电力建设2024，Vol.45Issue(8)：118-127,10.DOI:10.12204/j.issn.1000-7229.2024.08.011
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
摘要
Abstract
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 fault-related 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.关键词
高压直流输电线路/格拉姆角差场(GADF)/残差网络/迁移学习/故障识别Key words
HVDC line/Gram angle difference field(GADF)/Resnet18/transfer learning/fault identification分类
信息技术与安全科学引用本文复制引用
赵妍,孙延,聂永辉..基于格拉姆角差场和迁移残差网络的HVDC线路故障识别[J].电力建设,2024,45(8):118-127,10.基金项目
This work is supported by National Natural Science Foundation of China(No.61973072) 国家自然科学基金项目(61973072) （No.61973072）
2024 年度科学技术研究项目(JJKH20240144KJ) （JJKH20240144KJ）
