Traceability analysis for low-voltage distribution network abnormal line loss using a data-driven power flow model

Frontiers in Energy Research

Energy Research

Zhiqing Sun¹  Yi Xuan¹  Zikai Cao²  Yi Huang³  Jiansong Zhang⁴ 

[1] Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Company, Hangzhou, China;School of Electrical Power Engineering, Shanghai University of Electric Power, Shanghai, China;State Grid Corporation of China, Beijing, China;State Grid Zhejiang Electric Power Company, Ltd., Hangzhou, China;

关键词: traceability analysis;    abnormal line loss;    data-driven power flow model;    backpropagation algorithm;    low-voltage distribution network;   

DOI  :  10.3389/fenrg.2023.1272095

 received in 2023-08-07, accepted in 2023-08-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

The abnormal behavior of end-users is one of the main causes of abnormal line loss in distribution networks. The integration of a large amount of distributed renewable energy into a low-voltage distribution network (LVDN) complicates line loss analysis. Traceability analysis for abnormal line loss aims to identify the specific end-user responsible for the anomaly in line loss. This paper proposes, for LVDNs with incomplete topology and line parameters, a practical traceability analysis approach using a data-driven power flow model. A data-driven power flow model based on a neural network is first established to capture the power flow mapping relationship without topology and line parameter information. A backpropagation algorithm is then presented to correct the actual power consumption data according to the measured voltage data. By comparing actual power consumption data with measured power data, users with abnormal behavior can be accurately identified and tracked. Finally, the effectiveness of the proposed approach is verified by actual data.

【 授权许可】

Unknown   
Copyright © 2023 Sun, Xuan, Huang, Cao and Zhang.