An Abnormal Detection of Positive Active Total Power Based on Local Outlier Factor

Abstract

With the construction of smart grids, power companies have collected a lot of power data. Positive active total power is an important parameter in power data, and the abnormal detection of positive active total power is of great significance to the smooth operation of the power system. Aiming at the anomaly detection problem of positive active total power, this paper proposes an abnormal detection method based on local outlier factor. First, the sample data is preprocessed to eliminate invalid values, and then Lagrangian interpolation is used to fill in the missing values. Next the data is divided into 4 groups according to different equipment, and local outlier factors are used to detect abnormalities in the sample data. Experiments on real data sets show that the proposed method has good results.