Similar articles

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

[1]Anghel M, Werley KA, Motter AE, 2007. Stochastic model for power grid dynamics. 40^th 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 1^st 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 38^th 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 39^th 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 45^th 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. 15^th 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. 7^th 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 51^st 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 (2^nd 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. 45^th 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.