Visualized Estimation of Composite Insulator Pollution Status of Transmission Line Based on Reflective Multispectral Imaging

doi:10.11930/j.issn.1004-9649.202311070

Abstract

Abstract:

The timely and accurate online estimation for composite insulators of transmission lines can effectively prevent pollution flashover accidents. A visualized estimation method of composite insulator pollution status of transmission line was proposed in this paper. Firstly, a semi-automatic registration method for multisource reflective spectral images was constructed by combining image registration algorithm and target area selection, which solved the inherent image registration problem of multi-lens camera. Secondly, model pre-training was conducted through shooting artificial contaminated samples using the low cost, lightweight, and high imaging quality multi-lens multispectral imaging equipment, and further transfer training was conducted through real natural contaminated samples to construct a pollution grade diagnosis model of composite insulation material surfaces. Finally, the actual pollution status of transmission line composite insulators was measured and analyzed under different shooting conditions using the unmanned aerial vehicle platform. The results indicate that the pollution grade classification accuracy for artificial and natural contaminated samples is 95.3% and 87.8%, respectively, and the pollution grade classification accuracy for actual transmission line composite insulators can reach up to 90% with pollution distribution area clearly displayed. The feasibility of transmission line insulator pollution grade estimation and pollution distribution visualization diagnosis based on reflective multispectral imaging technology is verified, which can provide a new technique for status inspection and maintenance decision of transmission line insulators.

Key words: composite insulator, multispectral imaging technology, estimation of pollution status, image registration, transfer learning

Ming REN, Qianyu LI, Changjie XIA, Ming DONG. Visualized Estimation of Composite Insulator Pollution Status of Transmission Line Based on Reflective Multispectral Imaging[J]. Electric Power, 2025, 58(2): 203-215.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202311070

https://www.electricpower.com.cn/EN/Y2025/V58/I2/203

Figures/Tables 25

Fig.1 Diagram of multispectral image information acquisition platform

Fig.2 Physical image and structure diagram of multi-lens multispectral image acquisition device

Fig.3 Image acquisition diagram using multi-lens multispectral camera

Table 1 Main parameters of multi-lens multispectral image acquisition device

参数名称		数值
质量/g		640
中心波长（带宽）/nm		450 (35)、555 (25)、660 (22.5)、 720 (10)、750 (10)、840 (30)
量化位数/bit		12
图像分辨率		1280×960
CMOS尺寸		1/3′′
像元尺寸/μm		3.75×3.75

Fig.4 Overall construction process and application of visualization estimation method for pollution status

Fig.5 Unregistered multispectral image of transmission line composite insulators

Table 2 Mutual information and registration time of different registration algorithms

配准算法	综合互信息	配准时间/s
基于SIFT的配准算法	0.780 2	2.587 5
基于SURF的配准算法	0.775 0	0.881 3
基于ORB的配准算法	0.751 4	0.868 8

Fig.6 Complex background multispectral image registration based on SURF algorithm

Fig.7 Complex background multispectral image registration based on target area selection

Fig.8 Complex background multispectral image registration based on feature point pair marking

Table 3 NaCl and Kaolin content of different pollution grades 单位：mg/cm2

污秽等级	NaCl	CaSO₄	高岭土
0	0	0	0
Ⅰ	0.016	0.040	0.336
Ⅱ	0.028	0.070	0.588
Ⅲ	0.070	0.175	1.470
Ⅳ	0.100	0.250	2.100

Fig.9 Composite insulation material samples with different pollution levels

Fig.10 Spectral curve of composite insulation material with different pollution grades

Table 4 Estimation result of different classification models

分类模型	超参数搜索算法	验证准确率/%	测试准确率/%
SVM	网格搜索	94.6	94.5
	随机搜索	95.2	95.0
	贝叶斯优化	95.2	95.0
KNN	网格搜索	95.0	94.8
	随机搜索	95.2	95.0
	贝叶斯优化	95.3	95.2
随机森林	网格搜索	94.9	94.6
	随机搜索	95.2	95.0
	贝叶斯优化	95.4	95.3

Table 5 Hyper-parameters of pollution grade diagnosis model

参数名称		参数值
决策树数量		486
最大分离特征数		7
决策树最大深度		93
特征选取方法		基尼系数

Fig.11 Visualized result of surface pollution status of composite insulation material

Fig.12 Migration training data construction process

Table 6 Data set division of natural contaminated samples

样本污秽等级	训练集数据/个	测试集数据/个
0	1 550	523
Ⅰ	1 650	546
Ⅱ	1 700	577
Ⅲ	1 750	584
Ⅳ	1 800	612

