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CLC number: TP393

On-line Access: 2022-03-22

Received: 2020-11-02

Revision Accepted: 2022-04-22

Crosschecked: 2021-03-07

Cited: 0

Clicked: 3556

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Supaporn LONAPALAWONG

https://orcid.org/0000-0002-4032-7740

Can WANG

https://orcid.org/0000-0002-5890-4307

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.3 P.382-397

http://doi.org/10.1631/FITEE.2000596


Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition


Author(s):  Supaporn LONAPALAWONG, Jiangzhe YAN, Jiayu LI, Deshi YE, Wei CHEN, Yong TANG, Yanhao HUANG, Can WANG

Affiliation(s):  State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   11821132@zju.edu.cn, wcan@zju.edu.cn

Key Words:  Network robustness, Cascading failure, Average propagation, Algebraic connectivity, Power grid


Supaporn LONAPALAWONG, Jiangzhe YAN, Jiayu LI, Deshi YE, Wei CHEN, Yong TANG, Yanhao HUANG, Can WANG. Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(3): 382-397.

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journal="Frontiers of Information Technology & Electronic Engineering",
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pages="382-397",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000596"
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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.

通过最小化边缘添加中的代数连接度来减少电网级联故障传播

Supaporn LONAPALAWONG1,颜姜哲2,李家雨3,叶德仕2,陈为1,汤涌4,黄彦浩4,王灿2
1浙江大学计算机辅助设计与图形学国家重点实验室,中国杭州市,310058
2浙江大学计算机科学与技术学院,中国杭州市,310058
3浙江大学数学科学学院,中国杭州市,310058
4中国电力科学研究院电网安全与能源转换国家重点实验室,中国北京市,100192
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。

关键词:网络鲁棒性;级联故障;平均传播;代数连接度;电网

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Anghel M, Werley KA, Motter AE, 2007. Stochastic model for power grid dynamics. 40th Annual Hawaii Int Conf on System Sciences, p.1-10.

[2]Azzolin A, Dueñas-Osorio L, Cadini F, et al., 2018. Electrical and topological drivers of the cascading failure dynamics in power transmission networks. Reliab Eng Syst Saf, 175:196-206.

[3]Bigdeli A, Tizghadam A, Leon-Garcia A, 2009. Comparison of network criticality, algebraic connectivity, and other graph metrics. Proc 1st Annual Workshop on Simplifying Complex Network for Practitioners, p.1-6.

[4]Bompard E, Estebsari A, Huang T, et al., 2016. A framework for analyzing cascading failure in large interconnected power systems: a post-contingency evolution simulator. Int J Electr Power Energy Syst, 81:12-21.

[5]Carreras BA, Lynch VE, Dobson I, et al., 2002. Critical points and transitions in an electric power transmission model for cascading failure blackouts. Chaos, 12:985-994.

[6]Carreras BA, Newman DE, Dobson I, et al., 2004. Evidence for self-organized criticality in a time series of electric power system blackouts. IEEE Trans Circ Syst I, 51(9):1733-1740.

[7]Chen J, Thorp JS, Dobson I, 2005. Cascading dynamics and mitigation assessment in power system disturbances via a hidden failure model. Int J Electr Power Energy Syst, 27(4):318-326.

[8]Chen Q, Mili L, 2013. Composite power system vulnerability evaluation to cascading failures using importance sampling and antithetic variates. IEEE Trans Power Syst, 28(3):2321-2330.

[9]Correa-Henao GJ, Yusta-Loyo JM, 2015. Representation of electric power systems by complex networks with applications to risk vulnerability assessment. DYNA, 82(192):68-77.

[10]Correa-Henao GJ, Yusta JM, Lacal-Arántegui R, 2013. Using interconnected risk maps to assess the threats faced by electricity infrastructures. Int J Crit Infrastr Prot, 6(3-4):197-216.

[11]Cuadra L, Salcedo-Sanz S, Del Ser J, et al., 2015. A critical review of robustness in power grids using complex networks concepts. Energies, 8(9):9211-9265.

