Research on Parallel Topology Analysis Algorithm Based on Simulated Annealing

被引:0
|
作者
Wang Xin-liang [1 ,2 ]
Chen Jian-Lin [2 ]
Ma Tian-Fang [2 ]
Liu Zhi-huai [1 ]
Liu Na [3 ]
Fang Wei [3 ]
Liu Xue-bin [3 ]
Zhang Li-Wei [2 ]
Wu Jun [2 ]
机构
[1] Hami Yuxin Energy Ind Res Inst Co Ltd, Hami 839000, Peoples R China
[2] Henan Polytech Univ, Sch Phys & Elect Informat Engn, Jiaozuo 454000, Henan, Peoples R China
[3] Hami Vocat & Tech Coll, Hami 839000, Peoples R China
来源
PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019) | 2019年
基金
中国国家自然科学基金;
关键词
Topology analysis; Simulated annealing; High-voltage grid of coal mine; Parallel computing;
D O I
10.1109/itaic.2019.8785882
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the high-voltage power grid of coal mine, the existing adaptive topology analysis algorithm based on the correlation matrix for coal mine high-voltage power grid can effectively identify the network topology and construct a network topology analysis model to provide a basis for subsequent short-circuit current calculation and setting calculation. However, the topology analysis algorithm based on the correlation matrix takes much time. As the scale of the high-voltage power grid network of the coal mine increases, the time consumption will increase sharply as well. However, the prior-to-first-served parallel topology analysis algorithm has been used to reduce the topology analysis time consumption to some extent. On this basis, this article proposes a parallel topology analysis algorithm based on the simulated annealing and optimizes the scheduling strategy of parallel topology analysis. The simulation results show that compared with the prior-to-first-served parallel topology analysis algorithm, the parallel annealing topology analysis algorithm can further reduce the time consumption and improve the computational efficiency. And when the number of available threads increases, its computational efficiency will improve more significantly.
引用
收藏
页码:1703 / 1706
页数:4
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