利用语义分割对架空输电线路过热故障进行智能诊断

IF 4.4 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
High Voltage Pub Date : 2024-01-10 DOI:10.1049/hve2.12403
Xiangyu Yang, Youping Tu, Zhikang Yuan, Zhong Zheng, Geng Chen, Cong Wang, Yan Xu
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引用次数: 0

摘要

应力夹和引线是连接架空输电线导体和传导电流的重要部件。在运行过程中,这些部件之间的接触不良会导致异常过热,从而引发电力故障,威胁电力系统的可靠性。最近,使用配备红外热成像仪的无人机进行应变钳和导线维护变得越来越流行。基于深度学习的图像识别技术在过热故障的智能故障诊断方面前景广阔。本文提出了一种基于动态直方图均衡化的预处理方法,以增强红外图像的对比度。比较了 DeepLab v3+ 网络、损失函数和具有不同骨干网的现有网络。添加了 ResNet101 和卷积块注意力模块的 DeepLab v3+ 网络以及 Focal 损失函数取得了最高的性能,在测试集上的平均像素精度为 0.614,平均交集大于联合(AIoU)为 0.567,F1 分数为 0.644,频率加权交集大于联合为 0.594。优化后的 Atrous 率将 AIoU 提高了 12.91%。此外,还提出了一种用于评估应变夹具和导线缺陷状态的智能诊断方案,诊断准确率达到 91.0%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Intelligent overheating fault diagnosis for overhead transmission line using semantic segmentation

Intelligent overheating fault diagnosis for overhead transmission line using semantic segmentation

The strain clamps and leading wires are important components that connect conductors on overhead transmission lines and conduct current. During operation, poor contact between these components can cause abnormal overheating, leading to electric failures and threatening power system reliability. Recently, the use of unmanned aerial vehicles equipped with infrared thermal imagers for strain clamp and leading wire maintenance has become increasingly popular. Deep learning-based image recognition shows promising prospects for intelligent fault diagnosis of overheating faults. A pre-treatment method is proposed based on dynamic histogram equalisation to enhance the contrast of infrared images. The DeepLab v3+ network, loss function, and existing networks with different backbones are compared. The DeepLab v3+ network with ResNet101 and convolutional block attention module added, and the Focal Loss function achieved the highest performance with an average pixel accuracy of 0.614, an average intersection over union (AIoU) of 0.567, an F1 score of 0.644, and a frequency weighted intersection over union of 0.594 on the test set. The optimised Atrous rates has increased the AIoU by 12.91%. Moreover, an intelligent diagnosis scheme for evaluating the defect state of the strain clamps and leading wires is proposed and which achieves a diagnostic accuracy of 91.0%.

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来源期刊
High Voltage
High Voltage Energy-Energy Engineering and Power Technology
CiteScore
9.60
自引率
27.30%
发文量
97
审稿时长
21 weeks
期刊介绍: High Voltage aims to attract original research papers and review articles. The scope covers high-voltage power engineering and high voltage applications, including experimental, computational (including simulation and modelling) and theoretical studies, which include: Electrical Insulation ● Outdoor, indoor, solid, liquid and gas insulation ● Transient voltages and overvoltage protection ● Nano-dielectrics and new insulation materials ● Condition monitoring and maintenance Discharge and plasmas, pulsed power ● Electrical discharge, plasma generation and applications ● Interactions of plasma with surfaces ● Pulsed power science and technology High-field effects ● Computation, measurements of Intensive Electromagnetic Field ● Electromagnetic compatibility ● Biomedical effects ● Environmental effects and protection High Voltage Engineering ● Design problems, testing and measuring techniques ● Equipment development and asset management ● Smart Grid, live line working ● AC/DC power electronics ● UHV power transmission Special Issues. Call for papers: Interface Charging Phenomena for Dielectric Materials - https://digital-library.theiet.org/files/HVE_CFP_ICP.pdf Emerging Materials For High Voltage Applications - https://digital-library.theiet.org/files/HVE_CFP_EMHVA.pdf
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