Abstract
Many urban rail transit (URT) systems adopt the DC traction power supply system. Because of the impedance and incomplete ground insulation of the running track, it is inevitable for a part of the traction current to flow into the ground from the track, creating the stray current. This type of current causes great safety hazards to the metal structures in and near the URT system. Considering the power supply mode of the URT, this paper explores the different resistances in each power supply section under unilateral power supply and bilateral power supply. Then, the defects of the current discharge method were identified in the context of stray current protection. To solve these defects, the backpropagation neural network (BPNN) was adopted to build a discharge flow prediction model. On this basis, an intelligent current monitoring system was established for the URT. Finally, the authors simulated the impact of each factor on stray current, and verified the reliability and stability of the proposed monitoring system. Compared with predicted values and the actual values, the prediction agrees with the actual data very well.
Highlights
With the boom in urban rail transit (URT), the stability and safety of the URT system have become a major concern
The authors identified the defects of the current discharge method for stray current protection, and built a discharge flow prediction model based on the backpropagation neural network (BPNN)
According to the simulation results, the effects of power supply modes on track potential and stray current are presented in Figures 4 and 5, respectively
Summary
With the boom in urban rail transit (URT), the stability and safety of the URT system have become a major concern. Because of the impedance and incomplete ground insulation of the running track, it is inevitable for a part of the traction current to flow into the ground from the running track, and back to the track and the substation This part of current is called the stray current [1]. The authors identified the defects of the current discharge method for stray current protection, and built a discharge flow prediction model based on the backpropagation neural network (BPNN). On this basis, an intelligent current monitoring system was established for the URT, and verified through example analysis
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