With the continuous development of smart grid

the amount of data generated by power equipment is gradually increasing. How to use power data becomes the key to the development of power grid. In order to ensure the accuracy of power data and detect and process abnormal data quickly at the edge

a detection method for power data anomaly based on CFSFDP algorithm is proposed. Based on the hypothesis of CFSFDP

the sample points with low local density and far away from high density points are defined as outliers

and a new strategy of automatically selecting outliers based on the k values before and after is used to solve the problem of subjective factors in manual selection. The comparison with DBSCAN and LOF shows that the proposed method can quickly and efficiently find the outliers in power data

and is suitable for outlier detection of edge power data.