IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v215y2021ics0951832021004075.html
   My bibliography  Save this article

Research on a seismic connectivity reliability model of power systems based on the quasi-Monte Carlo method

Author

Listed:
  • Liu, Xiaohang
  • Zheng, Shansuo
  • Wu, Xinxia
  • Chen, Dianxin
  • He, Jinchuan

Abstract

To improve the speed of the error convergence of the Monte Carlo method in the seismic connectivity reliability assessment of power systems, the quasi-Monte Carlo method sampled by a low-discrepancy sequence is applied in the reliability evaluation. In addition, a triangle algorithm that can reduce the amount of computation in solving connectivity matrix is proposed to establish a seismic connectivity reliability calculation model with low-discrepancy sequence sampling. The ground motion attenuation model and the magnification effect of site soil were used to perform a seismic hazard analysis of the demonstration area, and the peak ground acceleration and the spatial distribution characteristics of the power system were obtained. Based on the results of the fragility analysis of a 110Â kV substation and a 330Â kV substation in Xi'an, Shaanxi Province, the standard Monte Carlo simulation and the Sobol sequence quasi-Monte Carlo simulation are carried out. The results show that with the same sampling number, the triangle algorithm has higher operational efficiency. Combining the triangle algorithm with the quasi-Monte Carlo method improves not only the accuracy but also the calculation speed.

Suggested Citation

  • Liu, Xiaohang & Zheng, Shansuo & Wu, Xinxia & Chen, Dianxin & He, Jinchuan, 2021. "Research on a seismic connectivity reliability model of power systems based on the quasi-Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004075
    DOI: 10.1016/j.ress.2021.107888
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021004075
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107888?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.
    2. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Kucherenko, S. & Rodriguez-Fernandez, M. & Pantelides, C. & Shah, N., 2009. "Monte Carlo evaluation of derivative-based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1135-1148.
    4. Baroud, Hiba & Barker, Kash, 2018. "A Bayesian kernel approach to modeling resilience-based network component importance," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 10-19.
    5. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    6. Michael O. Ball, 1979. "Computing Network Reliability," Operations Research, INFORMS, vol. 27(4), pages 823-838, August.
    7. Zio, E. & Golea, L.R., 2012. "Analyzing the topological, electrical and reliability characteristics of a power transmission system for identifying its critical elements," Reliability Engineering and System Safety, Elsevier, vol. 101(C), pages 67-74.
    8. Stern, R.E. & Song, J. & Work, D.B., 2017. "Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 1-9.
    9. BALL, Michael O., 1979. "Computing network reliability," LIDAM Reprints CORE 377, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Li Long & Shansuo Zheng & Yixin Zhang & Longfei Sun & Yan Zhou & Liguo Dong, 2020. "CEDLES: a framework for plugin-based applications for earthquake risk prediction and loss assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 531-556, August.
    11. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
    12. Hariri, Ali-Mohammad & Hashemi-Dezaki, Hamed & A. Hejazi, Maryam, 2020. "A novel generalized analytical reliability assessment method of smart grids including renewable and non-renewable distributed generations and plug-in hybrid electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    13. He, Xian & Cha, Eun Jeong, 2018. "Modeling the damage and recovery of interdependent critical infrastructure systems from natural hazards," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 162-175.
    14. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    15. Gatelli, D. & Kucherenko, S. & Ratto, M. & Tarantola, S., 2009. "Calculating first-order sensitivity measures: A benchmark of some recent methodologies," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1212-1219.
    16. Kucherenko, S. & Song, S., 2017. "Different numerical estimators for main effect global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 222-238.
    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. Zeng, Chen-dong & Qiu, Zhi-cheng & Zhang, Fen-hua & Zhang, Xian-min, 2023. "Error modelling and motion reliability analysis of a multi-DOF redundant parallel mechanism with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Wen, Jiayi & Li, Xiaoxuan & Xue, Jingwei, 2024. "Feasibility evaluation of Copula theory for substation equipment with multiple nonlinear-related seismic response indexes," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Huang, Xiubing & Wang, Naiyu, 2024. "An adaptive nested dynamic downscaling strategy of wind-field for real-time risk forecast of power transmission systems during tropical cyclones," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Cui, Hongjun & Wang, Fei & Ma, Xinwei & Zhu, Minqing, 2022. "A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

