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Issue title: Applied Mathematics Related to Nonlinear Problems
Guest editors: Juan L.G. Guirao and Wei Gao
Article type: Research Article
Authors: Zhang, Limeia | Liu, Yongfua; * | Zhang, Penghuia | Tang, Weib | Lv, Jinga | Zhang, Lub | Li, Dongmingc
Affiliations: [a] College of Information Science and Technology, Agricultural University of Hebei, Baoding, China | [b] College of Information and Electrical Engineering, China Agricultural University, Beijing, China | [c] College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding, China
Correspondence: [*] Corresponding author. Yongfu Liu, College of Information Science and Technology, Agricultural University of Hebei, Baoding, China. E-mail: [email protected].
Abstract: With the penetration of multi-source and multi-type distributed generation (DG) in distribution network, the power flow calculations of distribution network become more and more complex. On the premise of analysis of power flow calculation model for different DG, this paper presents a power flow parallel computing algorithm for complex distribution network based on multicore CPU technique. On the basis of analysis of topological structure of distribution system, the task assignment problems in the isomorphism multicore processors were solved to improve speedup ratio and parallel efficiency. Integrated with the characteristics of backward/forward sweep methods, a hybrid power flow parallel computing method was given to adapt for the nodal type of various DG. In order to test the convergence and parallel efficiency, the proposed algorithm have been tested on IEEE 90 bus and a composite system, which is composed of IEEE 20 bus, IEEE 90 bus, IEEE 37 bus and etc. The results show that the proposed hybrid power flow computing method is adaptive to the complex distribution network with multi-type DGs, and the designed parallel algorithm effectively shortens time of solving equations and multicore resources are fully utilized.
Keywords: Distributed generation, distribution network, power flow, parallel computing
DOI: 10.3233/JIFS-169350
Journal: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2987-2997, 2017
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