Research on Intelligent Identification of New Grid-Connected Entities in Distribution Networks Under Limited Measurement Conditions

To identify the growing number of new entities connected to low-voltage distribution networks when field measurements are limited, this study proposes an intelligent identification method based on tabular in-context learning (TabICL). An IEEE 33-bus distribution-network model was constructed in PowerFactory to simulate five scenarios: load only, load with EV charging piles, load with PV, load with wind turbines, and load with both PV and EV charging piles. The resulting dataset comprised 96-point daily active-power curves. TabICL captured dependencies across time points, and its performance was directly compared with that of SVM, random forest, XGBoost, and MLP using identical training and test sets and evaluation metrics. TabICL achieved an accuracy of 92.50% and a macro-averaged F1 score of 0.9386, exceeding the four baselines by 15.00–17.50 percentage points in accuracy and by 0.1990–0.3075 in macro-averaged F1. Its accuracy remained 0.8888 at 40 dB; however, the pronounced degradation at lower signal-to-noise ratios identifies noise robustness as a limitation of the current method. These simulation results demonstrate the feasibility of identifying new grid-connected entities under limited measurement conditions, while validation with measurements from operating distribution networks remains necessary.