融合多维度特征的绝缘子状态边缘识别方法

doi:10.11930/j.issn.1004-9649.202011120

中国电力 ›› 2022, Vol. 55 ›› Issue (1): 133-141.DOI: 10.11930/j.issn.1004-9649.202011120

• 人工智能在新型电力系统中的应用 • 上一篇下一篇

融合多维度特征的绝缘子状态边缘识别方法

黄冬梅¹, 王玥琦², 胡安铎¹, 孙锦中¹, 时帅², 孙园³, 房岭锋⁴

1. 上海电力大学电子与信息工程学院, 上海 201306;
2. 上海电力大学电气工程学院, 上海 200090;
3. 上海电力大学数理学院, 上海 201306;
4. 国家电网有限公司, 北京 100031

收稿日期:2020-11-30 修回日期:2021-03-17 出版日期:2022-01-28 发布日期:2022-01-20
作者简介:黄冬梅(1964-),女,博士,教授,博士生导师,从事海洋与电力时空信息技术研究,E-mail:dmhuang_dl@163.com;王玥琦(1997-),女,硕士研究生,从事智能电力巡检、绝缘子状态识别研究,E-mail:w18521019258@163.com;孙锦中(1981-),男,博士,通信作者,从事模式识别、边缘计算研究,E-mail:benima2001@sina.com
基金资助:
国家自然科学基金资助项目(41671431)；上海市科委地方院校能力建设项目(20020500700)

An Edge Recognition Method for Insulator State Based on Multi-dimension Feature Fusion

HUANG Dongmei¹, WANG Yueqi², HU Anduo¹, SUN Jinzhong¹, SHI Shuai², SUN Yuan³, FANG Lingfeng⁴

1. College of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China;
2. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
3. College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China;
4. State Grid Corporation of China, Beijing 100031, China

Received:2020-11-30 Revised:2021-03-17 Online:2022-01-28 Published:2022-01-20
Supported by:
This work is supported by National Natural Science Foundation of China (No.41671431), Local College Capacity Building Project of Shanghai Municipal Science and Technology Commission (No.20020500700)

摘要/Abstract

摘要： 针对传统的绝缘子状态识别方法存在实时性差、特征提取能力不足的问题，基于边缘计算的思想，提出了一种融合多维度特征的绝缘子状态边缘识别方法。利用云边协同和边边联邦协同的联合技术手段，构建了绝缘子状态的边缘识别框架。设计了一种融合多维度特征提取的深度学习网络，该网络采用ResNet101作为主干特征提取网络，使用Inception模块构建数据池化层，嵌入压缩激励模块和卷积注意力模块，从不同维度对特征进行高效提取。采用包括正常和缺陷2种状态的数据集进行绝缘子状态边缘识别实验，平均识别准确率达到了99%。实验表明了融合多维度特征的绝缘子状态边缘识别方法的有效性。

关键词: 绝缘子图像, 特征提取, 残差神经网络, 边缘计算, 状态识别

Abstract: Traditional insulator state recognition methods have such problems as poor real-time performance and insufficient feature extraction ability. Based on the idea of edge computing, this paper proposes a method for recognizing the insulator state based on multi-dimension feature fusion. An edge recognition framework for insulator state is constructed using cloud edge collaboration and edge federation collaboration. And a deep learning network integrating multi-dimension feature extraction is designed, which, by using the ResNet101 as the main feature extraction network, uses the Inception module to build the data pooling layer, and embeds the compression incentive module and convolution attention module to extract features from different dimensions. An insulator state recognition experiment is conducted using the data set of normal and defect states, and the average recognition accuracy reaches 99%. The experimental results have proved the validity of the proposed method.

Key words: insulator image, feature extraction, residual neural network, edge computing, state recognition

黄冬梅, 王玥琦, 胡安铎, 孙锦中, 时帅, 孙园, 房岭锋. 融合多维度特征的绝缘子状态边缘识别方法[J]. 中国电力, 2022, 55(1): 133-141.