[12]Dey P, Mehra R, Kazi F, et al., 2016. Impact of topology on the propagation of cascading failure in power grid. IEEE Trans Smart Grid, 7(4):1970-1978.

[13]Dobson I, Carreras BA, Newman DE, 2005. Branching process models for the exponentially increasing portions of cascading failure blackouts. Proc 38th Annual Hawaii Int Conf on System Sciences, p.1-10.

[14]Dobson I, Wierzbicki KR, Carreras BA, et al., 2006. An estimator of propagation of cascading failure. Proc 39th Annual Hawaii Int Conf on System Sciences, p.1-10.

[15]Dobson I, Kim J, Wierzbicki KR, 2010. Testing branching process estimators of cascading failure with data from a simulation of transmission line outages. Risk Anal, 30(4):650-662.

[16]Ellens W, Spieksma F, van Mieghem P, et al., 2011. Effective graph resistance. Linear Algebra Appl, 435(10):2491-2506.

[17]Eppstein MJ, Hines PDH, 2012. A “random chemistry” algorithm for identifying collections of multiple contingencies that initiate cascading failure. IEEE Trans Power Syst, 27(3):1698-1705.

[18]Fiedler M, 1973. Algebraic connectivity of graphs. Czech Math J, 23(2):298-305.

[19]Ghosh A, Boyd S, 2006. Growing well-connected graphs. Proc 45th IEEE Conf on Decision and Control, p.6605-6611.

[20]Gu YJ, Yang HY, Sun W, et al., 2020. Hierarchical control of DC microgrids robustness and smartness. CSEE J Power Energy Syst, 6(2):384-393.

[21]Guan ZH, Chen L, Qian TH, 2011. Routing in scale-free networks based on expanding betweenness centrality. Phys A, 390(6):1131-1138.

[22]Holme P, Kim BJ, Yoon CN, et al., 2002. Attack vulnerability of complex networks. Phys Rev E, 65:056109.

[23]Jamakovic A, Uhlig S, 2007. Influence of the network structure on robustness. 15th IEEE Int Conf on Networks, p.278-283.

[24]Ji XP, Wang B, Liu DC, et al., 2016. Improving interdependent networks robustness by adding connectivity links. Phys A, 444:9-19.

[25]Jiang ZY, Liang MG, Guo DC, 2011. Enhancing network performance by edge addition. Int J Mod Phys C, 22(11):1211-1226.

[26]Koç Y, Verma T, Araujo NAM, et al., 2013. MATCASC: a tool to analyse cascading line outages in power grids. IEEE Int Workshop on Intelligent Energy Systems, p.143-148.

[27]Koç Y, Warnier M, van Mieghem P, et al., 2014. The impact of the topology on cascading failures in a power grid model. Phys A, 402:169-179.

[28]Laszka A, Buttyán L, Szeszlér D, 2013. Designing robust network topologies for wireless sensor networks in adversarial environments. Pervas Mob Comput, 9(4):546-563.

[29]Li CH, Xue YS, 2019. Effects of cascading failure intervals on synchronous stability. Int J Elect Power Energy Syst, 106:502-510.

[30]Liu J, Zhang HX, Qiao W, et al., 2019. DC (optimal) power flow-based models for simulation and mitigation of overload cascading failures. North American Power Symp, p.1-5.

[31]Liu W, Sirisena H, Pawlikowski K, et al., 2009. Utility of algebraic connectivity metric in topology design of survivable networks. 7th Int Workshop on Design of Reliable Communication Networks, p.131-138.

[32]Liu ZY, Zhang HP, Smith P, et al., 2012. Optimizing weighted graph topology for robust network information dissemination. Proc 51st IEEE Conf on Decision and Control, p.3329-3334.

[33]Marsden PV, 2015. Network Centrality, Measures of. In: International Encyclopedia of the Social & Behavioral Sciences (2nd Ed.). Elsevier, Oxford, p.532-539.

