AUTHOR=Sun Zhiqing , Xuan Yi , Huang Yi , Cao Zikai , Zhang Jiansong 

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

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1272095

DOI=10.3389/fenrg.2023.1272095

ISSN=2296-598X

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