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面向电力系统的多粒度隐患检测方法

徐晓华 钱平 王一达 周昕悦 徐汉麟 徐李冰

徐晓华, 钱平, 王一达, 等 . 面向电力系统的多粒度隐患检测方法[J]. 北京航空航天大学学报, 2021, 47(3): 520-530. doi: 10.13700/j.bh.1001-5965.2020.0491
引用本文: 徐晓华, 钱平, 王一达, 等 . 面向电力系统的多粒度隐患检测方法[J]. 北京航空航天大学学报, 2021, 47(3): 520-530. doi: 10.13700/j.bh.1001-5965.2020.0491
XU Xiaohua, QIAN Ping, WANG Yida, et al. Multi-granularity hazard detection method for electrical power system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 520-530. doi: 10.13700/j.bh.1001-5965.2020.0491(in Chinese)
Citation: XU Xiaohua, QIAN Ping, WANG Yida, et al. Multi-granularity hazard detection method for electrical power system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 520-530. doi: 10.13700/j.bh.1001-5965.2020.0491(in Chinese)

面向电力系统的多粒度隐患检测方法

doi: 10.13700/j.bh.1001-5965.2020.0491
基金项目: 

国网浙江省电力有限公司科技项目 5211HZ19014U

详细信息
    作者简介:

    徐晓华  男, 硕士, 高级工程师。主要研究方向: 电力信息及通信管理

    钱平  男, 硕士, 高级工程师。主要研究方向: 电力系统自动化

    王一达  男, 硕士, 高级工程师。主要研究方向: 电力系统自动化

    周昕悦  女, 硕士, 工程师。主要研究方向: 电力信息系统

    徐汉麟  男, 工程师。主要研究方向: 电力信息系统

    徐李冰  男, 工程师。主要研究方向: 网络信息技术

    通讯作者:

    王一达, E-mail: wang_yida@zj.sgcc.com.cn

  • 中图分类号: TP391

Multi-granularity hazard detection method for electrical power system

Funds: 

Science and Technology Project of State Grid Zhejiang Electric Power Company 5211HZ19014U

More Information
  • 摘要:

    由于电力系统的安全问题往往会造成严重的经济或社会影响,隐患检测已成为电力系统不可或缺的重要环节。随着人工智能领域的发展,基于深度学习的智能化电力系统隐患检测技术逐渐得到越来越多的关注。但目前的方法大多只是单一地考虑图像的全局特征或局部特征,无法全面彻底表征图像,进而难以捕捉电力领域尤其室外复杂背景下的隐患检测。为此,基于深度学习技术,提出了一种面向电力系统的多粒度隐患检测方法MGNet。通过引入图像的多粒度信息,构建全局和局部网络,进行多粒度级检测;并通过不同粒度级检测结果的协作式融合,增强检测的全面性。在杆塔连接金具隐患和线路通道机械隐患2个数据集上进行了实验比较和分析,对所提模型的检测性能进行评估。通过与现有最优隐患检测基准方法相比,所提方法在2种不同数据集上的平均精度均值分别提升了2.74%和2.77%,验证了模型的有效性。

     

  • 图 1  多粒度隐患检测网络结构

    Figure 1.  Structure of multi-granularity hazard detection network

    图 2  区域建议网络结构

    Figure 2.  Structure of region proposal network

    图 3  代价函数收敛图

    Figure 3.  Cost function convergence graph

    图 4  MGNet和YOLOv4在杆塔连接金具隐患数据集中各隐患类别表现

    Figure 4.  Performance of MGNet and YOLOv4 on different categories of tower connection fitting hazard datasets

    图 5  MGNet和YOLOv4在线路通道机械隐患数据集中各隐患类别表现

    Figure 5.  Performance of MGNet and YOLOv4 on different categories of transmission line channel mechanical hazard datasets

    图 6  MGNet及其消融衍生模型在线路通道机械隐患数据集中各隐患类别表现

    Figure 6.  Performance of MGNet and its ablation derived models on different categories of transmission line channel mechanical hazard datasets

    图 7  MGNet及其消融衍生模型在杆塔连接金具隐患数据集中各隐患类别表现

    Figure 7.  Performance of MGNet and its ablation derived models on different categories of tower connection fitting hazard datasets

    图 8  消融实验检测结果对照图(m=3)

    Figure 8.  Comparison of detection results from ablation experiment (m=3)

    图 9  图 8检测结果放大图

    Figure 9.  Magnification of detection results in Fig. 8

    图 10  切块尺度因子在两个数据集中对模型MGNet平均精度均值的影响

    Figure 10.  Effect of block scale factor on mean average precision of MGNet model in two datasets

    图 11  融合阈值在杆塔连接金具隐患数据集中对模型MGNet平均精度均值的影响

    Figure 11.  Effect of fusion threshold on mean average precision of MGNet model in tower connection fitting hazard datasets

    图 12  融合阈值在线路通道机械隐患数据集中对模型MGNet平均精度均值的影响

    Figure 12.  Effect of fusion threshold on mean average precision of MGNet model in transmission line channel mechanical hazard datasets

    表  1  符号表示

    Table  1.   Summary of main notations' representation

    符号 含义
    C 隐患标签数目
    oj j个隐患物体
    cj oj的隐患类别标签标识
    tj oj的位置表示
    图像全局特征图
    图像在第r个网格中局部特征图表示
    隐患物体的预测类别
    隐患物体的预测位置
    下载: 导出CSV

    表  2  杆塔连接金具隐患数据集规模

    Table  2.   Statistics of tower connection fitting hazard datasets

    隐患类别 隐患物体数目
    销钉缺损 6 232
    销钉安装位置错误 3 272
    下载: 导出CSV

    表  3  线路通道机械隐患数据集规模

    Table  3.   Statistics of transmission line channel mechanical hazard datasets

    隐患类别 隐患物体数目
    卡车 7 073
    推土机 7 579
    起重机 15 796
    挖掘机 18 568
    泵车 4 808
    水泥混合器 2 449
    打桩机 1 336
    下载: 导出CSV

    表  4  不同隐患检测方法在2个数据集上的平均精度均值

    Table  4.   Mean average precision of different hazard detection methods on two datasets

    模型 mAP/%
    杆塔连接金具隐患数据集 线路通道机械隐患数据集
    SSD 19.99 60.93
    Faster R-CNN 38.96 71.65
    YOLOv3 40.59 61.32
    Cascade R-CNN 41.21 68.50
    YOLOv4 44.99 72.34
    MGNet 47.73 75.11
    下载: 导出CSV

    表  5  多粒度隐患检测网络消融实验

    Table  5.   Ablation experiments of multi-granularity hazard detection network

    模型 mAP/%
    杆塔连接金具隐患数据集 线路通道机械隐患数据集
    MGNet 47.73 75.11
    MGNet_NoGlobal 46.87 41.24
    MGNet_NoLocal 38.96 71.65
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-02
  • 录用日期:  2020-09-04
  • 刊出日期:  2021-03-20

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