Faulty Feeder Identification for Single-Phase-to-Ground Faults in Small-Current Grounded Systems Based on an Adaptive Transient Window and Robust Multi-Feeder Graph Consistency

Abstract

Accurate faulty feeder identification is essential for the safe operation of small-current grounded distribution networks under single-phase-to-ground faults, yet existing transient-based methods often rely on preset data windows and insufficiently exploit the collective consistency of healthy feeders. This study proposes a training-free method coupling adaptive transient window selection with robust multi-feeder graph consistency analysis. Its novelty is the joint determination of which post-fault interval should be retained and which feeder deviates from the healthy feeder group. Zero-sequence current variations are obtained using one-cycle-earlier pre-fault reference waveforms, and an effective analysis window is selected within a preset maximum acquisition interval according to the cumulative transient energy ratio. The normalized current segments are then mapped to a weighted similarity graph, and a fused anomaly index based on average connection strength and median edge weight is used for feeder selection. A 110/10 kV six-feeder MATLAB/Simulink system with 180 fault cases was evaluated. With η0=0.98, the method achieved 98.89% accuracy with an average selected analysis-window length of 29.62 ms; accuracies under 10 and 5 dB noise were 96.17% and 91.78%, respectively. It also outperformed Haar wavelet energy and Wasserstein distance baselines in accuracy and normalized separation. These results support its effectiveness as a physically interpretable, training-free feeder identification framework.