不同材质绝缘子污秽等级高光谱检测方法研究

doi:10.19595/j.cnki.1000-6753.tces.211892

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Abstract The surface of naturally contaminated insulators is not completely covered by pollution and contains insulator material information. According to the detection principle of hyperspectral technology, the differences between porcelain, glass and silicone rubber insulator materials will be reflected in hyperspectral spectral line data, so it is hard to detect the contamination grades by the same model. Modeling the measured spectra of each material sample separately requires a large number of samples with known spectra and measured values, high modeling cost, long time and low efficiency. This paper realizes the rapid detection of the contamination grades of different material insulators through the piecewise direct standardization (PDS) model transfer method.
Firstly, test samples with different materials and different contamination grades were prepared according to relevant standards, and their hyperspectral data were obtained by the hyperspectral data acquisition platform, and their salt density and ash density values were measured using the equivalent salt deposit density. Then, the samples hyperspectral data after removing the interference were obtained through preprocessing operation. Finally, the main model of contamination grades detection of support vector machine is established based on the data of glass insulator, and the PDS algorithm is used to transfer the data of porcelain and silicone rubber material samples. The contamination grades detection method of different material insulation samples are obtained.
Through spectral line analysis and test verification, the following conclusions are drawn: (1) Under the same contamination grades, the hyperspectral lines of different material insulators have obvious differences, which are shown in the positions and changing trends of absorption peak and reflection peak. In the case of different contamination grades of the same material, the difference of spectral lines is mainly reflected in the amplitude. The higher the contamination grades, the lower the spectral line amplitude. (2) The support vector machine (SVM) contamination grades detection model was established by the data of glass insulation samples, and the detection accuracy of the contamination grades of glass samples was 98.3%. PDS algorithm can effectively reduce the difference between the spectral lines of porcelain, silicone rubber and glass samples. After the model transfer, the accuracy of the model in classifying the contamination grades of porcelain samples is increased from 25.0% to 95.0%, and the accuracy of the model in classifying the contamination grades of silicone rubber samples is increased from 25.0% to 91.7%. (3) The detection method proposed in this paper can detect the contamination grades of artificial contaminated insulators of different materials, and the accuracy is 83.3%.

Key words： Insulator contamination grade hyperspectral technique piecewise direct standardization support vector machine

Received: 18 November 2021

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TM216

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	Zhang Xueqin
	Zhou Zhipeng
	Guo Yujun
	Yang Kun
	Wu Guangning

Cite this article:

Zhang Xueqin,Zhou Zhipeng,Guo Yujun等. Detection Method of Contamination Grades of Insulators with Different Materials Based on Hyperspectral Technique[J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1946-1955.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211892 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I7/1946

