太赫兹科学与电子信息学报2025，Vol.23Issue(7)：655-662,8.DOI:10.11805/TKYDA2025031
面向有源配电台区电力设备的太赫兹指纹识别学习方法
A terahertz fingerprint recognition learning method for power equipment in active distribution substations
摘要
Abstract
A terahertz fingerprint recognition and clustered Federated Learning(FL)collaboration method is proposed for anti-tampering protection of power equipment in power-supplied distribution areas,aiming to enhance the security management of massive distributed power devices within such areas.Firstly,specialized manufacturing techniques are employed to create terahertz tags for power equipment,providing a foundation for physical anti-counterfeiting.Then,a dual-branch multimodal convolutional neural network is introduced to detect abnormal tampering of device fingerprints.Finally,a clustered FL training method is designed to address data-sharing challenges caused by privacy concerns in anti-tampering recognition models,enabling distributed joint modeling and efficient collaborative training of terahertz fingerprint data across multiple devices in power-supplied distribution areas.Experimental results demonstrate that compared with traditional methods,the proposed approach improves recognition accuracy by 212%over the histogram similarity algorithm.Additionally,it significantly outperforms conventional image recognition algorithms in terms of fingerprint recognition accuracy,training efficiency,and ensuring data usability while maintaining privacy.Finally,the fingerprint recognition accuracy reaches more than 90%,which provides a novel technical pathway for security monitoring and attack prevention of power equipment.关键词
有源配电台区/太赫兹设备指纹/联邦学习(FL)/设备识别Key words
active distribution substations/terahertz device fingerprint/Federated Learning(FL)/equipment recognition分类
信息技术与安全科学引用本文复制引用
岳洋,张磐,吴磊,庞超..面向有源配电台区电力设备的太赫兹指纹识别学习方法[J].太赫兹科学与电子信息学报,2025,23(7):655-662,8.基金项目
国网天津市电力公司科技资助项目(电科-研发2023-41) （电科-研发2023-41）
