天津工业大学学报2026，Vol.45Issue(4)：62-72,79,12.DOI:10.3969/j.issn.1671-024x.2026.04.008
基于深度NCA与原型网络的MMC-HVDC故障诊断方法
Fault diagnosis method for MMC-HVDC based on Deep NCA and prototypical networks
摘要
Abstract
To address the complex fault transient process,feature overlapping in shallow feature spaces,and high real-time requirements for online diagnosis in modular multilevel converter-based flexible high voltage direct current(MMC-HVDC)systems,a fault diagnosis method based on deep neighborhood components analysis(Deep NCA)and prototypical networks is proposed.First,a one-dimensional convolutional neural network is used to adaptively extract deep features from multi-channel transient sequences,including arm current,submodule ca-pacitor voltage,and grounding current.Second,Deep NCA is introduced to impose supervised metric constraints on the embedding space,enhancing intra-class compactness and inter-class separability.Finally,fast distance-based classification is achieved through class prototypes.The simulation results show that the proposed method achieves an accuracy of 95.20%for five operating states,outperforming typical deep learning models including 1D-CNN+Softmax,1D-ResNet and 1D-Transformer.Under load-shift conditions,the average diagnostic accu-racy is 91.73%,and an accuracy of 86.93%is retained when only 50 training samples per class are used.Results demonstrates that the proposed method possesses favorable adaptability to varying operating conditions and the capability to learn from limited training samples.关键词
柔性直流(HVDC)输电/模块化多电平换流器(MMC)/故障诊断/深度邻域成分分析(Deep NCA)/原型网络/度量学习Key words
flexible high voltage direct current(HVDC)transmission/modular multilevel converter(MMC)/fault diagno-sis/deep neighborhood components analysis(Deep NCA)/prototypical network/metric learning分类
信息技术与安全科学引用本文复制引用
黄志光,连攀杰,骆玮,刘嘉宸,时艳强,刘伟成..基于深度NCA与原型网络的MMC-HVDC故障诊断方法[J].天津工业大学学报,2026,45(4):62-72,79,12.基金项目
国家电网有限公司华东分部科技项目(52992425001L) （52992425001L）
国家自然科学基金项目(52407079) （52407079）
湖南省自然科学基金项目(2025JJ60372) （2025JJ60372）
