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Real-Time Hierarchical Neural Network Based Fault Detection and Isolation for High-Speed Railway System Under Hybrid AC/DC Grid

Reliable and comfortable high-speed railway (HSR) has skyrocketed in popularity as a transportation medium for traveling around the world. High-voltage direct current (HVDC) electrification system has been introduced to the HSR gradually. However, the coexistence of AC and DC systems will last for a long time because AC railway systems are still in the dominant position. A detailed HSR traction system transient model operating under the hybrid AC/DC grid was established in PSCAD/EMTDC. We proposed a real-time fault detection and isolation (FDI) method for the simulated model using neural network (NN). Hierarchical structure of the monitoring system has been employed. Low-level sub-monitors supervised the conditions of their local regions and the top-level monitor collected all the feedback from sub-monitors making the final evaluation of the entire HSR system based on a voting strategy. Both off-line and real-time experiments were conducted to validate the effectiveness of the proposed method. In the experiments, the sub-monitors were designed based on Gated Recurrent Unit (GRU) algorithm and implemented on the Xilinx VCU128 FPGA board. For the off-line experiment, the sub-monitors used the training and testing dataset both from PSCAD/EMTDC to construct the architecture of their individual GRU networks and to verify how great the networks can be. For the real-time task, the sub-monitors interfaced with a real-time HSR system emulator running on the Xilinx VCU118 FPGA board to test the performance in the real-time application. The results proved that our proposed FDI method has the capability of real-time detection and can achieve better accuracy within reasonable time and resource consumption than other NN-based methods. Moreover, the method was capable of standing against noises from measured signals to some extent.