A Fault Location Estimation Technique for Catenary Line Branch Sections Using a Multiple Decision-Tree Learning Model and Direction Vector

This paper presents a technique in which a learning model, after training directly on data, discriminates the fault zone and estimates the fault location in the branch section of an AC power supply system. However, the conventional method has a limitation in that its performance degrades as the fault point approaches the branch point. The proposed technique feeds a multi-decision-tree model with the current distribution ratio — corrected for measurement-magnification error — together with a direction vector defined relative to the branch point, allowing the model to determine the fault location on its own. The training and evaluation dataset was constructed by interpolating fault data simulated with PSCAD/EMTDC. Compared with the conventional method, the proposed technique improved the discrimination accuracy by 18.9 percentage points and the average location error by 13m within 50m of the branch point, and by 23.2 percentage points and 32m, respectively, in the 50–100m range, showing substantial performance gains in both regions.