深度学习在绝缘子红外图像异常诊断的应用

范鹏, 冯万兴, 周自强, 赵淳, 周盛, 姚翔宇

范鹏, 冯万兴, 周自强, 赵淳, 周盛, 姚翔宇. 深度学习在绝缘子红外图像异常诊断的应用[J]. 红外技术, 2021, 43(1): 51-55.
引用本文: 范鹏, 冯万兴, 周自强, 赵淳, 周盛, 姚翔宇. 深度学习在绝缘子红外图像异常诊断的应用[J]. 红外技术, 2021, 43(1): 51-55.
FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. Infrared Technology , 2021, 43(1): 51-55.
Citation: FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. Infrared Technology , 2021, 43(1): 51-55.

深度学习在绝缘子红外图像异常诊断的应用

基金项目: 

国网电力科学研究院有限公司科技项目 524625190054

详细信息
    作者简介:

    范鹏(1986-),男,硕士,高级工程师,主要从事电网智能运检、电力物联网与人工智能方面的技术研究工作。E-mail: fanpeng2@sgepri.sgcc.com.cn

  • 中图分类号: TN219

Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis

  • 摘要: 绝缘子的红外图像分析一般采用图像处理的方法,易受背景环境和数据量的影响,准确率和效率均较低,本文提出一种深度学习的异常诊断方法,基于改进的Faster R-CNN方法搭建检测网络,开展不同类型的绝缘子测试。研究结果表明:相对于神经网络(Back Propagation,BP)、Faster R-CNN方法,本文方法可高效地诊断出绝缘子的异常缺陷,平均检测精度达到90.2%;单Ⅰ型和Ⅴ型绝缘子的异常诊断准确率高于双Ⅰ型绝缘子。研究结果可为输电线路绝缘子异常诊断提供一定的参考。
    Abstract: Because of the effects of the background environment and data volume, the accuracy and efficiency of abnormal defects in traditional infrared images of insulators are generally low. In this study, a deep-learning anomaly diagnosis method is proposed. Based on the improved faster region-based convolutional neural network (R-CNN) method, a detection network is built to test different types of insulators. Results show that compared with the back propagation neural network and faster R-CNN methods, the proposed method can diagnose abnormal defects of insulators efficiently with a mean average precision of 90.2%. In addition, the diagnostic accuracy of single type Ⅰ and type Ⅴ insulators is higher than that of double type Ⅰ insulators. The results can provide a reference for insulator defect identification in transmission lines.
  • 图  1   Faster R-CNN的算法流程

    Figure  1.   Algorithm flow of Faster R-CNN

    图  2   正负样本判定

    Figure  2.   Positive and negative sample decision

    图  3   改进的Faster R-CNN结构

    Figure  3.   Structure of improved Faster R-CNN

    图  4   准确率-召回率关系曲线

    Figure  4.   Relation curves of precision and recall

    图  5   不同类型绝缘子的红外图像

    Figure  5.   Infrared image of different types of insulators

    表  1   软硬件配置

    Table  1   Hardware and software configuration

    Name Model
    Operating system Ubuntu 16.04.1
    Database mysql 5.5.20
    CPU Intel Xeon Silver 4114T 12C
    GPU NVIDIA GTX1080Ti
    Memory 32 G
    Hard disk 1 T
    Frame Detectron
    下载: 导出CSV

    表  2   样本配置信息

    Table  2   Information of sample configuration

    sample type training set verification set test set total
    positive 500 250 750 1500
    negative 500 250 125 875
    total 1000 500 875 2375
    下载: 导出CSV

    表  3   不同方法的实验结果统计

    Table  3   Statistics of experimental results by different methods

    Name Precision Recall mAP Time/s
    BP 93.5% 90.4% 80.3% 2.3
    Faster R-CNN 98.7% 95.3% 88.7% 1.2
    BFEM 99.2% 97.6% 90.2% 0.9
    下载: 导出CSV

    表  4   绝缘子异常诊断的准确率

    Table  4   Accuracy of insulator anomaly diagnosis

    Insulator type Abnormal total Detected number Accuracy
    Single Ⅰ 62 61 98.4%
    Double Ⅰ 47 44 93.6%
    31 31 100.0%
    下载: 导出CSV
  • [1] 陈俊佑, 金立军, 段绍辉, 等.基于Hu不变矩的红外图像电力设备识别[J].机电工程, 2013, 30(1): 5-8. https://www.cnki.com.cn/Article/CJFDTOTAL-JDGC201301003.htm

    CHEN Junyou, JIN Lijun, DUAN Shaohui, et al. Power equipment identification in infrared image based on Hu invariant moments[J]. Journal of Mechanical & Electrical Engineering, 2013, 30(1): 5-8. https://www.cnki.com.cn/Article/CJFDTOTAL-JDGC201301003.htm

    [2] 邹辉, 黄福珍.基于改进Fast-Match算法的电力设备红外图像多目标定位[J].中国电机工程学报, 2017, 37(2): 591-598. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201702027.htm

    ZOU Hui, HU Fuzhen. Multi-target localization for infrared images of electrical equipment based on improved fast-match algorithm[J]. Proceedings of the CSEE, 2017, 37(2): 591-598. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201702027.htm

    [3] 魏秀深.解析深度学习:卷积神经网络原理与视觉实践[M].北京:电子工业出版社, 2018.

