposed in this paper.First,the feature extraction network is trained by comparing the similarity and difference between the unlabeled data samples to obtain the feature representation of the input data.Then,the classifier is trained by the labeled data to learn the defect categories represented by the features of different partial discharge data.Fi-nally,the samples to be tested are input into the model to achieve partial discharge diagnosis.In order to further improve the perception ability of the model in the process of feature extraction,a deformable convolutional neural network and a spatial transform module are introduced to enhance the adaptability of the convolutional check feature map.The results show that self-supervised contrastive learning can make full use of unlabeled data to achieve efficient feature capture.In the case of insufficient labeled data,the model pre-trained by unlabeled data can improve the PD diagnosis accuracy by 9.34%on average.The self-supervised contrastive learning method proposed in this paper can provide a new solution for the partial discharge diagnosis.