首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于Adaboost算法的输电线路舞动预警方法
引用本文:李哲,王建,梁允,熊小伏,翁世杰.基于Adaboost算法的输电线路舞动预警方法[J].重庆大学学报(自然科学版),2016,39(1):32-38,97.
作者姓名:李哲  王建  梁允  熊小伏  翁世杰
作者单位:1. 国网河南省电力公司 电力科学研究院,郑州,450052;2. 重庆大学 输配电装备及系统安全与新技术国家重点实验室,重庆,400044
基金项目:重庆市科技攻关(应用重点)项目(cstc2012gg-yyjsB90003);国家电网公司重大基础前瞻科技项目(SG20141187)。
摘    要:输电线路舞动是目前尚未被全面准确认识的世界性难题,已严重威胁输电系统的安全稳定运行。文章分析影响舞动的外界气象环境因素,并在此基础上提出一种基于Adaboost集成学习算法的输电线舞动预警方法。采用基于Gini指标的决策桩作为弱分类器,通过对多个弱分类器的训练及加权求和,输出舞动预测结果及其置信度,可为电网运维人员提供决策支撑。最后,使用历史数据进行验证性实验,结果证明了所提方法的有效性。

关 键 词:输电线  舞动  预警  Adaboost  算法  决策桩
收稿时间:7/5/2015 12:00:00 AM

An early warning method of transmission line galloping based on Adaboost algorithm
LI Zhe,WANG Jian,LIANG Yun,XIONG Xiaofu and WENG Shijie.An early warning method of transmission line galloping based on Adaboost algorithm[J].Journal of Chongqing University(Natural Science Edition),2016,39(1):32-38,97.
Authors:LI Zhe  WANG Jian  LIANG Yun  XIONG Xiaofu and WENG Shijie
Institution:Electric Power Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450052, P.R.China;,State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, P.R.China,Electric Power Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450052, P.R.China;,State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, P.R.China and State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, P.R.China
Abstract:Transmission line galloping is a worldwide problem which has not been fully understood, and it has seriously threatened the safe and stable operation of a transmission system. We investigated the factors of meteorological environment that influence galloping, and proposed an early warning method of transmission line galloping based on the Adaboost ensemble learning algorithm. In this method, the decision stump based on the Gini index is used as the weak classifier. The prediction result and its confidence are obtained by training and weighted summing of multiple weak classifiers, which are helpful information for the decision making of operators and dispatchers of power grids. The effectiveness of the proposed method is proved by the verification experiment with historical data.
Keywords:transmission line  galloping  early warning  Adaboost algorithm  decision stump
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《重庆大学学报(自然科学版)》浏览原始摘要信息
点击此处可从《重庆大学学报(自然科学版)》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号