微型电脑应用2026，Vol.42Issue(3)：61-65,5.
基于DBSCAN算法的电力自动化计量异常数据点剔除
Abnormal Data Point Elimination of Power Automation Measurement Based on DBSCAN Algorithm
摘要
Abstract
The power automation measurement data is easy to be disturbed by noise,which makes it difficult to identify abnor-mal data.Therefore,this paper presents an abnormal data point elimination method of power automation measurement based on density based spatial clustering of application with noise(DBSCAN)algorithm.The characteristics of power automation meas-urement data are extracted and the time series is reordered.The local density and minimum distance of power automation meas-urement data point are calculated,and abnormal measurement data points are judged and eliminated.Experimental results show that the proposed method can accurately detect abnormal data point of power automation measurement and has good effect in elimination.关键词
DBSCAN算法/电力自动化计量数据/局部离群因子算法/数据点剔除/异常检测Key words
DBSCAN algorithm/power automation measurement data/local outlier factor algorithm/data point elimination/abnormal detection分类
信息技术与安全科学引用本文复制引用
董新微,王超,江蒗,胡志亮,黄心心..基于DBSCAN算法的电力自动化计量异常数据点剔除[J].微型电脑应用,2026,42(3):61-65,5.基金项目
国家电网公司资助项目(2022B0505020006) （2022B0505020006）
