Identifying Key Components in Power Grid based on Multi-metric Fusion

X. Mao, W. Niu, Hua Huang, Huarong Zeng, Hao Li
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Abstract

The safe and stable operation of the power system is a critical support to maintain the security of the national science and technology industry and the steady operation of the social economy. Complex network theory abstracts power grid components and transmission lines into nodes and links in the network, respectively, providing a new perspective to explore the structure and operation characteristics of the power grid. Therefore, it is of great significance to apply complex network theory to the prevention and control of large-scale blackouts. In this paper, we propose an approach, IDEC, to Identifying key Components in a power grid based on multi-metric fusion. IDEC considers the network's topological characteristics and the electrical operating characteristics of the power system and introduces multiple metrics, such as degree centrality, one-order structural entropy, electrical betweenness centrality, and node contribution degree, to characterize the importance of components in the power grid, which are further combined together by AHP (Analytic Hierarchy Process) to characterize the importance of components overly. In the experiments, the IEEE-39 bus system is taken as a case study, and the results are compared with those in the literature. The results show that IDEC is superior to the approaches in the literature.
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基于多度量融合的电网关键部件识别
电力系统的安全稳定运行是维护国家科技产业安全和社会经济平稳运行的重要支撑。复杂网络理论将电网部件和输电线路分别抽象为网络中的节点和环节,为探讨电网的结构和运行特点提供了新的视角。因此,将复杂网络理论应用于大规模停电的防治具有重要意义。在本文中,我们提出了一种基于多度量融合的电网关键部件识别方法IDEC。IDEC考虑了电网的拓扑特性和电力系统的电气运行特性,引入了度中心性、一阶结构熵、电间中心性、节点贡献度等多个指标来表征电网中各组成部分的重要性,再通过层次分析法将这些指标结合在一起,过度表征各组成部分的重要性。在实验中,以IEEE-39总线系统为例,并与文献结果进行了比较。结果表明,IDEC方法优于文献中的方法。
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