基于自动聚类模型的输电线路外力破坏预警预测
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摘要
外力破坏事件已成为严重威胁架空输电线路安全稳定运行的主要因素,给防御、预警工作带来一定的困难。针对传统的聚类方法聚类中心难以准确确定、易受异常点影响的问题,提出了一种基于自动聚类模型的输电线路外破数据分析方法,对外力破坏数据从时间和空间纬度进行分析。该算法首先通过Canopy算法初始聚类中心,采用削弱不符合正态分布的异常数据权值的思想,利用优化的K-means算法进行聚类处理,最终通过实验分析证明了该算法的有效性及高效性。本文算法能够应用于电力信息系统的GIS模块,实现分析结果的时空可视化,为找到输电线路外力破坏发生原因、进行预警预测提供有力的决策支持。
The external force destruction has become a major threat to the safe and stable operation of overhead transmission lines, bringing difficulties to the defense and early warning work. In order to solve the problem that the traditional clustering center is difficult to accurately determined and susceptible to abnormal points, an automatic clustering method for data analysis work of transmission lines was presented, and external damage data was analyzed from time and space latitude. Firstly, the cluster center was initialized in this method by using Canopy algorithm.Then, the optimized K-means algorithm was used to perform clustering. Finally, the effectiveness of this method was proved by experimental analysis. This method will be applied to the GIS module in the power information system,which can realize the spatio-temporal visualization of the analysis results and provide powerful decision support for finding cause of the external force damage of the transmission line.
The external force destruction has become a major threat to the safe and stable operation of overhead transmission lines, bringing difficulties to the defense and early warning work. In order to solve the problem that the traditional clustering center is difficult to accurately determined and susceptible to abnormal points, an automatic clustering method for data analysis work of transmission lines was presented, and external damage data was analyzed from time and space latitude. Firstly, the cluster center was initialized in this method by using Canopy algorithm.Then, the optimized K-means algorithm was used to perform clustering. Finally, the effectiveness of this method was proved by experimental analysis. This method will be applied to the GIS module in the power information system,which can realize the spatio-temporal visualization of the analysis results and provide powerful decision support for finding cause of the external force damage of the transmission line.
引文
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[2]李天,石鑫,李永倩.基于输电线路监测的无线传感器网络路由优化技术研究[J].电测与仪表,2015,21(10):6-10.LI T,SHI X,LI Y Q.Research on routing optimization technique of wireless sensor networks based on power lines monitoring[J].Electrical Measurement and Instrumentation,2015,21(10):6-10.
[3]胡毅,刘凯,吴田,等.输电线路运行安全影响因素分析及防治措施[J].高电压技术,2014,40(11):3491-3499.HU Y,LIU K,WU T,et al.Analysis of influential factors on operation safety of transmission line and countermeasures[J].High Voltage Engineering,2014,40(11):3491-3499.
[4]巢亚锋,徐志强,段建家,等.架空输电线路防外力破坏智能管控系统设计[J].湖南电力,2017,37(5):18-28.CHAO Y F,XU Z Q,DUAN J J,et al.Design of anti-external damage intelligent control system for overhead transmission lines[J].Hunan Electric Power,2017,37(5):18-28.
[5]冷雪敏,高阳,许傲然.架空输电线路防外力破坏系统研究[J].电子世界,2017(13):45-46.LENG X M,GAO Y,XU A R.Study on anti external force damage system of overhead transmission lines[J].Electronics World,2017(13):45-46.
[6]杨振伟,易伟,陈键.输电线路外力破坏故障的分析与防治[J].吉林电力,2015,43(5):48-50.YANG Z W,YI W,CHEN J.Analysis and countermeasures of damage from outside destroy in transmission line[J].Jilin Electric Power,2015,43(5):48-50.
[7]谢兵,许金彤.基于3G网络的输电线路防外力破坏智能视频监控系统研究[J].华东电力,2010,38(11):1729-1732.XIE B,XU J T.Study of destruction of intel-ligent video surveillance system based on transmission lines against external3G network[J].East China Power,2010,38(11):1729-1732.
[8]许金明,江志蓉.嘉兴电网外力破坏事故和预防措施[J].电力建设,2009,30(7):106-107.XU J M,JIANG Z R.Jiaxing Power Grid’s external damage accidents and preventive measures[J].Electric Power Construction,2009,30(7):106-107.
[9]刘振亚.中国特高压交流输电技术创新[J].电网技术,2013,37(3):566-574.LIU Z Y.Innovation of UHVAC transmission technology in China[J].Power System Technology,2013,37(3):566-574.
[10]王继业.智能电网大数据[M].北京:中国电力出版社,2016.WANG J Y.Big data of smart grid[M].Beijing:China Electric Power Press,2016.
[11]CHEESEMAN P,STUTZ J.Bayesian classification(AutoC-lass):theory and results[M].Cambridge:AAAI/MIT Press,1996:153-180.
[12]LI F H,ZHAO Z F,WANG Z Y.Hierarchical clustering based on single-pass for breaking topic detection and tracking[J].High Technology Letters,2018(4).
[13]SUN J Z,LING B L.Density PSO-based software module clustering algorithm[J].The Journal of China Universities of Posts and Telecommunications,2018(4).