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Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach
Distributed, Parallel, and Cluster Computing · Submitted 5 Sep 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Testing results based on a provincial 1425-bus system demonstrate that the proposed comprehensive approach is a good candidate for power flow analysis.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Compared with relational database (RDB), graph database (GDB) is a more intuitive expression of the real world. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | Each node in the GDB is a both storage and logic unit. | background 0.98 | 0.08 | 0.02 | asserted 1.00 |
| s2 | Since it is connected to its neighboring nodes through edges, and its neighboring information could be easily obtained in one-step graph traversal. | background 0.96 | 0.07 | 0.02 | hedged possibility 0.84 |
| s3 | It is able to conduct local computation independently and all nodes can do their local work in parallel. | background 0.83 | 0.10 | 0.04 | asserted 0.94 |
| s4 | 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. | background 0.39 | 0.18 | 0.10 | asserted 0.79 |
| s5 | This paper firstly introduces graph database, power system graph modeling and potential graph computing applications in power systems. | contribution 0.94 | 0.73 | 0.04 | asserted 0.99 |
| s6 | Two iterative methods based on graph database and PageRank are presented and their convergence are discussed. | contribution 0.71 | 0.88 | 0.04 | asserted 1.00 |
| s7 | Vertex contraction is proposed to improve the performance by eliminating zero-impedance branch. | method 0.73 | 0.85 | 0.05 | asserted 1.00 |
| s8 | A combination of the two iterative methods is proposed to make use of their advantages. | method 0.57 | 0.88 | 0.07 | asserted 0.98 |
| s9 | Testing results based on a provincial 1425-bus system demonstrate that the proposed comprehensive approach is a good candidate for power flow analysis. | result 0.80 | 0.95 | 0.85 | asserted 0.84 |
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Original abstract
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.
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