输电线路电磁环境试验数据清理方法研究
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摘要
在电磁环境试验过程中,由于周围环境会出现各种突发性干扰,因此试验数据中常常存在异常数据,需要恰当地处理方法清理异常数据。为此,针对电磁环境试验数据的自动清理方法开展研究,采用离群点空间距离实现异常值判别,根据电磁环境数据特性,提出了适合于电磁环境数据的空间距离定义方法。提出了基于聚类的电磁环境试验数据清理方法,通过对空间离群点的检测完成对电磁环境试验数据的预处理过程。结果表明,得到的处理后数据中,异常数据均得到了清除,且未改变数据的统计特征值,其中异常数据主要表现为各个数据子类中的空间离群点;该算法可提高测量数据的可信度。
In the process of testing electromagnetic environment,outliers in the data sheet always need to clean because of the burst interference occurring in the test environment.Consequently,we studied an automatic data cleaning method of electromagnetic environment with the outliers detection method based on the distance.Considering the test device,the characteristics of data statistics,and the physical characteristics of the electromagnetic environment,we redefined the method of calculating the spatial distance,and put forward the outlier detection clustering.The preprocessing of the electromagnetic environment test data was finished by this method.The results show that outliers have been cleared in the processed database,not changing the statistical features of the data,and the cleaned data are mainly considered as spatial outliers in each subclass.This algorithm can improve the reliability of measurement data.
In the process of testing electromagnetic environment,outliers in the data sheet always need to clean because of the burst interference occurring in the test environment.Consequently,we studied an automatic data cleaning method of electromagnetic environment with the outliers detection method based on the distance.Considering the test device,the characteristics of data statistics,and the physical characteristics of the electromagnetic environment,we redefined the method of calculating the spatial distance,and put forward the outlier detection clustering.The preprocessing of the electromagnetic environment test data was finished by this method.The results show that outliers have been cleared in the processed database,not changing the statistical features of the data,and the cleaned data are mainly considered as spatial outliers in each subclass.This algorithm can improve the reliability of measurement data.
引文
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[18]庄池杰,张斌,胡军,等.基于无监督学习的电力用户异常用电模式检测[J].中国电机工程学报,2016,36(2):379-387.ZHUANG Chijie,ZHANG Bin,HU Jun,et al.Anomaly detection for power consumption patterns based on unsupervised learning[J].Proceedings of the CSEE,2016,36(2):379-387.
[19]HAN J,KAMBER M.Data mining:concepts and techniques[J].Data Mining Concepts Models Methods&Algorithms Second Edition,2006,5(4):1-18.
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[21]CHEN K J,HUANG C W,HE J L.Fault detection,classification and location for transmission lines and distribution systems:a review on the methods[J].High Voltage,2016,1(1):25-33.
[2]伍思霞,徐雅倩,凌平,等.特高压直流输电线环境电/磁场暴露生物非热效应[J].高电压技术,2016,42(11):3689-3696.WU Sixia,XU Yaqian,LING Ping,et al.Non-thermal biological effect of environmental electric or magnetic fields exposure induced by HVDC transmission lines[J].High Voltage Engineering,2016,42(11):3689-3696.
[3]INSTITUTE E P.Bipolar HVDC transmission system study between±600 k V and±1 200 k V:corona studies,phaseⅡ[M].California,USA:Electric Power Research Institute,1982:10-15.
[4]NAKANO Y,FUKUSHIMA M.Statistical audible noise performance of Shiobara HVDC test line[J].IEEE Transactions on Power Delivery,1990,5(1):290-296.
[5]唐剑,何金良,刘云鹏,等.海拔对导线交流电晕可听噪声影响的电晕笼试验结果与分析[J].中国电机工程学报,2010,30(4):105-111.TANG Jian,HE Jinliang,LIU Yunpeng,et al.Test results and analysis of altitude-change effect on audible noise of AC corona occurring on conductors with corona-test cage[J].Proceedings of the CSEE,2010,30(4):105-111.
