A Data-driven Method of Users-transformer Relationship Identification in the Secondary Power Distribution System

In secondary power distribution systems, the record of the relationship between users and the transformers may not be updated simultaneously after the wiring changes due to the elimination of the faults or other reasons. Manual testing requires special equipment and takes time. The paper proposes a method to detect the relationship between users and the transformers based on the data collected by the smart meter. Firstly, the method extracts the high-frequency features from the current time series data by frequency spectral analysis. Then it creates the mathematic model based on Kirchhoff's law of current. Finally, it solves the model with mixed integer linear programming (MILP) and achieves the result of the relationship between users and transformers. The data used in the paper is collected from local electricity utilities and the case study shows that using the proposed method can successfully identify the relationship between users and transformers with current time series data.