Detection of Outlier of Electric Power Data Based on Improved Fast Density Peak Clustering

doi:10.1109/IMCEC46724.2019.8983900

	Detection of Outlier of Electric Power Data Based on Improved Fast Density Peak Clustering
	Hao, Xiaohong; Li, Hang; Zhang, Chunyan; Rui, Chen
	2019-10-01
会议日期	October 11, 2019 - October 13, 2019
会议地点	Chongqing, China
关键词	Anomaly detection Big data Electric losses Electric power transmission networks Information management Petroleum industry Power quality Statistics Cluster centers Density clustering Electric power In-depth analysis Local Outlier Factor Power data Relative density Relative distances
DOI	10.1109/IMCEC46724.2019.8983900
页码	1248-1252
英文摘要	Outlier detection is an important field in the application of electric power big data, and has an important application in depth analysis and mining of electric power data. The detection of outlier of electric power can avoid the fault, eliminate the source of interference of power quality and reduce the loss of power grid. This paper is set in the power big data, aiming at the characteristics of big data of electric power. In this paper, an improved fast density peak clustering is proposed, which introduces the idea of local outlier factor algorithm, and redefines the relative density and relative distance. The identification degree of cluster center with relatively small density in data is improved, and the accuracy of outlier detection is improved. The validity of the algorithm is proved by the simulation experiment on the outlier detection of the load data from an oil company. © 2019 IEEE.
会议录	Proceedings of 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019
会议录出版者	Institute of Electrical and Electronics Engineers Inc., United States
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117926]
专题	电气工程与信息工程学院
作者单位	Lanzhou University of Technology, College of Electrical and Information Engineering, China
推荐引用方式 GB/T 7714	Hao, Xiaohong,Li, Hang,Zhang, Chunyan,et al. Detection of Outlier of Electric Power Data Based on Improved Fast Density Peak Clustering[C]. 见:. Chongqing, China. October 11, 2019 - October 13, 2019.