Pseudo-Measurement Enhancement in Power Distribution Systems

TL;DR

This paper introduces a low-rank tensor completion model using CPD to improve the quality of measurement data in smart power distribution networks, addressing missing data issues for enhanced system stability.

Contribution

It develops a novel CPD-based tensor completion method tailored for power distribution data, improving accuracy and efficiency over traditional techniques.

Findings

Higher completion accuracy demonstrated in case studies

Reduced computational time compared to traditional methods

Lower memory usage in data reconstruction

Abstract

With the rapid development of smart distribution networks (DNs), the integrity and accuracy of grid measurement data are crucial to the safety and stability of the entire system. However, the quality of the user power consumption data cannot be guaranteed during the collection and transmission process. To this end, this paper proposes a low-rank tensor completion model based on CANDECOMP/PARAFAC decomposition (CPD-LRTC) to enhance the quality of the measurement data of the DNs. Firstly, the causes and the associated characteristics of the missing data are analyzed, and a third-order standard tensor is constructed as a mathematical model of the measurement data of the DN. Then, a completion model is established based on the characteristics of measurement data and the low rank of the completion tensor, and the alternating direction method of multipliers (ADMM) is used to solve it iteratively. Finally, the proposed model is verified through two case studies, the completion accuracy, the computational efficiency, and the memory usage are compared to traditional methods.