[6]陆家榆,鞠勇.±800 k V直流输电线路电磁环境限值研究[J].中国电力,2006,39(10):37-42.LU Jiayu,JU Yong.Study on the limits of electromagnetic environment of±800 k V DC transmission lines[J].Electric Power,2006,39(10):37-42.
[7]中国南方电网有限责任公司.±800 k V直流输电技术研究[M].北京:中国电力出版社,2006:56-70.China Southern Power Grid Company Limited.Research on±800 k V DC transmission technology[M].Beijing,China:China Electric Power Press,2006:56-70.
[8]LIU L,LI M,LI R,et al.Influence of meteorological parameters on the EM environment of UHVDC transmission line at high altitude[C]∥Asia-Pacific Symposium on Electromagnetic Compatibility.Singapore:IEEE,2012:968-972.
[9]LI M,ZENG R,YANG D,et al.The influence of wind on the audible noise of ultra HVDC transmission line in high altitude area[C]∥Asia-Pacific Symposium on Electromagnetic Compatibility.Beijing,China:IEEE,2010:1668-1671.
[10]YU Z,ZENG R,LI M,et al.Radio interference of ultra HVDC transmission lines in high altitude region[C]∥Asia-Pacific International Symposium on Electromagnetic Compatibility.Beijing,China:IEEE,2010:1672-1675.
[11]怡勇,张楚岩,王元九,等.导线表面污秽对正极性单电晕点可听噪声频谱特性的影响[J].高电压技术,2016,42(6):1990-1996.YI Yong,ZHANG Chuyan,WANG Yuanjiu,et al.Influence of conductor surface’s contamination on spectrum characteristics of audible noise from single corona point under positive DC voltage[J].High Voltage Engineering,2016,42(6):1990-1996.
[12]LI X B,CUI X,LU T B,et al.Experimental investigation on correlation of corona-induced vibration and audible noise from DC conductor[J].High Voltage,2016,1(3):115-121.
[13]刘元庆,陆家榆,张强,等.高压直流输电线路可听噪声测量数据有效性判定方法[J].高电压技术,2014,40(9):2728-2733.LIU Yuanqing,LU Jiayu,ZHANG Qiang,et al.Effectiveness determination method of audible noise test data for high voltage DC transmission lines[J].High Voltage Engineering,2014,40(9):2728-2733.
[14]TIAN F,YU Z,ZENG R.Radio interference and audible noise of the UHVDC test line under high altitude condition[C]∥Asia-Pacific Symposium on Electromagnetic Compatibility.Singapore:IEEE,2012:449-452.
[15]YU Z,ZENG R,LI M,et al.Tests on electromagnetic environment of ultra HVDC transmission lines in high altitude region[C]∥Asia-Pacific International Symposium on Electromagnetic Compatibility.Beijing,China:IEEE,2010:1676-1680.
[16]李敏,余占清.高海拔±800 k V直流输电线路电磁环境测量[J].南方电网技术,2011,5(1):42-45.LI Min,YU Zhanqing.Electromagentic environment measurement of±800 k V DC transmission lines at high altitude[J].Southern Power System Technology,2011,5(1):42-45.
[17]KNORR E M,NG R T.Algorithms for mining distance-based outliers in large datasets[C]∥Proceedings of the 24th International Conference on Very Large Data Bases.New York,USA:Morgan Kaufmann Publishers Inc.,1998:392-403.
[18]庄池杰,张斌,胡军,等.基于无监督学习的电力用户异常用电模式检测[J].中国电机工程学报,2016,36(2):379-387.ZHUANG Chijie,ZHANG Bin,HU Jun,et al.Anomaly detection for power consumption patterns based on unsupervised learning[J].Proceedings of the CSEE,2016,36(2):379-387.
[19]HAN J,KAMBER M.Data mining:concepts and techniques[J].Data Mining Concepts Models Methods&Algorithms Second Edition,2006,5(4):1-18.
[20]JAMES G,WITTEN D,HASTIE T,et al.An introduction to statistical learning:with applications in R[M].Berlin,German:Springer,2013:234-256.
[21]CHEN K J,HUANG C W,HE J L.Fault detection,classification and location for transmission lines and distribution systems:a review on the methods[J].High Voltage,2016,1(1):25-33.