Research on Abnormal Risk Identification of Safe Power Consumption for Active Customers Based on Cluster Analysis

Aiming at the complexity of abnormal risk identification of active customers' safe power consumption, this paper proposed a clustering analysis method for abnormal risk identification of active customers' safe power consumption. Firstly, the basic composition of the proposed method is depicted which includes feature extraction and clustering recognition. Then, the specific three-step implementation of the proposed method is described, which is composed of obtaining typical features, normalizing typical features to map three-dimensional feature space, and classifying risk states by k-means cluster analysis. Finally, the case analysis is carried out by using daily-load curve data of charging-pile active customers as dataset. Compared with the comparative analytic hierarchy process algorithm, the experimental results illustrate that the proposed method can effectively identify the abnormal risk of safe power consumption of charging-pile active users which may further improve the risk management and control strategy of abnormal risk of safe power consumption for active customers.