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.
@article{arxiv.1809.01415,
title = {Exploration of Bi-Level PageRank Algorithm for Power Flow Analysis Using Graph Database},
author = {Chen Yuan and Yi Lu and Kewen Liu and Guangyi Liu and Renchang Dai and Zhiwei Wang},
journal= {arXiv preprint arXiv:1809.01415},
year = {2018}
}
Comments
7 pages, 6 figures, 3 tables, 2018 IEEE International Congress on Big Data. arXiv admin note: text overlap with arXiv:1809.01398