Exploration of Bi-Level PageRank Algorithm for Power Flow Analysis Using Graph Database
This work introduces a novel graph database-based approach for power flow analysis, potentially improving computational efficiency for large-scale power systems.
The paper explores power system modeling using graph databases and proposes a bi-level PageRank algorithm for power flow analysis, achieving high-performance parallel computation. Tests on cases up to 1,079,000 nodes and a real provincial system with 1,425 buses demonstrate the method's effectiveness.
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 bilevel PageRank algorithm is developed from PageRank algorithm and Gauss Seidel methodology realize high performance parallel computation. MP 10790 case, and its extensions, MP 107900 and MP 1079000, 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 effectiveness in real world.