Table 7 Estimation result of natural contamination dataset before and after migration training

	训练集准确率/%	测试集准确率/%
迁移训练前	70.4	71.5
迁移训练后	90.2	87.8

Fig.13 Actual pictures of measurement site

Table 8 Shooting plan of transmission line composite insulators

组别	环境光照	拍摄角度	绝缘子器型
a	晴天（60 000~80 000 lux）	30°俯拍	复合悬式绝缘子
b	晴天（60 000~80 000 lux）	30°仰拍	复合悬式绝缘子
c	阴天（10 000~15 000 lux）	30°仰拍	复合悬式绝缘子
d	阴天（10 000~15 000 lux）	30°俯拍	瓷复合绝缘子
e	阴天（10 000~15 000 lux）	30°仰拍	瓷复合绝缘子

Fig.14 Flow chart of the proposed detection method and traditional method

Fig.15 Comparison of actual pollution grade and estimation result of composite pin insulator

Fig.16 Comparison of actual pollution grade and estimation result of porcelain composite insulator

Fig.17 Multispectral image of insulator and visualized estimation result of pollution distribution

References 27

1	王少华, 胡文堂, 何文林, 等. 自然积污绝缘子的污闪电压和泄漏电流特性试验研究[J]. 电网技术, 2013, 37 (10): 2855- 2860.
	WANG Shaohua, HU Wentang, HE Wenlin, et al. Experimental research on pollution flashover voltage and leakage current characteristics of naturally polluted insulators[J]. Power System Technology, 2013, 37 (10): 2855- 2860.
2	蒋兴良, 赵世华, 毕茂强, 等. 污秽绝缘子闪络特性与泄漏电流特性研究[J]. 中国电机工程学报, 2013, 33 (31): 220- 226, 27.
	JIANG Xingliang, ZHAO Shihua, BI Maoqiang, et al. Research on flashover performance and leakage current performance of polluted insulators[J]. Proceedings of the CSEE, 2013, 33 (31): 220- 226, 27.
3	宿志一, 李庆峰. 我国电网防污闪措施的回顾和总结[J]. 电网技术, 2010, 34 (12): 124- 130.
	SU Zhiyi, LI Qingfeng. Historical review and summary on measures against pollution flashover occurred in power grids in China[J]. Power System Technology, 2010, 34 (12): 124- 130.
4	何昱燊, 张志劲, 傅海涛, 等. 自然环境下覆冰绝缘子长串交流闪络电压及电弧发展现象[J]. 电网技术, 2021, 45 (7): 2904- 2912.
	HE Yushen, ZHANG Zhijin, FU Haitao, et al. AC flashover voltage and arc development of long insulators strings in natural icing environment[J]. Power System Technology, 2021, 45 (7): 2904- 2912.
5	姜新建, 董弘川, 王黎明, 等. 用有效盐密作为表征污秽度的新方法[J]. 高电压技术, 2017, 43 (12): 3869- 3875.
	JIANG Xinjian, DONG Hongchuan, WANG Liming, et al. New method to describe contamination degree of insulators by effective equivalent salt deposit density[J]. High Voltage Engineering, 2017, 43 (12): 3869- 3875.
6	张志劲, 卢炳宏, 傅海涛, 等. 基于XP-160污秽体积分数的绝缘子积污表征[J]. 电网技术, 2021, 45 (9): 3737- 3744.
	ZHANG Zhijin, LU Binghong, FU Haitao, et al. Characterization of insulator contamination based on XP-160 pollution volume fraction[J]. Power System Technology, 2021, 45 (9): 3737- 3744.
7	王尧平, 李特, 姜凯华, 等. 基于气象特征挖掘与AdaBoost-MEA-ELM模型的绝缘子盐密预测[J]. 中国电力, 2023, 56 (9): 157- 167.
	WANG Yaoping, LI Te, JIANG Kaihua, et al. Prediction of Insulator ESDD based on meteorological feature mining and AdaBoost-MEA-ELM model[J]. Electric Power, 2023, 56 (9): 157- 167.
8	SLAMA M E, BEROUAL A, HADI H. Influence of the linear non-uniformity of pollution layer on the insulator flashover under impulse voltage-estimation of the effective pollution thickness[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2011, 2 (18): 384- 392.
9	张志劲, 杨晟欢, 蒋兴良, 等. 迎背风不均匀污秽下支柱绝缘子直流污闪特性[J]. 电网技术, 2020, 44 (1): 354- 360.
	ZHANG Zhijin, YANG Shenghuan, JIANG Xingliang, et al. DC flashover characteristics of post insulators under non-uniform pollution between windward and leeward sides[J]. Power System Technology, 2020, 44 (1): 354- 360.
10	金立军, 田治仁, 高凯, 等. 基于红外与可见光图像信息融合的绝缘子污秽等级识别[J]. 中国电机工程学报, 2016, 36 (13): 3682- 3691, 3389.
