Vulnerability and Resilience Assessments of Power Grid Based on Community Partition

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-04 DOI:10.1109/TNSE.2025.3556836

Jianhua Zhang;Yixuan Zhang;Guifeng Wang;Fei Li;Jun Xie

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引用次数: 0

Abstract

The power grid has become the most important infrastructure system and dominates the economic and social developments in modern society. However, the power network often suffers various failures which can cause huge damages to the power grid. Hence, this paper proposes a community detection method to explore the critical community and identify the critical node, so as to study the vulnerability and resilience of the power grid subjected to malicious attacks. Meanwhile, this paper presents a comprehensive model to quantify the resilience characteristics considering performance level and change speed, and the IEEE 118 network is adopted to verify the feasibility and effectiveness of the proposed schemes. The results show that the attack strategies considering the community structure can cause severe damages to the power grid compared with the traditional methods, and Community Gravity-based Recovery (CGBR) has the better recovery ability among the six recovery strategies. Moreover, the results also demonstrate that Community Priority and Gravity-based Recovery (CPGBR) and CGBR can generate the most complete communities, and they can cause the least resilience damages on the number of surviving nodes (SN) subjected to the attacks on nodes 49 and 69 respectively, and Gravity-based Recovery (GBR) can result in the least resilience damages on the power supplied (PS). Hence, we can discover that there is a tradeoff between decreasing resilience loss and increasing network performance by different choices of recovery strategies. Moreover, the results show that the proposed methods can precisely identify critical nodes and rapidly recover network characteristics, therefore this study has great significances for decreasing resilience loss of the power grid subjected to malicious attacks.

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基于社区划分的电网脆弱性与恢复力评估

电网已成为现代社会最重要的基础设施系统，主导着经济和社会的发展。然而，电网经常会发生各种故障，这些故障会对电网造成巨大的破坏。为此，本文提出一种社区检测方法，探索关键社区，识别关键节点，研究电网遭受恶意攻击时的脆弱性和弹性。同时，本文提出了考虑性能水平和变化速度的综合弹性特性量化模型，并采用IEEE 118网络验证了所提方案的可行性和有效性。结果表明：与传统方法相比，考虑社区结构的攻击策略会对电网造成严重的破坏，而基于社区重力的恢复（CGBR）在6种恢复策略中具有较好的恢复能力。此外，研究结果还表明，社区优先、重力恢复（CPGBR）和CGBR能生成最完整的社区，在49号节点和69号节点遭受攻击时，它们对幸存节点（SN）造成的弹性损害最小，重力恢复（GBR）对供电（PS）造成的弹性损害最小。因此，我们可以发现，通过选择不同的恢复策略，在减少弹性损失和提高网络性能之间存在权衡。研究结果表明，该方法能够准确识别关键节点，快速恢复网络特征，对降低电网遭受恶意攻击时的弹性损失具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.