Parallel Power Flow Calculation of Urban Rail Transit Traction Power Supply Network Based on Multi-Process

To solve the problem of high time cost in power flow calculation of urban rail transit traction power supply network, research on using an acceleration algorithm to reduce calculation time was carried out. As the complexity and scale of urban rail transit traction power supply network systems continue to increase, traditional serial calculation methods can no longer meet actual needs. Using parallel algorithms for optimization has become an important solution to improve the speed and efficiency of power flow calculations. In this optimization algorithm, the traction power supply network data in the database is read in batches, and based on the train's running time, the MapReduce model and process pool are used to process and calculate the fragmented data in parallel. Finally, the results are merged and output. Such an optimization algorithm can make full use of the performance of parallel computing devices, such as multi-core CPUs while reducing the time of data transmission and processing, thereby effectively improving the speed and efficiency of power flow calculations. Experimental results show that the optimization effect of using this parallel algorithm is closely related to the number of parallel processes and CPU cores. The more cores are, the more obvious the optimization effect is. In the experiments of this article, the acceleration effect is optimal when the number of CPU cores is equal to the number of processes. The calculation time required after optimization is about 1/6 of that before optimization.