Author:
He Ming,Lu Yi,Li Jing,Zhang Guofang,Guo Guo
Abstract
Abstract
With the rapid development of UHV AC / DC hybrid power grid, it is required that the Network Analysis Application have the ability of unified analysis and high-performance computing. In this paper, the time-consuming analysis of each sub-function of Network Analysis Application is carried out to realize the performance bottleneck analysis of the whole process of network analysis. It is proposed that the performance of network analysis should be improved in three aspects: data input and output, topology analysis and core computing of Network Analysis Application. In the aspect of data input, the grid model data service is constructed to realize the combination of measurement reading and model reading, and the parallel model verification and boundary equivalence are completed; In the aspect of data output, it can be parallelized by table, block and equipment; In the aspect of topology analysis, the shared memory programming model OpenMP is adopted, and based on the fork/join parallel mode, its parallelization is realized; In the aspect of core computing, the existing parallel computing methods are summarized, and through the actual power grid simulation analysis, it puts forward the parallel computing mode applicable to different scale power grids and different applications. Finally, the effectiveness of this method is verified by comparing the optimized performance of state estimation.
Reference6 articles.
1. Parallel solution method of power flow correction equation for large-scale power grid [J];Zhang;Power System Protection and Control,2017
2. Integrated Node-Branch Computing Model Service of Large Power Grid for Unified Analysis [J];Li;Power System Technology,2017
3. Current Status of High-performance On-line Analysis Computation and Key Technologies for Cooperating Computation [J];Guo;Automation of Electric Power Systems,2018
4. The Latest Development of GPU and Its Prospective Application in Powe System [J];Chen;Electric Power Information and Communication Technology,2018
5. A real-time and reliable dynamic migration model for concurrent taskflow in a GPU cluster [J];Fang;Cluster Computing,2019