    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. Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
    2. Bigatti, A.M. & Pascual-Ortigosa, P. & Sáenz-de-Cabezón, E., 2021. "A C++ class for multi-state algebraic reliability computations," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Hougaard, Jens Leth & Moulin, Hervé, 2014. "Sharing the cost of redundant items," Games and Economic Behavior, Elsevier, vol. 87(C), pages 339-352.
    4. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Jane, Chin-Chia & Laih, Yih-Wenn, 2010. "A dynamic bounding algorithm for approximating multi-state two-terminal reliability," European Journal of Operational Research, Elsevier, vol. 205(3), pages 625-637, September.
    7. Jun, Tackseung & Kim, Jeong-Yoo, 2007. "Connectivity, stability and efficiency in a network as an information flow," Mathematical Social Sciences, Elsevier, vol. 53(3), pages 314-331, May.
    8. Cancela, Héctor & Petingi, Louis, 2007. "Properties of a generalized source-to-all-terminal network reliability model with diameter constraints," Omega, Elsevier, vol. 35(6), pages 659-670, December.
    9. Lawrence V. Snyder & Mark S. Daskin, 2005. "Reliability Models for Facility Location: The Expected Failure Cost Case," Transportation Science, INFORMS, vol. 39(3), pages 400-416, August.
    10. Jens Leth Hougaard & Hervé Moulin, 2018. "Sharing the cost of risky projects," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(3), pages 663-679, May.
    11. Fangyuan Han & Enrico Zio, 2018. "Modeling an electric power microgrid by model predictive control for analyzing its characteristics from reliability, controllability and topological perspectives," Journal of Risk and Reliability, , vol. 232(2), pages 216-224, April.
    12. Azzini, Ivano & Rosati, Rossana, 2021. "Sobol’ main effect index: an Innovative Algorithm (IA) using Dynamic Adaptive Variances," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Han, Fangyuan & Zio, Enrico, 2019. "A multi-perspective framework of analysis of critical infrastructures with respect to supply service, controllability and topology," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 1-13.
    14. Byun, Ji-Eun & Noh, Hee-Min & Song, Junho, 2017. "Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 410-421.
    15. Mehrdad Aslani & Hamed Hashemi-Dezaki & Abbas Ketabi, 2021. "Reliability Evaluation of Smart Microgrids Considering Cyber Failures and Disturbances under Various Cyber Network Topologies and Distributed Generation’s Scenarios," Sustainability, MDPI, vol. 13(10), pages 1-30, May.
    16. Fabian Dickmann & Nikolaus Schweizer, 2014. "Faster Comparison of Stopping Times by Nested Conditional Monte Carlo," Papers 1402.0243, arXiv.org.
    17. Turvey, Calum G., 2001. "Random Walks And Fractal Structures In Agricultural Commodity Futures Prices," Working Papers 34151, University of Guelph, Department of Food, Agricultural and Resource Economics.
    18. Grosen, Anders & Lochte Jorgensen, Peter, 2000. "Fair valuation of life insurance liabilities: The impact of interest rate guarantees, surrender options, and bonus policies," Insurance: Mathematics and Economics, Elsevier, vol. 26(1), pages 37-57, February.
    19. E. Nasakkala & J. Keppo, 2008. "Hydropower with Financial Information," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(5-6), pages 503-529.
    20. Song-Ping Zhu & Xin-Jiang He, 2018. "A hybrid computational approach for option pricing," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 1-16, September.

    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:eee:reensy:v:215:y:2021:i:c:s0951832021004075. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.