    WEI Xiushen. Analytic Deep Learning: Convolutional Neural Network Theory And Visual Practice[M]. Beijing: Electronic Industry Press, 2018.

    [4] 罗舜.电力变压器套管将军帽发热故障的红外诊断分析[J].变压器, 2018, 55(1): 50-53. https://www.cnki.com.cn/Article/CJFDTOTAL-BYQZ201801018.htm

    LUO Sun. Infrared diagnosis analysis of power transformer bushing coupler heating[J]. Transformer, 2018, 55(1): 50-53. https://www.cnki.com.cn/Article/CJFDTOTAL-BYQZ201801018.htm

    [5] 张杰, 付泉泳, 袁野.变压器局部放电带电检测技术应用研究[J].变压器, 2018, 55(8): 66-71. https://www.cnki.com.cn/Article/CJFDTOTAL-BYQZ201808023.htm

    ZHANG Jie, FU Quanyong, YUAN Ye. Application research of electric detection technology of partial discharge for transformer[J]. Transformer, 2018, 55(8): 66-71. https://www.cnki.com.cn/Article/CJFDTOTAL-BYQZ201808023.htm

    [6] 梁天明, 袁焯锋, 石延辉.高压交流滤波电容器局部过热诱因分析及预防[J].电力电容器与无功补偿, 2015, 36(6): 49-53. https://www.cnki.com.cn/Article/CJFDTOTAL-DLDY201506011.htm

    LIANG Tianming, YUAN Daofeng, SHI Yanhui. Cause analysis and preventions on local overheating of high voltage ac filter capacitor[J]. Power Capacitor & Reactive Power Compensation, 2015, 36(6): 49-53. https://www.cnki.com.cn/Article/CJFDTOTAL-DLDY201506011.htm

    [7] 潘臻, 安立.一起35 kV并联电容器组事故爆炸原因分析[J].电力电容器与无功补偿, 2015, 36(3): 17-20. https://www.cnki.com.cn/Article/CJFDTOTAL-DLDY201503005.htm

    PAN Zhen, AN Li. Analysis of 35 kV shunt capacitor banks explosion accident[J]. Power Capacitor & Reactive Power Compensation, 2015, 36(3): 17-20. https://www.cnki.com.cn/Article/CJFDTOTAL-DLDY201503005.htm

    [8] 黄斌, 李昊, 徐姗姗, 等.一起35 kV并联电容器组爆炸原因分析及防范措施[J].电力电容器与无功补偿, 2018, 39(1): 23-27. https://www.cnki.com.cn/Article/CJFDTOTAL-DLDY201801005.htm

    HUANG Bin, LI Hao, XU Sansan, et al. Reason analysis and precautionary measures for a 35kv shunt capacitor bank explosion[J]. Power Capacitor & Reactive Power Compensation, 2018, 39(1): 23-27. https://www.cnki.com.cn/Article/CJFDTOTAL-DLDY201801005.htm

    [9] 商俊平, 李储欣, 陈亮.基于视觉的绝缘子定位与自爆缺陷检测[J].电子测量与仪器学报, 2017, 31(6): 844-849. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201706007.htm

    SHANG Junping, LI Chuxin, CHEN Liang. Location and detectionfor self-explode insulator based on vision[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(6): 844-849. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201706007.htm

    [10] 沈新平, 彭刚, 袁志强.基于霍夫变换和RANSAC算法的绝缘子定位方法[J].电子测量技术, 2017, 40(6): 132-137. https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL201706031.htm

    SHEN Xinping, PENG Gang, YUAN Zhiqiang. Insulator location method based on hough transformation and RANSAC algorithm[J]. Electronic Measurement Technology, 2017, 40(6): 132-137. https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL201706031.htm

    [11] 李军锋, 王钦若, 李敏.结合深度学习和随机森林的电力设备图像识别[J].高电压技术, 2017, 43(11): 3705-3711. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ201711028.htm

    LI Junfeng, WANG Qinruo, LI Min, et al. Electric Equipment Image Recognition Based on Deep Learning and Random Forest[J]. High Voltage Engineering, 2017, 43(11): 3705-3711. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ201711028.htm

    [12] 侯春萍, 章衡光, 张巍, 等.输电线路绝缘子自爆缺陷识别方法[J].电力系统及其自动化学报, 2019, 31(6): 1-6.

    HOU Chunping, ZHANG Hengguang, ZHANG Wei, et al. Recognition method for faults of insulators on transmission lines[C]//Proceedings of the CSU-EPSA, 2019, 31(6): 1-6.

