Abstract: In urban rail transit construction, establishing a low-voltage power distribution system is essential to ensure operational order and safety stability at subway stations. This paper analyzes challenges in fault monitoring technology for subway station low-voltage power distribution systems and proposes optimization strategies. The solutions emphasize developing standardized unified data collection mechanisms for multi-source heterogeneous data, improving BP neural network-based intelligent fault diagnosis methods, implementing hierarchical early-warning systems with dynamic prediction capabilities, and optimizing automatic fault circuit localization. Using a case study from a station on Metro Line 2, validation results demonstrate that optimized fault monitoring technology significantly enhances fault detection accuracy while substantially reducing false alarm rates, thereby improving system reliability and maintenance efficiency. These findings provide valuable technical references for future development and intelligent operation of low-voltage power distribution systems in urban rail transit networks.
Keywords: subway station; low-voltage power distribution; electrical automation; fault monitoring; edge computing; intelligent diagnosis