Research on Data Mining of Wind Disaster of Power Transmission Line Based on Clustering Analysis

被引:1
作者
Li Peng [1 ]
Liu Bin [1 ]
Cheng Yong-feng [1 ]
Wang Jing-chao [1 ]
Li Dan-yu [1 ]
Yang Jia-lun [1 ]
机构
[1] China Elect Power Res Inst, Power Transmiss & Transformat Engn Res Inst, Beijing, Peoples R China
来源
2019 6TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2019) | 2019年
关键词
Data mining; Clustering; Power transmission line conductor; Wind vibration disaster; Influential factor;
D O I
10.1109/ICISCE48695.2019.00098
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This Paper conducts effective mining to wind vibration disaster data of power transmission line conductor by using the clustering analysis method. The main influential factors which cause different types of wind vibration disasters are analyzed. This paper laying a foundation for the subsequent con-elation analysis and machine learning model. On the basis of K-Means method, the significant degrees of different influential factors on forecast of galloping, wind deviation and sub-span oscillation disaster are studied. The results show that: the span, conductor icing thickness and wind direction are main influential factors of conductor galloping; the included angle between wind and conductor and span are main influential factors of insulator wind vibration; the span, spacer span, and number of spacers are main influential factors of sub span oscillation.
引用
收藏
页码:459 / 463
页数:5
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