Exploration of Bi-Level PageRank Algorithm for Power Flow Analysis Using Graph Database

Abstract

Compared with traditional relational database, graph database (GDB) is a natural expression of most real-world systems. Each node in the GDB is not only a storage unit, but also a logic operation unit to implement local computation in parallel. This paper firstly explores the feasibility of power system modeling using GDB. Then a brief introduction of the PageRank algorithm and the feasibility analysis of its application in GDB are presented. Then the proposed GDB based bi-level PageRank algorithm is developed from PageRank algorithm and Gauss-Seidel methodology realize high performance parallel computation. MP 10790 case, and its extensions, MP 10790∗10 and MP 10790∗100, are tested to verify the proposed method and investigate its parallelism in GDB. Besides, a provincial system, FJ case which include 1425 buses and 1922 branches, is also included in the case study to further prove the proposed algorithm's effectiveness in real world.