Data-Driven Identification of Active Distribution Network-to-Customer Transformer Relationships: A Power Active Admittance Regression Method

Accurate identification of customer transformer relationships in distribution sub-zones is a fundamental prerequisite for the refined management of low-voltage distribution networks and the integration of distributed generation sources. Addressing current issues such as missing records, non-standard wiring and unclear boundaries between multiple sub-zones, this paper proposes an identification method based on the Power Admittance Regression Algorithm (PARA). Based on the fundamental laws of electrical circuits, this method constructs a regressible model of the linear relationship between the total admittance at the transformer end and the admittances at each consumer end. By utilising electrical data collected simultaneously from smart metres and distribution transformer terminals, it formulates the identification of consumer transformer relationships as a problem of minimising regression residuals. For three typical operating conditions—pure residential load, mixed residential and commercial load, and photovoltaic connection at the feeder terminus—constrained least-squares regression models and binary regression models incorporating PV variables were established respectively; ridge regression regularisation was introduced to suppress multicollinearity and enhance model robustness. Simulation tests were conducted using a dataset comprising 150 consecutive time sections and 70 test nodes (of which 60 were customers within the local substation area and 10 were interference nodes from other substation areas) for validation. The results indicate that, under the three conditions described above, in engineering simulations accounting for three-phase imbalance, random perturbations in line parameters and measurement noise, the average accuracy of this method, as determined by 100 Monte Carlo simulations, was 86.2 percent, 92.8 percent and 93.1 percent respectively, with standard deviations ranging from 1.6% to 1.9%, thereby validating its effectiveness and superiority in scenarios involving complex load structures and the integration of renewable energy. As this work is based on simulation data, further online validation using actual feeder data from electricity consumption data acquisition systems is required.