[1] 关志成, 刘瑛岩, 周远翔. 绝缘子及输变电设备外绝缘[M]. 北京: 清华大学出版社, 2006.
[2] 蒋兴良, 任晓东, 韩兴波, 等. 不同布置方式对交流绝缘子串人工污秽闪络特性的影响[J]. 电工技术学报, 2020, 35(4): 896-905.
Jiang Xingliang, Ren Xiaodong, Han Xingbo, et al.Influence of different layout methods on artificial pollution flashover characteristics of AC insulator strings[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 896-905.
[3] 姜昀芃, 李黎, 卢明, 等. 瓷绝缘子表面粘附颗粒的粒径分布特性及其影响因素研究[J]. 电工技术学报, 2019, 34(3): 611-619.
Jiang Yunpeng, Li Li, Lu Ming, et al.Study on particle diameter distribution characteristics and influence factors of adhered particles on the porcelain insulator surface[J]. Transactions of China Electrotechnical Society, 2019, 34(3): 611-619.
[4] 谷裕, 阳林, 张福增, 等. 高海拔地区特高压换流站大尺寸复合支柱绝缘子直流污闪特性[J]. 电工技术学报, 2016, 31(10): 93-101.
Gu Yu, Yang Lin, Zhang Fuzeng, et al.DC pollution flashover performance of ultra high voltage convert stations large-size composite post insulators at high altitude areas[J]. Transactions of China Electrotechnical Society, 2016, 31(10): 93-101.
[5] 赵全香, 李红艳, 韩振, 等. 绝缘子污秽检测方法综述[J]. 电气开关, 2012, 50(4): 96-98.
Zhao Quanxiang, Li Hongyan, Han Zhen, et al.A summary of insulator pollution detection method[J]. Electric Switchgear, 2012, 50(4): 96-98.
[6] 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.
[7] 王黎明, 李旭, 曹彬, 等. 局部电弧对绝缘子泄漏电流和表面电导率关系的影响[J]. 高电压技术, 2019, 45(5): 1624-1629.
Wang Liming, Li Xu, Cao Bin, et al.Influence of partial arc on leakage current and surface conductivity of insulators[J]. High Voltage Engineering, 2019, 45(5): 1624-1629.
[8] 刘刚, 胡倩楠, 陈锡阳. 由表面电导实现绝缘子污秽程度监测的可行性分析[J]. 高电压技术, 2012, 38(6): 1321-1326.
Liu Gang, Hu Qiannan, Chen Xiyang.Feasibility analysis of monitoring the insulator surface contamination based on surface conductivity[J]. High Voltage Engineering, 2012, 38(6): 1321-1326.
[9] 毛颖科, 关志成, 王黎明, 等. 基于泄漏电流脉冲主成分分析的外绝缘污秽状态评估方法[J]. 电工技术学报, 2009, 24(8): 39-45.
Mao Yingke, Guan Zhicheng, Wang Liming, et al.Evaluation of contamination levels of outdoor insulators based on the principal components analysis of leakage current pulses[J]. Transactions of China Electrotechnical Society, 2009, 24(8): 39-45.
[10] 宋毅. 绝缘子表面污层电导率的影响因素研究[J]. 高压电器, 2014, 50(10): 102-106.
Song Yi.Study on influence factor of insulator surface pollution layer conductivity[J]. High Voltage Apparatus, 2014, 50(10): 102-106.
[11] 王旭红, 李浩, 樊绍胜, 等. 基于改进SSD的电力设备红外图像异常自动检测方法[J]. 电工技术学报, 2020, 35(增刊1): 302-310.
Wang Xuhong, Li Hao, Fan Shaosheng, et al.Infrared image anomaly automatic detection method for power equipment based on improved single shot multi box detection[J]. Transactions of China Electrotechnical Society, 2020, 35(S1): 302-310.
[12] Jin Lijun, Tian Zhiren, Ai Jianyong, et al.Condition evaluation of the contaminated insulators by visible light images assisted with infrared information[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(6): 1349-1358.
[13] 艾建勇, 金立军. 基于紫外图像的接触网棒瓷绝缘子污秽状态检测[J]. 电工技术学报, 2016, 31(10): 112-118.
Ai Jianyong, Jin Lijun.Rod porcelain insulator filth state detection of catenary based on ultraviolet image[J]. Transactions of China Electrotechnical Society, 2016, 31(10): 112-118.
[14] 黄新波, 杨璐雅, 张烨, 等. 基于图像增强的瓷质绝缘子灰密程度检测方法[J]. 电力系统自动化, 2018, 42(14): 151-157.
Huang Xinbo, Yang Luya, Zhang Ye, et al.Image enhancement based detection method of non-soluble deposit density levels of porcelain insulators[J]. Automation of Electric Power Systems, 2018, 42(14): 151-157.
[15] 陈宏达. 高光谱遥感图像的降维与分类研究[D]. 上海: 复旦大学, 2013.
[16] 童庆禧, 张兵, 郑兰芬. 高光谱遥感: 原理、技术与应用[M]. 北京: 高等教育出版社, 2006.
[17] Shi Chaoqun, Zeng Haolun, Guo Yujun, et al.Surface roughness detection of roof insulator based on hyperspectral technology[J]. IEEE Access, 2020, 8: 81651-81659.
[18] 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.
[19] 张血琴, 张玉翠, 郭裕钧, 等. 基于高光谱技术的复合绝缘子表面老化程度评估[J]. 电工技术学报, 2021, 36(2): 388-396.
Zhang Xueqin, Zhang Yucui, Guo Yujun, et al.Aging degree evaluation of composite insulator based on hyperspectral technology[J]. Transactions of China Electrotechnical Society, 2021, 36(2): 388-396.
[20] 王艳斌, 袁洪福, 陆婉珍. 一种基于目标因子分析的模型传递方法[J]. 光谱学与光谱分析, 2005, 25(3): 398-401.
Wang Yanbin, Yuan Hongfu, Lu Wanzhen.A new calibration transfer method based on target factor analysis[J]. Spectroscopy and Spectral Analysis, 2005, 25(3): 398-401.
[21] 李庆波, 张广军, 徐可欣, 等. DS算法在近红外光谱多元校正模型传递中的应用[J]. 光谱学与光谱分析, 2007, 27(5): 873-876.
Li Qingbo, Zhang Guangjun, Xu Kexin, et al.Application of DS algorithm to the calibration transfer in near-infrared spectroscopy[J]. Spectroscopy and Spectral Analysis, 2007, 27(5): 873-876.
[22] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 22707—2008 直流系统用高压绝缘子的人工污秽试验[S]. 北京: 中国标准出版社, 2009.
[23] 芦永军, 曲艳玲, 宋敏. 近红外相关光谱的多元散射校正处理研究[J]. 光谱学与光谱分析, 2007, 27(5): 877-880.
Lu Yongjun, Qu Yanling, Song Min.Research on the correlation chart of near infrared spectra by using multiple scatter correction technique[J]. Spectroscopy and Spectral Analysis, 2007, 27(5): 877-880.
[24] 刘晓旭. 基于不同预处理方法的小麦叶片氮素含量的高光谱估测[D]. 泰安: 山东农业大学, 2018.
[25] 刘娇, 李小昱, 郭小许, 等. 不同品种间的猪肉含水率高光谱模型传递方法研究[J]. 农业工程学报, 2014, 30(17): 276-284.
Liu Jiao, Li Xiaoyu, Guo Xiaoxu, et al.Transfer method among water content detection models for different breeds of pork by hyperspectral imaging technique[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(17): 276-284.
[26] 黄承伟, 戴连奎, 董学锋. 结合SNV的分段直接标准化方法在拉曼光谱模型传递中的应用[J]. 光谱学与光谱分析, 2011, 31(5): 1279-1282.
Huang Chengwei, Dai Liankui, Dong Xuefeng.The application of piecewise direct standardization with SNV in calibration transfer of Raman spectra[J]. Spectroscopy and Spectral Analysis, 2011, 31(5): 1279-1282.
[27] 吴广宁, 袁海满, 宋臻杰, 等. 基于粗糙集与多类支持向量机的电力变压器故障诊断[J]. 高电压技术, 2017, 43(11): 3668-3674.
Wu Guangning, Yuan Haiman, Song Zhenjie, et al.Fault diagnosis for power transformer based on rough set and multi-class support vector machine[J]. High Voltage Engineering, 2017, 43(11): 3668-3674.
[28] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 26218.1—2010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[S]. 北京: 中国标准出版社, 2011.