Identifying core nodes in interaction graphs for critical component analysis in cascading failures of power grids

• Autorzy: Rivas-Dávalos Francisco , Toledo-Adame Daniel , Martínez-Ceseña Eduardo A.

Identyfikatory

DOI

10.24425/aee.2025.153022

• Języki publikacji: EN

Abstrakty

EN

This paper introduces a novel approach to identifying critical lines in power systems during cascading failures, addressing significant limitations in previous methodologies such as computational inefficiency and limited effectiveness. The proposed methodology is inspired by social network analysis techniques for evaluating the importance of nodes and determining core roles within a network. By adopting these techniques, multiple centrality metrics – degree, ego-betweenness centrality, and eigenvector centrality – are applied to assess the importance and role of lines in interaction graphs of cascading failures. The methodology can be applied to any type of interaction graph of cascading failures and involves selecting centrality metrics with strong correlations to outline the particularity of critical lines. The importance of each line is evaluated as the normalized sum of its three metrics. Critical lines are identified based on their deviation from the statistical correlation of the overall interaction graph, analogous to the role determination process in social networks. The effectiveness of the proposed approach is demonstrated through extensive testing on IEEE 39-bus and 118-bus systems. The identified critical lines are validated with a method from the literature and by analysing the effects of upgrading the critical lines, demonstrating the accuracy and reliability of the proposed methodology. By leveraging interaction graphs and simulation data, our approach provides a robust framework for mitigating cascading failures. The results indicate that this methodology not only improves computational efficiency but also enhances the precision of critical line identification, making it highly suitable for real-time applications in power system stability and reliability.

Słowa kluczowe

EN

cascading failures; interaction graphs; power grids

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2025
• Tom: Vol. 74, nr 1
• Strony: 247--268
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr., wz.

Twórcy

autor

Rivas-Dávalos Francisco

• Tecnológico Nacional de México/Instituto Tecnológico de Morelia Av. Tecnológico 1500, Col. Lomas de Santiaguito, Morelia, Michoacán, México

• https://orcid.org/0000-0002-3000-8666

autor

Toledo-Adame Daniel

• Tecnológico Nacional de México/Instituto Tecnológico de Morelia Av. Tecnológico 1500, Col. Lomas de Santiaguito, Morelia, Michoacán, México

autor

Martínez-Ceseña Eduardo A.

• Department of Electrical and Electronic Engineering, The University of Manchester Manchester, M13 9PL, UK

