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文章摘要
基于需求差异化的电网核心骨干网架构建
Construction of The Core Backbone of Power Grid Based on The Different Needs
Received:March 16, 2017  Revised:March 16, 2017
DOI:
中文关键词: 需求差异化  连通性修复  改进量子粒子群算法  早熟判断  混沌变异
英文关键词: need  differentiation, connectivity-repairing, improved  quantum binary  particle swarm  optimization, premature  judgment, chaotic  mutation
基金项目:国家自然科学基金资质项目(51207114)
Author NameAffiliationE-mail
WANG Kai School of Electrical Engineering,Wuhan University 982055691@qq.com 
wujun* school of electrical engineering,Wuhan university byronwu@whu.edu.cn 
liu dichen school of electrical engineering, Wuhan university dcliu@whu.edu.cn 
zhu xuedong school of eletrical engineering,Wuhan university 2016102070001@whu.edu.cn 
gaofan school of eletrical engineering,Wuhan university 1033347536@qq.com 
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中文摘要:
      为了提高故障状态下电网保障重要负荷的能力,提出一种基于需求差异化的核心骨干网架构建方法。分别从负荷、电源和网架三个需求方面建立了核心骨干网架的数学模型,同时改进了网架连通性修复策略。采用引入了动态旋转角,早熟判断机制和混沌变异策略的改进量子粒子群算法进行模型的求解,并与量子粒子群算法和量子进化算法的搜索结果进行了对比分析。应用IEEE-118节点系统进行算例分析,结果表明能准确搜索出基于负荷、电源和网架需求的核心骨干网架,所提出的改进量子粒子群算法收敛快,能够克服陷入局部最优,收敛精度高。
英文摘要:
      In order to improve the ability to protect the important load under the fault status, it presents a construction method based on the different needs of the core backbone grid. The method, respectively, establishes the model of three core backbone grid from loads, sources and grids needs, and improved the network connectivity-repairing strategy. Improved quantum binary particle swarm optimization (IQBPSO) with dynamic rotation angle, premature judgment mechanism and chaotic mutation strategy is applied to solve the model, and search results are compared and analyzed with quantum binary particle swarm algorithm (QBPSO) and quantum-inspired evolutionary algorithm (QEA). It is applied to the IEEE-118 system, and the results show that the method can accurately search out the core backbone network based on loads, sources and grids needs, and the proposed improved quantum particle swarm algorithm has fast convergence and better optimum result and can overcome the local optimum.
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