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Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach

机译:基于图的迭代法与顶点收缩法相结合的潮流分析

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Compared with relational database (RDB), graph database (GDB) is a more intuitive expression of the real world. Each node in the GDB is a both storage and logic unit. Since it is connected to its neighboring nodes through edges, and its neighboring information could be easily obtained in one-step graph traversal. It is able to conduct local computation independently and all nodes can do their local work in parallel. Then the whole system can be maximally analyzed and assessed in parallel to largely improve the computation performance without sacrificing the precision of final results. This paper firstly introduces graph database, power system graph modeling and potential graph computing applications in power systems. Two iterative methods based on graph database and PageRank are presented and their convergence are discussed. Vertex contraction is proposed to improve the performance by eliminating zero-impedance branch. A combination of the two iterative methods is proposed to make use of their advantages. Testing results based on a provincial 1425-bus system demonstrate that the proposed comprehensive approach is a good candidate for power flow analysis.
机译:与关系数据库(RDB)相比,图数据库(GDB)是现实世界中更直观的表达。 GDB中的每个节点都是存储和逻辑单元。由于它通过边缘连接到其相邻节点,因此可以通过一步图遍历轻松获得其相邻信息。它能够独立进行本地计算,并且所有节点都可以并行执行其本地工作。然后,可以最大程度地并行分析和评估整个系统,以在不牺牲最终结果精度的情况下大大提高计算性能。本文首先介绍了图形数据库,电力系统图形建模和潜在的图形计算在电力系统中的应用。提出了两种基于图数据库和PageRank的迭代方法,并讨论了它们的收敛性。提出了顶点收缩以通过消除零阻抗分支来提高性能。提出了两种迭代方法的组合以利用它们的优点。基于省级1425总线系统的测试结果表明,所提出的综合方法非常适合进行潮流分析。

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