The Method of Improving the Performance of Network Analysis Application for the Whole Power Grid

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.

Publisher

IOP Publishing

Subject

General Engineering

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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3