Connectivity Verification in Distribution Systems Using Smart Meter Voltage Analytics: A Cloud-Edge Collaboration Approach

Distribution topology is oftentimes changed to cope with the development of local power load. Therefore, connectivity verification has become a critical task for optimal grid operation. In this article, a novel cloud-edge collaboration approach is presented to identify outlier users and correct connections. In this article, based on the smart meter voltage analytics, an affinity propagation clustering-based local outlier factor (AP-LOF) algorithm is proposed for the voltage outlier identification and verification of the edge transformer. Compared to traditional methods, it can effectively identify the outlier user groups with high internal voltage correlation. Besides, a recommendation mechanism is developed in the cloud center, which repositions the identified outlier users by coordinating the information exchange between the edge transformers and the cloud center. Numerical tests are conducted using the actual smart meter voltage data. The results show that the proposed AP-LOF algorithm exhibits a better performance, which is suitable for the identification of various outlier users. Compared to a centralized architecture, 66% savings in calculation time is achieved by the cloud-edge collaboration approach. It further demonstrates the effectiveness and practicability of the proposed method in terms of identification accuracy and verification efficiency.

Automated summary

A novel cloud-edge collaboration approach is proposed in this article to identify outlier users and correct connections. The AP-LOF algorithm based on smart meter voltage analytics is used for voltage outlier identification and edge transformer verification. The recommendation mechanism in the cloud center facilitates information exchange between edge transformers and the cloud center, resulting in improved efficiency in outlier identification.