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

doi:10.13700/j.bh.1001-5965.2020.0491

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

1.
国网浙江省电力有限公司杭州供电公司, 杭州 310020
2.
国网浙江省电力有限公司, 杭州 310007

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
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- 被引次数: 0
出版历程
- 收稿日期: 2020-09-02
- 录用日期: 2020-09-04
- 网络出版日期: 2021-03-20

Multi-granularity hazard detection method for electrical power system

1.
State Grid Zhejiang Electric Power Company Hangzhou Power Supply Company, Hangzhou 310020, China
2.
State Grid Zhejiang Electric Power Company, Hangzhou 310007, China

Funds:

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

More Information

Corresponding author: WANG Yida, E-mail: wang_yida@zj.sgcc.com.cn

摘要

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。
- 隐患检测 /
- 多粒度信息 /
- 协作式融合 /
- 深度学习 /
- 电力系统
Abstract:
As the security hazard of the electrical power system can lead to serious economic damage and social impacts, the potential hazard detection has become an indispensable part for electrical power system. With the advances of artificial intelligence, intelligent deep learning based hazard detection methods for electrical power system have emerged. Although the existing methods have made promising progress, most of them only consider the global or local features of the image, which cannot thoroughly characterize the imageand accurately conduct the hazard detection in the context of the electrical power system especially for the complex outdoor background. In the light of this, in this paper, we present a multi-granularity hazard detection network MGNet for the electrical power system. To be specific, we explore the multi-granularity representation of images with both the global and local representation learning networks. Based on that, we conduct the hazard detection at different granularity levels and finally collaboratively fuse the detection results to fulfill the precise hazard detection. Extensive experiments on two real-world datasets of hazard(i.e., tower connection fitting hazard dataset and transmission line channel mechanical hazard dataset) demonstrate the superiority of the detection performance of the proposed model. In particular, the mean average precision is improved by 2.74% and 2.77% on two datasets, respectively, compared with the existing optimal hazard detection benchmark method.
- hazard detection /
- multi-granularity /
- collaborative fusion /
- deep learning /
- electrical power system

HTML全文

图 1 多粒度隐患检测网络结构

Figure 1. Structure of multi-granularity hazard detection network

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图 2 区域建议网络结构

Figure 2. Structure of region proposal network

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图 3 代价函数收敛图

Figure 3. Cost function convergence graph

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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

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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

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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

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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

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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)

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图 9 图 8检测结果放大图

Figure 9. Magnification of detection results in Fig. 8

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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

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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

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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

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表 1 符号表示

Table 1. Summary of main notations' representation

符号	含义
C	隐患标签数目
o_j	第j个隐患物体
c_j	o_j的隐患类别标签标识
t_j	o_j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

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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

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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

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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/%
模型	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

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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/%
模型	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

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