摘要
绝缘子是输电线路中的重要部件,尤其是在特高压线路中发挥支撑和绝缘的重要作用。然而,随着各种工业粉尘、烟尘等污秽在绝缘子表面附着堆积,引起绝缘子的绝缘性能下降,易诱发输电线路污秽闪络,造成重大损失,因此,对输电线路绝缘子污秽等级的判别是输电系统安全可靠运行的一项重要任务。激光诱导击穿光谱技术(LIBS)是一种元素原位快速分析技术,具备无需取样、现场分析及远程遥测等特点。以人工污秽为分析对象,开展了玻璃绝缘子表面污秽样品等级的LIBS原位远程遥测判别方法研究。基于远程LIBS新装置,在测试距离2 m,激光器输出能量50 mJ,激光器频率20 Hz,光谱仪积分时间2.0 s,延时时间2.0μs的工作条件下,实现了对人工污秽中Mg,Si,Al,Ca,Na等元素的定性分析,污秽中Na元素谱线强度与污秽等值盐密之间呈现良好的线性关系。基于所获得LIBS光谱中Mg、Na、Al等代表性盐类的特征谱线,以主成分分析(PCA)和K-近邻算法(KNN)算法分别完成了对人工污秽等级的聚类判别,其中KNN分类模型准确度为94.4%,精确率为93.7%,模型召回率为96.4%。该研究表明,LIBS技术可实现绝缘子表面污秽多元素成分的远程遥测原位分析,进一步结合机器学习算法可实现对污秽等级的直接判别。本工作可为后续深入研究LIBS技术服务电力行业的原位分析应用,开发针对性的配套分析仪器设备提供方法学支撑。
Insulators are critical components in transmission lines,playing a vital role in supporting and insulating,especially in ultra-high-voltage(UHV)transmission lines.However,the accumulation of industrial dust and other pollutants on the surface of insulators leads to a decline in insulating performance,which can trigger contamination flashover and cause significant damage to the transmission system.Therefore,monitoring the contamination level of transmission line insulators is a key factor in ensuring the safe and reliable operation of the power grid.Laser-induced breakdown spectroscopy(LIBS)is an in situ,rapid elemental analysis technique that offers the advantage of on-site,non-destructive analysis without requiring sample preparation.The remote sensing capability is one of the distinctive strengths of LIBS.In this study,artificial contamination was used as the target for analysis.A novel remote LIBS analyzer was used to conduct remote analysis of the elemental composition and contamination levels of glass insulator surfaces.A novel in-situ,rapid analytical method for determining the contamination level of insulators using remote LIBS was established.In the experimental process,under working conditions of a 2-meter testing distance,a laser energy output of 50mJ,a laser frequency of 20Hz,an integration time of 2.0seconds,and a delay time of 2.0μs,effective qualitative determination of elements such as Mg,Si,Al,Ca,and Na in artificial contamination was achieved.The quantitative analysis revealed a good linear relationship between the Na intensity in the contamination and the equivalent salt density.This indicates that the remote LIBS analyzer has a strong spectral response to Na in the contamination.Using the characteristic spectrall ines of soluble salts,such as Mg and Na,obtained from the LIBS spectra,principal component analysis(PCA)and K-nearest neighbors(KNN)algorithms were employed to cluster and distinguish contamination levels effectively.The KNN classification model achieved an accuracy of 94.4%,aprecision of 93.7%,and a recall of 96.4%,demonstrating its high effectiveness in identifying contamination levels.This study demonstrates that remote LIBS can achieve in-situ multi element analysis of contamination on insulator surfaces.Combined with machine learning,it enables direct recognition of contamination levels.This provides methodological support for the further development of LIBS technology in power industry applications and the development of targeted analysis equipment for future in-situ applications.
作者
管子然
胡聪
石俏
吴慧峰
何文峰
GUAN Zi-ran;HU Cong;SHI Qiao;WU Hui-feng;HE Wen-feng(Foshan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Foshan 528000,China)
出处
《光谱学与光谱分析》
北大核心
2025年第9期2563-2568,共6页
Spectroscopy and Spectral Analysis
基金
创新科技资金项目(GDKJXM20230903)资助。
关键词
激光诱导击穿光谱
远程遥测
污秽等级
绝缘子
元素分析
Laser-induced breakdown spectroscopy
Remote analysis
Insulator
Contamination grades
Elemental analysi
