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. INDEX TERMS Urban rail transit (URT), backpropagation neural network (BPNN), stray current, current monitoring system.
Based on electromagnetic and temperature field models of nonlinear ferromagnetic materials, this paper conducts a finite element analysis of the induction heating process of PC steel bar, thus obtaining the change curves of temperature, active power, heating efficiency and power density. The main factors and mechanisms affecting the heating efficiency and the temperature uniformity are analyzed systematically by a combination of the simulation result and the experimental fact. This work is expected to contribute significantly for optimizing the key parameters of induction heating production of PC steel bar.
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