HUANG Dongmei, WANG Yueqi, HU Anduo, SUN Jinzhong, SHI Shuai, SUN Yuan, FANG Lingfeng. An Edge Recognition Method for Insulator State Based on Multi-dimension Feature Fusion[J]. Electric Power, 2022, 55(1): 133-141.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202011120

https://www.electricpower.com.cn/CN/Y2022/V55/I1/133

参考文献

[1] 刘逸凡, 王淑青, 庆毅辉, 等. 基于EfficientDet和双目摄像头的绝缘子缺陷检测[J]. 中国电力, 2021, 54(2): 156–163, 196
LIU Yifan, WANG Shuqing, QING Yihui, et al. Insulator defect detection based on EfficientDet and binocular camera[J]. Electric Power, 2021, 54(2): 156–163, 196
[2] 李振宇, 郭锐, 赖秋频, 等. 基于计算机视觉的架空输电线路机器人巡检技术综述[J]. 中国电力, 2018, 51(11): 139–146
LI Zhenyu, GUO Rui, LAI Qiupin, et al. Survey of inspection technology of overhead transmission line robot based on computer vision[J]. Electric Power, 2018, 51(11): 139–146
[3] 白洁音, 赵瑞, 谷丰强, 等. 多目标检测和故障识别图像处理方法[J]. 高电压技术, 2019, 45(11): 3504–3511
BAI Jieyin, ZHAO Rui, GU Fengqiang, et al. Multi-target detection and fault recognition image processing method[J]. High Voltage Engineering, 2019, 45(11): 3504–3511
[4] 黄郑, 王永强, 王红星, 等. 基于云雾边异构协同的无人机智慧巡检系统[J]. 中国电力, 2020, 53(4): 161–168
HUANG Zheng, WANG Yongqiang, WANG Hongxing, et al. Design and application of UAV intelligent inspection system for transmission lines based on cloud and fog-edge heterogeneous collaborative computing architecture[J]. Electric Power, 2020, 53(4): 161–168
[5] 杨焰飞, 曹阳. 基于深度学习的无人机拍摄图像绝缘子目标检测[J]. 激光杂志, 2020, 41(10): 63–66
YANG Yanfei, CAO Yang. Image insulator target detection based on deep learning for UAV[J]. Laser Journal, 2020, 41(10): 63–66
[6] 李雪峰, 刘海莹, 刘高华, 等.基于深度学习的输电线路销钉缺陷检测[J/OL].电网技术: 1-9[2021-03-09]. https://doi.org/10.13335/j.1000-3673.pst.2020.1028.
LI Xuefeng, LIU Haiying, LIU Gaohua, et al. Transmission line pin defect detection based on deep learning [J/OL]. Power System Technology: 1-9[2021-03-09]. https://doi.org/10.13335/j.1000-3673.pst.2020.1028.
[7] 姚春羽, 金立军, 闫书佳. 电网巡检图像中绝缘子的识别[J]. 系统仿真学报, 2012, 24(9): 1818–1822
YAO Chunyu, JIN Lijun, YAN Shujia. Recognition of insulator string in power grid patrol images[J]. Journal of System Simulation, 2012, 24(9): 1818–1822
[8] 王银立, 闫斌. 基于视觉的绝缘子“掉串”缺陷的检测与定位[J]. 计算机工程与设计, 2014, 35(2): 583–587
WANG Yinli, YAN Bin. Vision based detection and location for cracked insulator[J]. Computer Engineering and Design, 2014, 35(2): 583–587
[9] 高强, 孟格格. 基于卷积神经网络的绝缘子故障识别算法研究[J]. 电测与仪表, 2017, 54(21): 30–36
GAO Qiang, MENG Gege. Research of a faulted insulator identification algorithm based on convolution neural network[J]. Electrical Measurement & Instrumentation, 2017, 54(21): 30–36
[10] 潘哲, 张兴忠, 杨罡, 等. 弱监督细粒度分类在绝缘子故障识别中的应用[J]. 山西大学学报(自然科学版), 2020, 43(3): 490–498
PAN Zhe, ZHANG Xingzhong, YANG Gang, et al. Application of weakly supervised fine-grained classification in insulator fault identification[J]. Journal of Shanxi University (Natural Science Edition), 2020, 43(3): 490–498
[11] JIANG H, QIU X J, CHEN J, et al. Insulator fault detection in aerial images based on ensemble learning with multi-level perception[J]. IEEE Access, 2019, 7: 61797–61810.
