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.

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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

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