电力建设2017，Vol.38Issue(5)：105-110,6.DOI:10.3969/j.issn-1000-7229-2017-05-014
基于时间序列提取和维诺图的电力数据异常检测方法
Outlier Detection Method Based on Compressed Time Series and Voronoi Diagram for Power Data
摘要
Abstract
The deep integration of information system and physical system made power system easily affected by outlier data, while the existing outlier detection methods for power system didn`t take the advantages of data features, and had problems such as heavy computation, bad flexibility and low precision, etc.This paper proposes an outlier detection method based on compressed time series and Voronoi diagram, which adopts the time series extraction method of important points section to reduce the dimension of data in power system, map it to a two-dimensional plane, construct the Voronoi diagram partition, and then detect the abnormal data.This method can reduce the data dimension and algorithm complexity, set anomaly threshold according to the sequence features flexible, and realize the accurate detection of abnormal data.The simulation results have verified the effectiveness of the proposed method.关键词
时间序列/维诺图/异常检测/电力数据Key words
time series/Voronoi diagram/outlier detection/power data分类
信息技术与安全科学引用本文复制引用
裴湉,齐冬莲..基于时间序列提取和维诺图的电力数据异常检测方法[J].电力建设,2017,38(5):105-110,6.基金项目
国家高技术研究发展计划项目(2015AA050202) （2015AA050202）
国家自然科学基金项目(U1509218) Project supported by the National High Technology Research and Development of China (2015AA050202) （U1509218）
National Natural Science Foundation of China(U1509218) （U1509218）
