用于变压器局部放电监测的柔性天线和分布式深度学习模式识别研究

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, APPLIED Journal of Physics D: Applied Physics Pub Date : 2024-09-10 DOI:10.1088/1361-6463/ad759f
Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li
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引用次数: 0

摘要

电力变压器是电力系统的重要组成部分,对局部放电的连续监测可以为变压器的故障诊断和运行维护提供更合理的方案。然而,刚性局部放电超高频天线无法与被监测设备贴合安装,且局部放电超高频信号衰减严重,导致检测能效低,增益性能无法满足需求。集中式深度学习局部放电模式识别方法训练效率低,分布式深度学习可以提高训练效率,但多源异构数据会降低模型精度。基于此,本文设计了一种小型化、宽频带、高增益、高弯曲变形稳定性的超高频柔性复合螺旋天线,并研究了一种基于残差收缩网络的联合学习模式识别方法,在保证精度的同时大幅提高了训练效率。
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Research on flexible antenna and distributed deep learning pattern recognition for partial discharge monitoring of transformer
Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.
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来源期刊
Journal of Physics D: Applied Physics
Journal of Physics D: Applied Physics 物理-物理:应用
CiteScore
6.80
自引率
8.80%
发文量
835
审稿时长
2.1 months
期刊介绍: This journal is concerned with all aspects of applied physics research, from biophysics, magnetism, plasmas and semiconductors to the structure and properties of matter.
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