基于自动聚类模型的输电线路外力破坏预警预测

马大燕

doi:10.11959/j.issn.1000-0801.2019053

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基于自动聚类模型的输电线路外力破坏预警预测

电力信息化专栏 | 更新时间：2024-06-05

- 基于自动聚类模型的输电线路外力破坏预警预测
- Early warning prediction of external force destruction in transmission lines based on automatic clustering model
- 电信科学 2019年35卷第3期页码：135-139
- 作者机构：
- 作者简介：
  
  [ "马大燕（1984- ），女，博士，国网电子商务有限公司工程师，主要从事分布式电源与微电网、能源互联网技术方面的研究工作。" ]
- 基金信息：
  
  国家电网公司科技项目（国网分布式光伏云网深化研究与应用）;Science and Technology Project of State Grid Corporation of China (Deepening Research and Application of PVCloud)
- DOI：10.11959/j.issn.1000-0801.2019053
  中图分类号： TM715
- 网络出版日期：2019-03，
  
  纸质出版日期：2019-03-20
- 稿件说明：
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马大燕. 基于自动聚类模型的输电线路外力破坏预警预测[J]. 电信科学, 2019,35(3):135-139.

Dayan MA. Early warning prediction of external force destruction in transmission lines based on automatic clustering model[J]. Telecommunications science, 2019, 35(3): 135-139.
马大燕. 基于自动聚类模型的输电线路外力破坏预警预测[J]. 电信科学, 2019,35(3):135-139. DOI： 10.11959/j.issn.1000-0801.2019053.

Dayan MA. Early warning prediction of external force destruction in transmission lines based on automatic clustering model[J]. Telecommunications science, 2019, 35(3): 135-139. DOI： 10.11959/j.issn.1000-0801.2019053.

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。

Abstract

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.

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