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Fault localization and identification method based on dual-stream multi-task network
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DOI：10.1016/j.ijepes.2025.111411.png)
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Abstract
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• Proposes a dual-stream network fusing temporal and impedance-weighted spatial cues. • Training and evaluation includes high-impedance faults, yielding inherent robustness to such events. • Improves classification by 0.97% identification, and localization by 5.35% over classical model on IEEE-33. • Achieves high accuracy using measurement from only 16 buses. • Exhibits topology generalization and superior speed over metaheuristic methods.
Keywords:
Distribution network
Multi-task learning
Fault location
Spatio-temporal graph neural network
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