基于格拉姆角差场和迁移残差网络的HVDC线路故障识别

HVDC Line Fault Identification Based on the Gram Angle Difference Field and Transfer Residual Network

为了提高高压直流(high voltage direct current,HVDC)输电线路在样本数量不足和高阻抗条件下的识别准确率,提出了一种基于格拉姆角差场(Gramian angular difference field,GADF)和迁移残差网络(ResNet18)结合的高压直流输电线路故障识别方法。首先利用格拉姆角差场将一维时序信号转化为二维角差场图,然后将在源域ImageNet-1K数据集上训练好的ResNet18模型的权重参数迁移至以角场图为目标域的ResNet18模型中,自适应提取故障相关特征,进行故障类型识别。实验结果证明:相较于其他深度学习方法,所提方法在小样本条件下能够正确识别区内正极接地故障、区内负极接地故障、区内双极短路故障和区外故障,识别准确率达到99.67%,并且具有较强的耐受过渡电阻能力、抗噪性和泛化性。

To improve the identification accuracy of high-voltage direct current(HVDC)transmission line faults under conditions of limited sample size and high impedance,a fault identification method for high-voltage direct current transmission lines that combines the gram angle difference field(GADF)and transfer learning using Residual Network 18(ResNet18-TL)is proposed.First,one-dimensional time-domain signals were transformed into two-dimensional angle-difference field maps using GADF.Subsequently,the weight parameters of a ResNet18 model pre-trained on the source domain ImageNet-1K dataset were transferred to a ResNet18 model with angle-field maps as the target domain,enabling the adaptive extraction of faultrelated features for fault-type recognition.Experimental results demonstrate that,compared with other deep learning methods,the proposed approach can correctly identify internal positive-polarity ground faults,internal negative-polarity ground faults,internal bipolar short-circuit faults,and external faults under small-sample conditions,achieving an accuracy of 99.67%.Additionally,it exhibits a strong tolerance to transient resistance,noise resistance,and generalization capabilities.