Bibliografia

• [1] Sroka K., Złotecka D., The risk of large blackout failures in power systems, Archives of Electrical Engineering, pp. 411–426 (2019), DOI: https://doi.org/10.24425/aee.2019.128277.
• [2] Zhang G., Li Z., Zhang B., Halang W.A., Understanding the cascading failures in Indian power grids with complex networks theory, Physica A: Statistical Mechanics and its Applications, vol. 392, no. 15, pp. 3273–3280 (2013), DOI: https://doi.org/10.1016/j.physa.2013.03.003.
• [3] Andersson G., Donalek P., Farmer R., Hatziargyriou N., Kamwa I., Kundur P., Martins N., Paserba J., Pourbeik P., Sanchez-Gasca J., Schulz R., Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance, IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 1922–1928 (2005), DOI: https://doi.org/10.1109/TPWRS.2005.857942.
• [4] Solé R.V., Rosas-Casals M., Corominas-Murtra B., Valverde S., Robustness of the European power grids under intentional attack, Physical Review E – Statistical, Nonlinear, and Soft Matter Physics, vol. 77, no. 2, 026102 (2008), DOI: https://doi.org/10.1103/PhysRevE.77.026102.
• [5] Rosas-Casals M., Valverde S., Solé R.V., Topological vulnerability of the European power grid under errors and attacks, International Journal of Bifurcation and Chaos, vol. 17 no. 7, pp. 2465–2475 (2007), DOI: https://doi.org/10.1142/S0218127407018531.
• [6] Mei S., Zhang X., Cao M., Power grid complexity, Springer Science & Business Media (2011).
• [7] Yu W., Wen G., Yu X., Wu Z., Lü J., Bridging the gap between complex networks and smart grids, Journal of Control and Decision, vol. 1, no. 1, pp. 102–114 (2014), DOI: https://doi.org/10.1080/ 23307706.2014.885293.
• [8] Boccaletti S., Latora V., Moreno Y., Chavez M., Hwang D.U., Complex networks: Structure and dynamics, Physics Reports, vol. 424, no. 4–5, pp. 175–308 (2006), DOI: https://doi.org/10.1016/ j.physrep.2005.10.009.
• [9] Ahuja M., Sharma K., Complex networks: A review, International Journal of Computer Applications, vol. 101, no. 15 (2014).
• [10] Restrepo J.G., Ott E., Hunt B.R., Characterizing the dynamical importance of network nodes and links, Physical Review Letters, vol. 97, no. 9, 094102 (2006), DOI: https://doi.org/10.1103/PhysRevLett. 97.094102.
• [11] Adebayo I., Sun Y., New approaches for the identification of influential and critical nodes in an electric grid, Archives of Electrical Engineering, vol. 71, no. 3, pp. 671–686 (2022), DOI: https://doi.org/10.24425/aee.2022.141678.
• [12] Holmgren Å.J., Using graph models to analyze the vulnerability of electric power networks, Risk Analysis, vol. 26, no. 4, pp. 955–969 (2006), DOI: https://doi.org/10.1111/j.1539-6924.2006.00791.x.
• [13] Qi J., Sun K., Mei S., An interaction model for simulation and mitigation of cascading failures, IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 804–819 (2014), DOI: https://doi.org/10.1109/TPWRS.2014.2337284.
• [14] Ju W., Sun K., Qi J., Multi-Layer Interaction Graph for Analysis and Mitigation of Cascading Outages, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 7, no. 2, pp. 239–249 (2017), DOI: https://doi.org/10.1109/JETCAS.2017.2703948.
• [15] Hines P.D., Dobson I., Rezaei P., Cascading power outages propagate locally in an influence graph that is not the actual grid topology, EEE Transactions on Power Systems, vol. 32, no. 2, pp. 958–967 (2016), DOI: https://doi.org/10.1109/TPWRS.2016.2578259.
• [16] Zhou K., Dobson I., Hines P.D.H., Wang Z., Can an influence graph driven by outage data determine transmission line upgrades that mitigate cascading blackouts?, IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Boise, ID, USA, pp. 1–6 (2018), DOI: https://doi.org/10.1109/PMAPS.2018.8440497.
• [17] Zhou K., Dobson I., Wang Z., Roitershtein A., Ghosh A.P., A Markovian influence graph formed from utility line outage data to mitigate large cascades, IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 3224–3235 (2020), DOI: https://doi.org/10.1109/TPWRS.2020.2970406.
• [18] Koç Y., Warnier M., Kooij R., Brazier F., Structural vulnerability assessment of electric power grids, Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, Miami, FL, USA, pp. 386–391 (2014), DOI: https://doi.org/10.1109/ICNSC.2014.6819657.
• [19] Arianos S., Bompard E., Carbone A., Xue F., Power grid vulnerability: A complex network approach, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 19, no. 1 (2009), DOI: https://doi.org/10.1063/1.3077229.
• [20] Bompard E., Napoli R., Xue F., Analysis of structural vulnerabilities in power transmission grids, International Journal of Critical Infrastructure Protection, vol. 2, iss. 1–2, pp. 5–12 (2009), DOI: https://doi.org/10.1016/j.ijcip.2009.02.002.
• [21] Wei X., Zhao J., Huang T., Bompard E., A novel cascading faults graph based transmission network vulnerability assessment method, IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 2995–3000 (2017), DOI: https://doi.org/10.1109/TPWRS.2017.2759782.
• [22] Wei X., Gao S., Huang T., Wang T., Fan W., Identification of two vulnerability features: A new framework for electrical networks based on the load redistribution mechanism of complex networks, Complexity, vol. 2019, no. 1, 3531209 (2019), DOI: https://doi.org/10.1155/2019/3531209.
• [23] Hines P., Blumsack S., A centrality measure for electrical networks, In Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), pp. 185–185 (2008), DOI: https://doi.org/10.1109/HICSS.2008.5.
• [24] Wang K., Zhang B.H., Zhang Z., Yin X.G., Wang B., An electrical betweenness approach for vulnerability assessment of power grids considering the capacity of generators and load, Physica A: Statistical Mechanics and its Applications, vol. 390, iss. 23–24, pp. 4692–4701 (2011), DOI: https://doi.org/10.1016/j.physa.2011.07.031.
• [25] Bonacich P., Factoring and weighting approaches to status scores and clique identification, Journal of Mathematical Sociology, vol. 2, no. 1, pp. 113–120 (1972), DOI: https://doi.org/10.1080/ 0022250X.1972.9989806.
• [26] Xiaolong R., Linyuan L., Review of ranking nodes in complex networks, Chinese Science Bulletin, vol. 59, no. 13, pp. 1175–1197 (2014).
• [27] Caro-Ruiz C., Mojica-Nava E., Centrality measures for voltage instability analysis in power networks, IEEE 2nd Colombian Conference on Automatic Control (CCAC), Manizales, Colombia, pp. 1–6 (2015), DOI: https://doi.org/10.1109/CCAC.2015.7345182.
• [28] Wang F., Hu H.,Coverage hole detection method of wireless sensor network based on clustering algorithm, Measurement, vol. 179, 109449 (2021), DOI: https://doi.org/10.1016/j.measurement.2021.109449.
• [29] Lü L., Chen D., Ren X.L., Zhang Q.M., Zhang Y.C., Zhou T., Vital nodes identification in complex networks, Physics Reports, vol. 650, pp. 1–63 (2016), DOI: https://doi.org/10.1016/j.physrep.2016.06.007.
• [30] Sabidussi G., The centrality index of a graph, Psychometrika, vol. 31, no. 4, pp. 581–603 (1966), DOI: https://doi.org/10.1007/BF02289527.
• [31] Freeman L.C., Centrality in social networks: Conceptual clarification, Social Network: Critical Concepts in Sociology, Londres: Routledge, vol. 1, pp. 238–263 (2002).
• [32] Kitsak M., Gallos L.K., Havlin S., Liljeros F., Muchnik L., Stanley H.E., Makse H.A., Identification of influential spreaders in complex networks, Nature Physics, vol. 6, no. 11, pp. 888–893 (2010), DOI: https://doi.org/10.1038/nphys1746.
• [33] Qiu L., Zhang J., Tian X., Ranking influential nodes in complex networks based on local and global structures, Applied Intelligence, vol. 51, pp. 4394–4407 (2021), DOI: https://doi.org/10.1007/s10489- 020-02132-1.
• [34] Nakarmi U., Rahnamay-Naeini M., Analyzing Power Grids’ Cascading Failures and Critical Components using Interaction Graphs, IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, pp. 1–5 (2018), DOI: https://doi.org/10.1109/PESGM.2018.8585812.
• [35] Nakarmi U., Rahnamay-Naeini M., Khamfroush H., Critical component analysis in cascading failures for power grids using community structures in interaction graphs, IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 1079–1093 (2019), DOI: https://doi.org/10.1109/TNSE.2019.2904008.
• [36] Huang S., Lv T., Zhang X., Yang Y., Zheng W., Wen C., Identifying node role in social network based on multiple indicators, PloS One, vol. 9, no. 8, e103733 (2014), DOI: https://doi.org/10.1371/ journal.pone.0103733.
• [37] Cetinay H., Soltan S., Kuipers F.A., Zussman G., Van Mieghem P., Comparing the effects of failures in power grids under the AC and DC power flow models, IEEE Transactions on Network Science and Engineering, vol. 5, no. 4, pp. 301–312 (2017), DOI: https://doi.org/10.1109/TNSE.2017.2763746.
• [38] Cotilla-Sanchez E., Modeling Cascading Failures in Power Systems: Quasi-Steady-State Models and Dynamic Models, In Cascading Failures in Power Grids: Risk Assessment, Modeling, and Simulation, pp. 175–190, Cham: Springer International Publishing (2023), DOI: https://doi.org/10.1007/978-3-031- 48000-3_5.
• [39] Song J., Cotilla-Sanchez E., Ghanavati G., Hines, P.D., Dynamic modeling of cascading failure in power systems, IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 2085–2095 (2015), DOI: https://doi.org/10.1109/TPWRS.2015.2439237.
• [40] Antoine J.P., Stubbe M., EUROSTAG, software for the simulation of power system dynamics. Its application to the study of a voltage collapse scenario, In IEE Colloquium on Interactive Graphic Power System Analysis Programs, IET, pp. 5/1 (1992).
• [41] Dai Y., Mathaios P., Robin P., Python scripting for DIgSILENT PowerFactory: Enhancing dynamic modelling of cascading failures, In 2021 IEEE Madrid PowerTech, pp. 1–6, IEEE (2021), DOI: https://doi.org/10.1109/PowerTech46648.2021.9494872.
• [42] Carreras B.A., Newman D.E., Dobson I., Degala N.S., Validating OPA with WECC data, In 2013 46th Hawaii International Conference on System Sciences, pp. 2197–2204 (2013), DOI: https://doi.org/10.1109/HICSS.2013.594.
• [43] Rios M.A., Kirschen D.S., Jayaweera D., Nedic D.P., Allan R.N., Value of security: modeling timedependent phenomena and weather conditions, IEEE Transactions on Power Systems, vol. 17, no. 3, pp. 543–548 (2002), DOI: https://doi.org/10.1109/TPWRS.2002.800872.
• [44] Dobson I., Carreras B.A., Lynch V.E., Newman D.E., Complex systems analysis of series of blackouts: Cascading failure, critical points, and self-organization, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 17, no. 2 (2007), DOI: https://doi.org/10.1063/1.2737822.
• [45] Estrada E., The structure of complex networks: theory and applications, American Chemical Society (2012).
• [46] Newman M., Networks, Oxford University Press (2018).
• [47] https://matpower.org/docs/ref/matpower5.0/case39.html, accessed April 2023.
• [48] https://matpower.org/docs/ref/matpower5.0/case118.html, accessed April 2023.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).