At present, electric power companys’ judgment on abnomal line loss is that the line loss is abnormal when the line loss rate exceeds a certain threshold. Yet the judgement is one-sidedness and limited. To effectively identify the problem of line loss, based on the study of clustering algorithm and the characteristics of the line loss rate data, an improved k-means clustering algorithm for anomal line loss discrimination is proposed. The method firstly carries out a k-means clustering on the line loss rate of the low voltage substation area to be classified into three classes, then judges whether to carry out secondary classification according to the quantity of various data, and finally judges whether a line loss abnormality exists in the low voltage substation area according to factors such as the size of average line loss rate, and the distance of the clustering center. By analyzing the time dispersion of the class of data with high line loss rate of the clustering results. the degree of abnormality of the line loss can be obtained. Experimental results show that this method has a certain practical application effect and can improve the accuracy of abnormal line loss judgement.