[12] 马富齐, 王波, 董旭柱, 等. 电力视觉边缘智能: 边缘计算驱动下的电力深度视觉加速技术[J]. 电网技术, 2020, 44(6): 2020–2029
MA Fuqi, WANG Bo, DONG Xuzhu, et al. Power vision edge intelligence: power depth vision acceleration technology driven by edge computing[J]. Power System Technology, 2020, 44(6): 2020–2029
[13] 司羽飞, 谭阳红, 汪沨, 等. 面向电力物联网的云边协同结构模型[J]. 中国电机工程学报, 2020, 40(24): 7973–7979, 8234
SI Yufei, TAN Yanghong, WANG Feng, et al. Cloud-edge collaborative structure model for power Internet of Things[J]. Proceedings of the CSEE, 2020, 40(24): 7973–7979, 8234
[14] 赵文清, 严海, 周震东, 等. 基于残差BP神经网络的变压器故障诊断[J]. 电力自动化设备, 2020, 40(2): 143–148
ZHAO Wenqing, YAN Hai, ZHOU Zhendong, et al. Fault diagnosis of transformer based on residual BP neural network[J]. Electric Power Automation Equipment, 2020, 40(2): 143–148
[15] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023.
[16] ZHANG Y M, XU M, LI X D. Remote sensing image retrieval based on DenseNet model and CBAM[C]//2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET). Beijing, China. IEEE, 2020: 86-90.
[17] 路艳巧, 孙翠英, 曹红卫, 等. 基于边缘计算与深度学习的输电设备异物检测方法[J]. 中国电力, 2020, 53(6): 27–33
LU Yanqiao, SUN Cuiying, CAO Hongwei, et al. Foreign body detection method for transmission equipment based on edge computing and deep learning[J]. Electric Power, 2020, 53(6): 27–33
[18] 原吕泽芮, 顾洁, 金之俭. 基于云-边-端协同的电力物联网用户侧数据应用框架[J]. 电力建设, 2020, 41(7): 1–8
YUAN Lüzerui, GU Jie, JIN Zhijian. User-side data application framework based on cloud-edge-user collaboration in power Internet of Things[J]. Electric Power Construction, 2020, 41(7): 1–8
[19] 徐风, 苗哲, 业巧林. 基于卷积注意力模块的端到端遥感图像分类[J]. 林业工程学报, 2020, 5(4): 133–138
XU Feng, MIAO Zhe, YE Qiaolin. End-to-end remote sensing image classification framework based on convolutional block attention module[J]. Journal of Forestry Engineering, 2020, 5(4): 133–138
[20] 徐凯, 梁志坚, 张镱议, 等. 基于GoogLeNet Inception-V3模型的电力设备图像识别[J]. 高压电器, 2020, 56(9): 129–135, 143
XU Kai, LIANG Zhijian, ZHANG Yiyi, et al. Image recognition of electric equipment based on GoogLeNet inception-V3 model[J]. High Voltage Apparatus, 2020, 56(9): 129–135, 143
[21] 赵小强, 梁浩鹏. 使用改进残差神经网络的滚动轴承变工况故障诊断方法[J]. 西安交通大学学报, 2020, 54(9): 23–31
ZHAO Xiaoqiang, LIANG Haopeng. Fault diagnosis method for rolling bearing under variable working conditions using improved residual neural network[J]. Journal of Xi'an Jiaotong University, 2020, 54(9): 23–31
[22] GitHub-shanghai/InsulatorDataSet: 提供无人机捕获的普通绝缘子图像和合成有缺陷的绝缘子图像[EB/OL]. https://github.com/InsulatorData/InsulatorDataSet.
[23] 罗建军, 刘振声, 龚翔, 等. 基于无人机图像与迁移学习的线路绝缘子状态评价方法[J]. 电力工程技术, 2019, 38(5): 30–36
LUO Jianjun, LIU Zhensheng, GONG Xiang, et al. Insulator state evaluation method based on UAV image and migration learning[J]. Electric Power Engineering Technology, 2019, 38(5): 30–36
[24] 龚钢军, 张帅, 吴秋新, 等. 基于TensorFlow的高压输电线路异物识别[J]. 电力自动化设备, 2019, 39(4): 204–209, 216
GONG Gangjun, ZHANG Shuai, WU Qiuxin, et al. Foreign body identification based on TensorFlow for high voltage transmission line[J]. Electric Power Automation Equipment, 2019, 39(4): 204–209, 216

