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文章摘要
基于局部贪婪优化的电网自组织临界子区域辨识方法
Self-organized Criticality Sub-area Identification Method Based on Local Greedy Optimization Method
Received:June 25, 2019  Revised:July 11, 2019
DOI:DOI: 10.19753/j.issn1001-1390.2020.22.003
中文关键词: 级联事故,自组织临界,能量函数,复杂网络,局部贪婪优化算法
英文关键词: cascading  accident, self-organized  criticality, energy  function, complex  network, the  local greedy  optimization algorithm
基金项目:国家自然科学基金“多类型利益主体集群交易驱动的配电网分布鲁棒规划方法”(51807125);
Author NameAffiliationE-mail
WEI Zhenbo School of Electrical Engineering,Sichuan University weizhenbo@scu.edu.c 
GUAN Xiangyou* School of Electrical Engineering,Sichuan University 2821572344@qq.com 
GAO Hongjun School of Electrical Engineering,Sichuan University gaohongjun@scu.edu.cn 
LIU Lianghao School of Electrical Engineering,Sichuan University 335675139@qq.com 
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中文摘要:
      针对传统电力系统自组织临界现象研究角度及其物理模型——沙堆模型无法有效解释当前电网级联事故体现出的新特征,提出了社区自组织临界性概念及其物理模型——重叠沙堆模型。首先,应用能量函数并结合直接法的建模方式,量化电网线路之间的能量关联关系,并由从构造出加权无向复杂网络模型;之后,在此模型的基础上,运用局部贪婪优化算法实现电网自组织临界子区域辨识;最后,通过级联故障模拟仿真量化分析自组织临界子区域作用。研究表明,电网级联事故本质上是某一子区域达到SOC状态;在以线路为基本结构单元,能量函数量化权重的复杂网络模型的执行环境内,局部贪婪优化算法可有效挖掘电网中的自组织临界子区域。为电网级联事故安全性分析提供新的思路。
英文摘要:
      Because the traditional self-organized criticality (SOC) research point of power system and its physical model—the sand pile model and cannot effectively explain the new characteristics of cascading accidents under the existing development trend of the power grid, we propose the concept of community self-organized criticality and its physical model—the overlapping sand pile model indicates that the grid cascading accident is essentially a sub-area reaching the SOC state. Firstly, the energy function is applied and combined with the direct method modeling method to quantify the energy correlation between the grid lines, and the weighted undirected complex network model is constructed. Then, based on the model, the improved the local greedy optimization algorithm is used to realize the self-organized criticality sub-area identification of the power grid. Finally, the simulation results verify the effectiveness of the above model and algorithm. In the execution environment of a complex network model with line as the basic structural unit and energy function quantization weight, the local greedy optimization algorithm can effectively mine the SOC sub-area in the power grid. Then, we can provide new ideas for grid cascading accident safety analysis.
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