	JIN Lijun, TIAN Zhiren, GAO Kai, et al. Discrimination of insulator contamination grades using information fusion of infrared and visible images[J]. Proceedings of the CSEE, 2016, 36 (13): 3682- 3691, 3389.
11	王丰华, 刘国坚, 张宏钊, 等. 基于改进C-V模型的外绝缘放电紫外图像特征量提取[J]. 高电压技术, 2018, 44 (8): 2525- 2532.
	WANG Fenghua, LIU Guojian, ZHANG Hongzhao, et al. Ultraviolet image parameters extraction of insulation surface discharge based on improved C-V model[J]. High Voltage Engineering, 2018, 44 (8): 2525- 2532.
12	DOUGLAS B, GAMAL E, SUN D W. Non-destructive of chemical composition intact and minced pork using near-infrared hyperspectral imaging[J]. Food Chemistry, 2013, 138, 1162- 1171.
13	KONG Y, LIU Y, GENG J, et al. Pixel-level assessment model of contamination conditions of composite insulators based on hyperspectral imaging technology and a semi-supervised ladder network[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2023, 30 (1): 326- 335.
14	邱彦, 张血琴, 郭裕钧, 等. 基于高光谱技术的绝缘子污秽等级检测方法[J]. 高电压技术, 2019, 45 (11): 3587- 3594.
	QIU Yan, ZHANG Xueqin, GUO Yujun, et al. Detection method of insulator contamination grades based on hyperspectral technique[J]. High Voltage Engineering, 2019, 45 (11): 3587- 3594.
15	邱彦, 郭裕钧, 张血琴, 等. 基于高光谱技术的绝缘子污秽成分识别方法[J]. 高电压技术, 2020, 46 (11): 4023- 4030.
	QIU Yan, GUO Yujun, ZHANG Xueqin, et al. Identification method of insulator pollution components based on hyperspectral technology[J]. High Voltage Engineering, 2020, 46 (11): 4023- 4030.
16	马欢, 郭裕钧, 张血琴, 等. 基于高光谱技术的绝缘子污秽含水量检测[J]. 高电压技术, 2020, 46 (4): 1396- 1404.
	MA Huan, GUO Yujun, ZHANG Xueqin, et al. Moisture content detection of insulator contamination based on hyperspectral technology[J]. High Voltage Engineering, 2020, 46 (4): 1396- 1404.
17	MANOLAKIS D, MARDEN D, SHAW G A. Hyperspectral image processing for automatic target detection applications[J]. Lincoln Laboratory Journal, 2003, 14 (1): 79- 116.
18	XIA C J, REN M, WANG B, et al. Acquisition and analysis of hyperspectral data for surface contamination level of insulating materials[J]. Measurement, 2021, 173, 108560.
19	何锦强, 李锐海, 李昊, 等. 基于Yolo v5与Grabcut的架空线路绝缘子可见光图像自动识别与分割方法[J]. 南方电网技术, 2023, 17 (6): 128- 135.
	HE Jinqiang, LI Ruihai, LI Hao, et al. Visible light image automatic recognition and segmentation method for overhead power line insulators based on Yolo v5 and Grabcut[J]. Southern Power System Technology, 2023, 17 (6): 128- 135.
20	WANG B, DONG M, REN M, et al. Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69 (8): 5345- 5355.
21	张二虎, 卞正中. 基于最大熵和互信息最大化的特征点配准算法[J]. 计算机研究与发展, 2004, 41 (7): 1194- 1199.
	ZHANG Erhu, BIAN Zhengzhong. A new robust point registration algorithm by maximization of entropy and mutual information[J]. Journal of Computer Research and Development, 2004, 41 (7): 1194- 1199.
22	肖明, 鲍永亮, 颜仲新. 基于点特征的图像配准方法综述[J]. 兵工学报, 2015, 36 (增刊2): 326- 340.
23	关志成, 张仁豫, 薛家麒, 等. 自然污秽可溶盐构成及其对污闪电压值的影响[J]. 电瓷避雷器, 1989, (6): 13- 18.
24	国家质量监督检验检疫总局, 中国国家标准化管理委员会. 污秽条件下使用的高压绝缘子的选择和尺寸确定第1部分: 定义、信息和一般原则: GB/T 26218.1—2010[S]. 北京: 中国标准出版社, 2011.
25	国家能源局. 高压交流系统用复合绝缘子人工污秽试验: DL/T 859—2015[S]. 北京: 中国电力出版社, 2015.
26	程乐峰, 余涛, 张孝顺, 等. 机器学习在能源与电力系统领域的应用和展望[J]. 电力系统自动化, 2019, 43 (1): 15- 31.
	CHENG Lefeng, YU Tao, ZHANG Xiaoshun, et al. Machine learning for energy and electric power systems: state of the art and prospects[J]. Automation of Electric Power Systems, 2019, 43 (1): 15- 31.
27	肖建, 于龙, 白裔峰. 支持向量回归中核函数和超参数选择方法综述[J]. 西南交通大学学报, 2008, 43 (3): 297- 303.
	XIAO Jian, YU Long, BAI Yifeng. Survey of the selection of kernels and hyper-parameters in support vector regression[J]. Journal of Southwest Jiaotong University, 2008, 43 (3): 297- 303.