融合多维度特征的绝缘子状态边缘识别方法

An Edge Recognition Method for Insulator State Based on Multi-dimension Feature Fusion

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	郑鹏, 韩鹏程, 王国栋, 娄颖. 中压配电线路断线高阻接地故障精细化诊断方法[J]. 中国电力, 2024, 57(4): 220-228.
[2]	魏明江, 李鹏, 于浩, 冀浩然, 宋关羽, 习伟. 数字配电网边缘计算模拟实验平台[J]. 中国电力, 2024, 57(3): 12-19.
[3]	张博智, 张茹, 焦东翔, 王龙宇, 周一凡, 周丽霞. 基于VMD-SAST的电能质量扰动分类识别方法[J]. 中国电力, 2024, 57(2): 34-40.
[4]	李丹, 梁云嫣, 缪书唯, 方泽仁, 胡越, 贺帅. 基于高斯混合聚类和改进条件变分自编码的多风电场功率日场景生成方法[J]. 中国电力, 2024, 57(12): 17-29.
[5]	夏向阳, 谭欣欣, 单周平, 李辉, 徐志强, 吴晋波, 岳家辉, 陈贵全. 储能电站锂离子电池本体安全关键技术及新技术应用情况[J]. 中国电力, 2024, 57(11): 1-17.
[6]	李天楚, 容斌, 伍智鹏, 黄珏, 黄开来, 杨刚, 易杨. 基于边缘计算的风电群非故意发射超高次谐波抑制策略[J]. 中国电力, 2023, 56(8): 200-206,215.
[7]	吴钢, 周金辉, 李慧. 面向边缘增强分布式电力无线传感网的资源分配[J]. 中国电力, 2023, 56(8): 77-85,98.
[8]	杨晓峰, 方逸航, 赵鹏臻, 王承民, 谢宁. 基于K-means和BPNN的风机状态识别[J]. 中国电力, 2023, 56(6): 158-166,175.
[9]	杨晋祥, 彭勇刚, 蔡田田, 习伟, 邓清唐. 面向边缘计算的电力终端轻量级认证协议[J]. 中国电力, 2023, 56(4): 88-94.
[10]	赵隆, 温冠儒, 刘志成, 袁鹏, 董新胜. 基于参数优化的VMD-SVD和LSTM的输电杆塔倾斜状态识别[J]. 中国电力, 2023, 56(12): 217-226, 237.
[11]	马海飞, 滕伟, 彭迪康, 柳亦兵, 靳涛. 基于DRS与改进Autogram的风电齿轮箱复合故障特征提取[J]. 中国电力, 2023, 56(10): 71-79.
[12]	胡京, 邓颖, 蒋兴良, 曾蕴睿. 输电线路覆冰垭口微地形的特征提取与识别方法[J]. 中国电力, 2022, 55(8): 135-142.
[13]	孙毅, 常少南, 陈恺, 崔强, 沈维捷. 面向电工智慧物联的服务缓存和计算卸载策略[J]. 中国电力, 2022, 55(4): 23-32,43.
[14]	樊江川, 于昊正, 刘慧婷, 杨丽君, 安佳坤. 基于多分支门控残差卷积神经网络的短期电力负荷预测[J]. 中国电力, 2022, 55(11): 155-162,174.
[15]	陆旭, 陈影, 许中平, 张海全, 慕春芳, 陈恺, 孙毅. 面向5G边缘计算网络的联合需求响应与任务卸载策略[J]. 中国电力, 2022, 55(10): 209-218.

模态框（Modal）标题

融合多维度特征的绝缘子状态边缘识别方法

An Edge Recognition Method for Insulator State Based on Multi-dimension Feature Fusion

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics