IDEAS home Printed from https://ideas.repec.org/a/hin/complx/3531209.html
   My bibliography  Save this article

Identification of Two Vulnerability Features: A New Framework for Electrical Networks Based on the Load Redistribution Mechanism of Complex Networks

Author

Listed:
  • Xiaoguang Wei
  • Shibin Gao
  • Tao Huang
  • Tao Wang
  • Wenli Fan

Abstract

This paper proposes a new framework to analyze two vulnerability features, impactability and susceptibility, in electrical networks under deliberate attacks based on complex network theory: these two features are overlooked but vital in vulnerability analyses. To analyze these features, metrics are proposed based on correlation graphs constructed via critical paths, which replace the original physical network. Moreover, we analyze the relationship between the proposed metrics according to degree from the perspective of load redistribution mechanisms by adjusting parameters associated with the metrics, which can change the load redistribution rules. Finally, IEEE 118- and 300-bus systems and a realistic large-scale French grid are used to validate the effectiveness of the proposed metrics.

Suggested Citation

  • Xiaoguang Wei & Shibin Gao & Tao Huang & Tao Wang & Wenli Fan, 2019. "Identification of Two Vulnerability Features: A New Framework for Electrical Networks Based on the Load Redistribution Mechanism of Complex Networks," Complexity, Hindawi, vol. 2019, pages 1-14, January.
    • Handle: RePEc:hin:complx:3531209
    DOI: 10.1155/2019/3531209
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/3531209.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2019/3531209.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/3531209?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ying Wang & Xiangmei Li & Jiangfeng Li & Zhengdong Huang & Renbin Xiao, 2018. "Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach," Complexity, Hindawi, vol. 2018, pages 1-18, May.
    2. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo, 2004. "A topological analysis of the Italian electric power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 92-97.
    3. Long Guo & Xu Cai, 2008. "Degree And Weighted Properties Of The Directed China Railway Network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(12), pages 1909-1918.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tao Wang & Xiaoguang Wei & Tao Huang & Jun Wang & Luis Valencia-Cabrera & Zhennan Fan & Mario J. Pérez-Jiménez, 2019. "Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber-Physical Systems in Smart Grids," Complexity, Hindawi, vol. 2019, pages 1-15, March.
    2. Upama Nakarmi & Mahshid Rahnamay Naeini & Md Jakir Hossain & Md Abul Hasnat, 2020. "Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey," Energies, MDPI, vol. 13(9), pages 1-25, May.
    3. Tianlei Zang & Zian Wang & Xiaoguang Wei & Yi Zhou & Jiale Wu & Buxiang Zhou, 2023. "Current Status and Perspective of Vulnerability Assessment of Cyber-Physical Power Systems Based on Complex Network Theory," Energies, MDPI, vol. 16(18), pages 1-38, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dávid Csercsik & László Á. Kóczy, 2017. "Efficiency and Stability in Electrical Power Transmission Networks: a Partition Function Form Approach," Networks and Spatial Economics, Springer, vol. 17(4), pages 1161-1184, December.
    2. Ettore Bompard & Lingen Luo & Enrico Pons, 2015. "A perspective overview of topological approaches for vulnerability analysis of power transmission grids," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 11(1), pages 15-26.
    3. Wang, Zhuoyang & Chen, Guo & Hill, David J. & Dong, Zhao Yang, 2016. "A power flow based model for the analysis of vulnerability in power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 105-115.
    4. Shahpari, Alireza & Khansari, Mohammad & Moeini, Ali, 2019. "Vulnerability analysis of power grid with the network science approach based on actual grid characteristics: A case study in Iran," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 14-21.
    5. Wilko Heitkoetter & Wided Medjroubi & Thomas Vogt & Carsten Agert, 2019. "Comparison of Open Source Power Grid Models—Combining a Mathematical, Visual and Electrical Analysis in an Open Source Tool," Energies, MDPI, vol. 12(24), pages 1-15, December.
    6. Zhaoming Yang & Qi Xiang & Yuxuan He & Shiliang Peng & Michael Havbro Faber & Enrico Zio & Lili Zuo & Huai Su & Jinjun Zhang, 2023. "Resilience of Natural Gas Pipeline System: A Review and Outlook," Energies, MDPI, vol. 16(17), pages 1-19, August.
    7. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    8. Arinushkin, P.A. & Vadivasova, T.E., 2021. "Nonlinear damping effects in a simplified power grid model based on coupled Kuramoto-like oscillators with inertia," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    9. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    10. Ferrario, E. & Poulos, A. & Castro, S. & de la Llera, J.C. & Lorca, A., 2022. "Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. repec:plo:pone00:0224522 is not listed on IDEAS
    12. Johan Rose Santos & Nur Diana Safitri & Maya Safira & Varun Varghese & Makoto Chikaraishi, 2021. "Road network vulnerability and city-level characteristics: A nationwide comparative analysis of Japanese cities," Environment and Planning B, , vol. 48(5), pages 1091-1107, June.
    13. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2011. "Maximal network reliability for a stochastic power transmission network," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1332-1339.
    14. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    15. Vaccariello, Enrico & Leone, Pierluigi & Canavero, Flavio G. & Stievano, Igor S., 2021. "Topological modelling of gas networks for co-simulation applications in multi-energy systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 244-253.
    16. Ma, Xiangyu & Zhou, Huijie & Li, Zhiyi, 2021. "On the resilience of modern power systems: A complex network perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    17. Juliane Manitz & Jonas Harbering & Marie Schmidt & Thomas Kneib & Anita Schöbel, 2017. "Source estimation for propagation processes on complex networks with an application to delays in public transportation systems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 521-536, April.
    18. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    19. Zio, E. & Golea, L.R. & Rocco S., C.M., 2012. "Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 172-177.
    20. Daniele Sora, 2015. "Energy Switch: a Home-Automation System for Renewable Energy Self-Consumption Optimization," DIAG Technical Reports 2015-13, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    21. Ziqi Wang & Jinghan He & Alexandru Nechifor & Dahai Zhang & Peter Crossley, 2017. "Identification of Critical Transmission Lines in Complex Power Networks," Energies, MDPI, vol. 10(9), pages 1-19, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:3531209. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.