Coordinated Cloud-Edge Anomaly Identification for Active Distribution Networks

Abstract

The extensive integration of distributed energy resources transforms the conventional power distribution network into a complex and tightly-coupled cyber-physical system denoted as active distribution network (ADN). In this paper, a data-driven anomaly identification method based on the cloud-edge computing architecture is proposed to reduce the complicated risk of ADN operations. Especially, the proposed method would serve as a widely applicable and efficient line of defense against either cyber or physical anomalies. On one hand, a hierarchical data flow scheme is designed to balance the timeliness and economical requirements of anomaly identification by taking advantage of coordinated cloud-edge computing. On the other hand, an integrated algorithm is proposed on the basis of the proposed data flow architecture in order to identify anomalies with satisfactory robustness and accuracy, even under complex cyber-physical conditions. Results of numerical experiments have validated the computational superiority of the proposed method over the state-of-the-art by simulating a variety of physical faults and cyber attacks.