一种绝缘子并联间隙解耦分层故障检测方法:结合轻量定位和基于注意力的诊断

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Shuai Hao;Tianqi Li;Xu Ma;Shiao Fan;Tianrui Qi
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引用次数: 0

摘要

绝缘子并联间隙作为高压输电线路中重要的防雷元件,容易出现短路、间距过大等故障,影响线路安全和供电可靠性。然而,从无人机捕获的图像中检测绝缘子平行间隙受到复杂背景、结构畸变和遮挡的挑战。为此,提出了一种结合轻量定位和基于注意机制诊断的分层检测方法(KLSD-HDet),该方法利用定位网络捕获故障并进行故障诊断。首先,为了准确捕获目标物体,设计了一种轻量级故障定位网络(KDeFus-LNet),增强了复杂背景下的非线性特征提取(NFE)和目标定位能力;其次,利用三维故障模型的空间几何信息,开发了一种多视图故障图像生成方法,以弥补现实世界数据集中部分视点特征表示的缺失。然后,提出了一种基于跨空间学习和多尺度残差的故障诊断网络(S-CR2-DNet),以提高对多视图故障表示的理解和诊断精度。最后,将知识精馏应用到轻量级S-CR2-DNet中,增强了其实用性。大量实验验证了KLSD-HDet优于最先进的(SOTA)方法,达到94.49%的检测精度,比SOTA算法提高了6.65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Decoupled Hierarchical Fault Detection Method for Insulator Parallel Gaps: Integrating Lightweight Localization and Attention-Based Diagnosis
Insulator parallel gaps, as critical lightning protection components in high-voltage transmission lines, are prone to faults, such as short circuits and excessive spacing, which compromise line safety and power supply reliability. However, detecting insulator parallel gaps from unmanned aerial vehicle (UAV) captured images is challenged by complex backgrounds, structural distortion, and occlusion. Thus, a hierarchical detection method incorporating lightweight localization and attention mechanism-based diagnosis (KLSD-HDet) is proposed, which uses a localization network to capture the faults and then conducts fault diagnosis. First, to accurately capture target objects, a lightweight fault localization network (KDeFus-LNet) is designed, with nonlinear feature extraction (NFE) and target localization capabilities in complex backgrounds enhanced. Second, leveraging the spatial geometric information from 3-D fault models, a multiview fault image generation method is developed to compensate for the missing feature representations of partial viewpoints in real-world datasets. Then, a cross-space learning and multiscale residual-based fault diagnosis network (S-CR2-DNet) is proposed to improve multiview fault representation understanding and diagnostic accuracy. Finally, knowledge distillation is employed to lightweight S-CR2-DNet, enhancing its practical applicability. Extensive experiments validate that KLSD-HDet outperforms the state-of-the-art (SOTA) methods, achieving 94.49% detection precision, improved by 6.65% compared to the SOTA algorithms.
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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