湖南电力 ›› 2026, Vol. 46 ›› Issue (3): 108-113.doi: 10.3969/j.issn.1008-0198.2026.03.014

• 电力人工智能与数字化 • 上一篇    下一篇

基于MobileNetV3轻量化网络架构的超高压六分裂导线X射线探伤技术

马小军1, 李波2, 刘家瑞1   

  1. 1.宁夏超高压电力工程有限公司,宁夏 银川 750011;
    2.国网宁夏电力有限公司超高压公司,宁夏 银川 750011
  • 收稿日期:2025-11-28 修回日期:2026-01-29 出版日期:2026-06-25 发布日期:2026-07-07
  • 通信作者: 马小军(1992),硕士,工程师,研究方向为输电运维、电力无人机智能巡检、交直流变电运维等。
  • 作者简介:李波(1989),本科,高级技师,研究方向为电力无人机智能巡检。刘家瑞(1993),本科,工程师,研究方向为输电运维、交直流变电运维等。
  • 基金资助:
    国网宁夏电力有限公司科技项目(522979250003)

X-Ray Detection Technology for EHV Six-Bundle Con‍ductor Based on MobileNetV3 Lightweight Network Architecture

MA Xiaojun1, LI Bo2, LIU Jiarui1   

  1. 1. Ningxia Ultra High Voltage Power Engineering Co., Ltd., Yinchuan 750011, China;
    2. State Grid Ningxia Electric Power Company Limited UHV Company, Yinchuan 750011, China
  • Received:2025-11-28 Revised:2026-01-29 Online:2026-06-25 Published:2026-07-07

摘要: 针对六分裂导线中多类缺陷共存、信号相互重叠所产生的干扰及其导致的探伤检测结果偏差现象,提出一种基于MobileNetV3轻量化网络架构的超高压六分裂导线X射线探伤技术。首先,利用X射线穿过线夹时不同区域的衰减差异,并结合朗伯-比尔定律与多角度电信号重建,采集耐张线夹内部X射线图像。然后,采用深度可分离卷积和轻量级通道注意力机制强化方法,提升对关键缺陷特征的捕获能力。最后,通过自适应预选框生成与分类-回归网络实现缺陷的定位与识别,利用联合向量与全连接层优化输出结果。实验结果表明,该方法探伤准确率达99.6%,与传统人工巡检相比,单次检测时间从120 min以上缩短至60 min以内,能有效识别断裂和散股的多缺陷并存场景,对输电线路的安全稳定运行具有重要实践意义。

关键词: 六分裂导线, 耐张线夹, MobileNetV3, 朗伯-比尔定律, X射线图像

Abstract: Aiming at the deviation phenomenon of flaw detection results caused by the coexistence and mutual interference of multiple types of defects in six-bundle conductor, an X-ray flaw detection technology for ultra-high voltage six-bundle conductor based on MobileNetV3 lightweight network architecture is proposed. Firstly, the X-ray attenuation difference in different areas when X-ray passes through the clamp is used, and the internal X-ray image of the tension clamp is collected by combining Lambert-Beer law and multi-angle electrical signal reconstruction. Then, the depthwise separable convolution and lightweight channel attention mechanism enhancement methods are used to improve the capture ability of key defect features. Finally, the defect location and recognition are realized by adaptive pre selection box generation and classification regression network, and the output results are optimized by using joint vector and full connection layer. The experimental results show that the detection accuracy of this method is 99.6%. Compared with the traditional manual inspection, the single detection time is shortened from more than 120 min to less than 60 min, which can effectively identify the scene of multiple defects coexisting of fracture and loose strands, and has important practical significance for the safe and stable operation of transmission lines.

Key words: six-boundle conductor, tension clamp, MobileNetV3, Lambert-Beer, X-ray image

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