[1]	WANG Xuejun, FANG Shuiping, CHI Guangyong. Transient Stability Assessment Method for Multi-machine Power Systems Considering Dense Channels [J]. Electric Power, 2025, 58(8): 139-146.
[2]	Chenhao ZHAO, Zaibin JIAO, Chenghao LI, Di ZHANG, Penghui ZHANG. Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning [J]. Electric Power, 2025, 58(1): 70-77.
[3]	XU Hao, LIU Liqiang, LV Chao. An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning [J]. Electric Power, 2021, 54(8): 52-59.
[4]	WANG Sihua, WANG Junjun, ZHAO Lei, CHEN Long. Influence of Pollution Components on Surface Electric Field of Composite Insulators [J]. Electric Power, 2021, 54(7): 149-157.
[5]	WANG Sihua, CHEN Long, WANG Junjun, ZHAO Lei. Fuzzy Comprehensive Evaluation of Aging State of Silicone Rubber Sheds of Composite Insulators [J]. Electric Power, 2021, 54(5): 156-165.
[6]	GAO Yi, TIAN Lianfang, DU Qiliang. Overheating Defect Detection of Composite Insulator Based on Mask R-CNN [J]. Electric Power, 2021, 54(1): 135-141.
[7]	SHEN Wei, GAO Hua, ZHANG Peng, ZUO Kun, TIAN Yi, YANG Wenbo, YUAN Zhenkang. Study on UV Aging Properties of Polymer Composite Insulators [J]. Electric Power, 2019, 52(9): 73-78,85.
[8]	LEI Xinglie, LI Yuze, PENG Yong, FANG Yuqun, QIAN Liqun. Influence of Conductivity Defects of Composite Insulators on Live-Line Working Approach Distance [J]. Electric Power, 2019, 52(6): 134-139.
[9]	ZHOU Ziqiang, JI Yang, SU Ye, CAI Junyu. A Hybrid Transfer Learning/CNN Algorithm for Cable Tunnel Rust Recognition [J]. Electric Power, 2019, 52(4): 104-110.
[10]	WU Gaolin, ZHOU Qing, XIAO Qianbo, HU Jianlin, BI Maoqiang. Study on Icing and Contamination Flashover Characteristics and Critical Flashover Current of Composite Insulators [J]. Electric Power, 2019, 52(4): 59-65.
[11]	WANG Kang, ZHU Yazhou, ZHOU Xinnan, WU Zhaoguo, HU Yuanxiang. Analysis on the Interfacial Issues of Running Composite Insulators [J]. Electric Power, 2018, 51(4): 45-52.
[12]	ZHAO Meiyun, WU Yang, ZHAO Xinze, PENG Wenyu, XI Senming. Analysis on the Leakage Current on the Composite Insulator Surface with Corrosion Product Fouling [J]. Electric Power, 2018, 51(2): 7-12,53.
[13]	DANG Huixue, ZHAO Junhai, WU Jing. Characteristic Study for Windage Yaw Analysis of V-Shape Composite Insulator String [J]. Electric Power, 2016, 49(7): 9-14.
[14]	MIN Xuan, WEN Zhike, ZENG Yunfei, FU Jing. Characteristics of Windage Yaw in UHV Insulator Strings under Fluctuating Wind [J]. Electric Power, 2016, 49(3): 65-71.
[15]	LI Mengxing, WANG Haiyan, SONG Xinli, LIU Jian, ZHAO Lianzheng, HOU Ruifeng. Structure Design and Test Analysis of Surge Arrester with Insulator Performance [J]. journal1, 2015, 48(9): 90-94.