Abstract
Analyzing network robustness under various circumstances is generally regarded as a challenging problem. Robustness against failure is one of the essential properties of large-scale dynamic network systems such as power grids, transportation systems, communication systems, and computer networks. Due to the network diversity and complexity, many topological features have been proposed to capture specific system properties. For power grids, a popular process for improving a network’s structural robustness is via the topology design. However, most of existing methods focus on localized network metrics, such as node connectivity and edge connectivity, which do not encompass a global perspective of cascading propagation in a power grid. In this paper, we use an informative global metric algebraic connectivity because it is sensitive to the connectedness in a broader spectrum of graphs. Our process involves decreasing the average propagation in a power grid by minimizing the increase in its algebraic connectivity. We propose a topology-based greedy strategy to optimize the robustness of the power grid. To evaluate the network robustness, we calculate the average propagation using MATCASC to simulate cascading line outages in power grids. Experimental results illustrate that our proposed method outperforms existing techniques.
摘要
在各种情况下分析网络鲁棒性通常被认为是一个具有挑战性的问题. 应对故障的鲁棒性是大型动态网络系统 (如电力网、 运输系统、 通信系统和计算机网络) 的基本特性之一. 由于网络的多样性和复杂性, 人们已提出许多拓扑特征以捕获系统特定属性. 对于电网, 通过拓扑设计提高网络结构鲁棒性是常见做法. 然而, 大多数现有方法集中于局部网络度量, 例如节点连接度和边连接度, 而非从全局视角看待电网中的级联传播. 本文使用信息量大的全局度量代数连接度, 因为它对谱图的全局连接度敏感. 我们通过最小化代数连接度的增量以减少电网中的平均传播. 提出一种基于拓扑的贪婪策略, 以优化电网鲁棒性. 为评估网络鲁棒性, 使用MATCASC计算电网中级联故障中断的平均传播. 实验结果表明, 所提方法优于现有技术.
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Supaporn LONAPALAWONG designed the study and addressed the problems. Supaporn LONAPALAWONG and Jiangzhe YAN processed the data and designed the algorithms. Jiayu LI and Deshi YE helped deduce the mathematical models and algorithms. Supaporn LONAPALAWONG drafted the manuscript. Yong TANG and Yanhao HUANG helped with the technical information. Wei CHEN supported with the resources. Can WANG helped organize the manuscript. Supaporn LONAPALAWONG, Jiangzhe YAN, and Can WANG revised and finalized the paper.
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Supaporn LONAPALAWONG, Jiangzhe YAN, Jiayu LI, Deshi YE, Wei CHEN, Yong TANG, Yanhao HUANG, and Can WANG declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (No. U1866602) and the National Key R&D Program of China (Nos. 2019YFB1600700 and 2018AAA0101505)
Supaporn LONAPALAWONG, first author of this invited paper, is currently a PhD candidate in the College of Computer Science and Technology at Zhejiang University, China. Her research interests include data mining and visual analytics.
Can WANG, corresponding author of this invited paper, is currently an associate professor in the College of Computer Science and Technology at Zhejiang University, China. He received his BS degree in Economics in 1995, and his MS and PhD degrees in Computer Science in 2003 and 2009, respectively, from Zhejiang University. His research interests include information retrieval, data mining, machine learning, and information accessibility. He has published over 30 research papers in SCI indexed journals and top international conferences. He is the recipient of AAAI Outstanding Paper Award (2012). He is also the coach of ACM/ICPC team at Zhejiang University, which won the 35th ACM/ICPC World Final Champion in Orlando, USA, in 2011.
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Lonapalawong, S., Yan, J., Li, J. et al. Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition. Front Inform Technol Electron Eng 23, 382–397 (2022). https://doi.org/10.1631/FITEE.2000596
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DOI: https://doi.org/10.1631/FITEE.2000596