[1]杨纯平,张文斌,周年荣,等.时间序列特征提取的高压输电线电压等级识别方法[J].机械与电子,2017,(12):47-50.
　YANG Chunping,ZHANG Wenbin,ZHOU Nianrong,et al.Voltage Level Identification for High Voltage Transmission Line Based onTime Series Feature Extraction[J].Machinery & Electronics,2017,(12):47-50.
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时间序列特征提取的高压输电线电压等级识别方法
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2017年12期
页码:
47-50
栏目:
自动控制与检测
出版日期:
2017-12-15

文章信息/Info

Title:
Voltage Level Identification for High Voltage Transmission Line Based on Time Series Feature Extraction
文章编号:
1001-2257(2017)12-0047-04
作者:
杨纯平1; 张文斌1; 周年荣2; 唐立军2
(1.昆明理工大学机电工程学院,云南 昆明 650504; 2.云南电网责任有限公司电力科学研究院,云南 昆明 650011)
Author(s):
YANG Chunping1; ZHANG Wenbin1; ZHOU Nianrong2; TANG Lijun2
(1.Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500,China; 2. Yunnan Power Grid Electric Power Research Institute of LLC, Kunming 650011, China)
关键词:
时间序列;  特征提取;  高压输电线;  电压等级识别;  SVM
Keywords:
sequentially;  feature extraction;  high voltage transmission line;  voltage level identification;  SVM
分类号:
TM769
文献标志码:
A
摘要:
不同电压等级的工频高压输电线安全距离不同,登塔过程中电压等级的识别对电力施工人员具有重要的安全意义。根据输电线路不同电压等级电场分布不同的特点,在10 kV、35 kV及110 kV三种输电线路攀爬铁塔过程中,佩戴电场传感器分别采集多组电场强度数据。将其定义为电场时间序列,先利用Kalman滤波算法对数据进行滤波,再利用分段聚合近似表示法进行特征提取。利用SVM分类器进行电压等级识别,结果表明识别正确率为93.3%。
Abstract:
As the safety distance of the high voltage transmission line varies between different voltage levels, it is of vital importance for the electric construction personnel to identify the voltage level when climbing the tower. For the voltage levels and the electric field distribution of the transmission line are different, the electric field sensors were used respectively to collect the data of electric field intensity in the process of tower climbing of 10 kV, 35 kV and 110 kV transmission lines. Defined as time series of electric field, the data was filtered by Kalman filter algorithm, and then, the features were extracted by using the method of piecewise aggregation approximation. Finally, the voltage level was identified by SVM classifier. The results show that the identification accuracy is 93.3%.

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备注/Memo

备注/Memo:
收稿日期:2017-09-26
基金项目:中国南方电网有限责任公司科研资助项目(2016KF00035)
作者简介:杨纯平(1992-),男,云南凤庆人,硕士研究生,研究方向为嵌入式系统; 张文斌(1976-),男,云南昆明人,博士后,副教授,研究方向为测控技术及仪器,智能传感器。
更新日期/Last Update: 2017-12-25