User Localization Strategy for Abnormal Fluctuations in Distribution Line Losses Based on Energy Flow Analysis of Multiple Data Sources

Driven by the “dual carbon” goal, achieving power grid loss reduction and efficiency improvement has become an important task for the development of the power system, and accurately identifying abnormal users in abnormal line loss areas is a key link to achieving this goal. This article proposes a user localization strategy for abnormal fluctuations in distribution line losses based on energy flow analysis from multiple data sources. This strategy integrates multi-source heterogeneous data such as line loss rate data and user electricity meter data in the substation area, and uses adaptive adjustment of rank difference method to clean and repair the data. By analyzing the energy flow of multiple data sources and combining with least squares regression to deeply analyze the correlation between electricity and abnormal fluctuations in line loss rate, a user contribution evaluation model for line loss anomalies is constructed, and accurate positioning of abnormal users is achieved based on the loss of electricity beyond the limit of line loss. The simulation results show that this strategy can efficiently detect abnormal fluctuations in distribution line losses, accurately identify users who have a significant impact on distribution line loss rates, and provide a reliable and effective method for distribution line loss management. It has the advantages of convenient data collection and scientific and accurate identification principles.