    [13] 左川.基于图像识别的输电线路绝缘子检测方法研究[D].北京: 华北电力大学, 2019.

    ZUO Chuang. Research on detection method of transmission line insulator based on image recognition[D]. Beijing: North China Electric Power University, 2019.

    [14] 杨光俊.卷积神经网络在电力设备红外图像识别中的应用研究[D].广州: 华南理工大学, 2019.

    YANG Guangjun. Research on the application of convolutional neural network in infrared image recognition of power equipment[D]. Guangzhou: South CHINA University of Technology, 2019.

    [15] 周可慧, 廖志伟, 肖异瑶, 等.基于改进CNN的电力设备红外图像分类模型构建研究[J].红外技术, 2019, 41(11): 1033-1038. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201911007.htm

    ZHOU Kehui, LIAO Zhiwei, XIAO Yiyao, et al. Construction of infrared image classification model for power equipments based on improved CNN[J]. Infrared Technology, 2019, 41(11): 1033-1038. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201911007.htm

    [16] 许必宵.基于多尺度特征融合与上下文分析的目标检测技术研究[D].南京: 南京邮电大学, 2019.

    XU Bixiao. Research on object detection technology based on multi-scale feature fusion and context analysis[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2019.

    [17] 张丹丹.基于航拍图像的绝缘子自爆位置的检测[D].成都: 西华大学, 2018.

    ZHANG Dandan. Detection of self-exploding position of insulator based on aerial image[D]. Chengdu: Xihua University, 2018.

    [18] 王梦.基于绝缘子图像的缺陷检测方法研究[D].武汉: 华中科技大学, 2019.

    WANG Meng. A thesis submitted in partial fulfillment of the requirements[D]. Wuhan: Huazhong University of Science & Technology, 2019.

    [19] 国家能源局.带电设备红外诊断应用规范: DL/T 664-2008[S].北京: 中国标准出版社, 2008.

    National Energy Administration. Application rules of infrared diagnosis for live electrical equipment: DL/T 664-2008[S]. Beijing: China Electric Power Press, 2008.

  • 期刊类型引用(10)

    1. 马庆禄,汪曦洪,马恋,段学锋. 隧道内不均匀照度下无人驾驶视觉融合感知方法. 应用光学. 2025(01): 89-101 . 百度学术
    2. 段锦,张昊,宋靖远,刘举. 深度学习偏振图像融合研究现状. 红外技术. 2024(02): 119-128 . 本站查看
    3. 陈锦妮,陈宇洋,李云红,拜晓桦. 基于结构与分解的红外光强与偏振图像融合. 红外技术. 2023(03): 257-265 . 本站查看
    4. 张哲卿,朱志宇,魏莱,古静,顾健,臧旭. 复杂海面背景下船舶红外偏振图像融合方法. 电光与控制. 2023(07): 68-72 . 百度学术
    5. 张媛,陆小妍,郭群,邱建博,缪正飞. 基于主成分分析和双树复小波变换的CT和MRI图像融合改进算法研究. 中国医学装备. 2022(04): 7-12 . 百度学术
    6. 王晓娜,潘晴,田妮莉. 基于NSST-DWT-ICSAPCNN的多模态图像融合算法. 红外技术. 2022(05): 497-503 . 本站查看
    7. 田立凡,杨莘,梁佳明,吴谨. 基于SGWT和多显著性的红外与可见光图像融合. 红外技术. 2022(07): 676-685 . 本站查看
    8. 安晓东,李亚丽,王芳. 汽车驾驶辅助系统红外与可见光融合算法综述. 计算机工程与应用. 2022(19): 64-75 . 百度学术
    9. 刘立群,顾任远,周煜博,火久元. 多尺度分解双寻优策略SPCNN的果园苹果异源图像融合模型. 农业工程学报. 2022(17): 158-167 . 百度学术
    10. 贺兴容,龚奕宇,范松海,吴天宝,刘益岑,刘小江. 基于帧差检测技术与区域特征的红外与可见光图像融合算法. 现代电子技术. 2019(01): 57-61 . 百度学术

    其他类型引用(14)

图(5)  /  表(4)
计量
  • 文章访问数:  558
  • HTML全文浏览量:  183
  • PDF下载量:  59
  • 被引次数: 24
出版历程
  • 收稿日期:  2020-03-07
  • 修回日期:  2020-11-21
  • 刊出日期:  2021-01-19

目录

    /

    返回文章
    返回
    x 关闭 永久关闭

    尊敬的专家、作者、读者:

    端午节期间因系统维护,《红外技术》网站(hwjs.nvir.cn)将于2024年6月7日20:00-6月10日关闭。关闭期间,您将暂时无法访问《红外技术》网站和登录投审稿系统,给您带来不便敬请谅解!

    预计6月11日正常恢复《红外技术》网站及投审稿系统的服务。您如有任何问题,可发送邮件至编辑部邮箱(irtek@china.com)与我们联系。

    感谢您对本刊的支持!

    《红外技术》编辑部

    2024年6月6日