[34]Mohar B, Alavi Y, Chartrand G, et al., 1991. The Laplacian spectrum of graphs. Graph Theory Combin Appl, 2:5364.

[35]Moussawi A, Derzsy N, Lin X, et al., 2017. Limits of predictability of cascading overload failures in spatially-embedded networks with distributed flows. Sci Rep, 7:11729.

[36]Pahwa S, Hodges A, Scoglio C, et al., 2012. Topological analysis and mitigation strategies for cascading failures in power grid networks. https://arxiv.org/abs/1212.5620

[37]Peng GS, Wu J, 2016. Optimal network topology for structural robustness based on natural connectivity. Phys A, 443:212-220.

[38]Pizzuti C, Socievole A, van Mieghem P, 2020. Comparative network robustness evaluation of link attacks. Complex Networks and Their Applications VIII. Studies in Computational Intelligence, p.735-746.

[39]Qi JJ, Dobson I, Mei SW, 2013. Towards estimating the statistics of simulated cascades of outages with branching processes. IEEE Trans Power Syst, 28(3):3410-3419.

[40]Rei AM, Leite da Silva AM, Jardim JL, et al., 2000. Static and dynamic aspects in bulk power system reliability evaluations. IEEE Trans Power Syst, 15(1):189-195.

[41]Rezaei P, Hines P, Eppstein M, 2015. Estimating cascading failure risk with random chemistry. IEEE Power and Energy Society General Meeting, p.1.

[42]Saleh M, Esa Y, Mohamed A, 2018. Applications of complex network analysis in electric power systems. Energies, 11(6):1381.

[43]Song JJ, Cotilla-Sanchez E, Ghanavati G, et al., 2016. Dynamic modeling of cascading failure in power systems. IEEE Trans Power Syst, 31(3):2085-2095.

[44]Spiewak R, Soltan S, Forman Y, et al., 2018. A study of cascading failures in real and synthetic power grid topologies. Netw Sci, 6(4):448-468.

[45]Sydney A, Scoglio C, Gruenbacher D, 2013. Optimizing algebraic connectivity by edge rewiring. Appl Math Comput, 219(10):5465-5479.

[46]Tang Y, Huang YH, Wang HZ, et al., 2018. Framework for artificial intelligence analysis in large-scale power grids based on digital simulation. CSEE J Power Energy Syst, 4(4):459-468.

[47]van Mieghem P, 2010. Graph Spectra for Complex Networks. Cambridge University Press, Cambridge, UK.

[48]Wang JW, Rong LL, 2009. Cascade-based attack vulnerability on the US power grid. Saf Sci, 47(10):1332-1336.

[49]Wang JX, Wei JD, Zhu YC, et al., 2020. The reliability and operational test system of a power grid with large-scale renewable integration. CSEE J Power Energy Syst, 6(3):704-711.

[50]Wang YZ, Baldick R, 2014. Interdiction analysis of electric grids combining cascading outage and medium-term impacts. IEEE Trans Power Syst, 29(5):2160-2168.

[51]Wang ZF, Scaglione A, Thomas RJ, 2012. A Markov-transition model for cascading failures in power grids. 45th Hawaii Int Conf on System Sciences, p.2115-2124.

[52]Wei P, Chen L, Sun D, 2014. Algebraic connectivity maximization of an air transportation network: the flight routes’ addition/deletion problem. Trans Res E, 61:13-27.

[53]Wei XG, Gao SB, Huang T, et al., 2019. Identification of two vulnerability features: a new framework for electrical networks based on the load redistribution mechanism of complex networks. Complexity, 2019:3531209.

[54]Zhang X, Tse CK, 2015. Assessment of robustness of power systems from a network perspective. IEEE J Emerg Sel Top Circ Syst, 5(3):456-464.

[55]Zheng YX, Zhao SH, Liu Y, et al., 2017. Weighted algebraic connectivity maximization for optical satellite networks. IEEE Access, 5:6885-6893.

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