IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i8p1447-d223100.html
   My bibliography  Save this article

Damage Probability Assessment of Transmission Line-Tower System Under Typhoon Disaster, Based on Model-Driven and Data-Driven Views

Author

Listed:
  • Hui Hou

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Hao Geng

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Yong Huang

    (Electric Power Research Institute, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Hao Wu

    (Electric Power Research Institute, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Xixiu Wu

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Shiwen Yu

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Under the typhoon disaster, the power grid often has serious accidents caused by falling power towers and breaking lines. It is of great significance to analyze and predict the damage probability of a transmission line-tower system for disaster prevention and reduction. However, some problems existing in current models, such as complicated calculation, few factors, and so on, affect the accuracy of the prediction. Therefore, a damage probability assessment method of a transmission line-tower system under a typhoon disaster is proposed. Firstly, considering the actual wind load and the design wind load, physical models for calculating the damage probability of the transmission line and power tower are established, respectively based on model-driven thought. Then, the damage probability of the transmission line-tower system is obtained, combining the transmission line and power tower damage probability. Secondly, in order to improve prediction accuracy, this paper analyzes the historical sample data containing multiple influencing factors, such as geographic information, meteorological information, and power grid information, and then obtains the correction coefficient based on data-driven thought. Thirdly, the comprehensive damage probability of the transmission line-tower system is calculated considering the results of model-driven and data-driven thought. Ultimately, the proposed method is verified to be effective, taking typhoon ‘Mangkhut’ in 2018 as a case study.

Suggested Citation

  • Hui Hou & Hao Geng & Yong Huang & Hao Wu & Xixiu Wu & Shiwen Yu, 2019. "Damage Probability Assessment of Transmission Line-Tower System Under Typhoon Disaster, Based on Model-Driven and Data-Driven Views," Energies, MDPI, vol. 12(8), pages 1-17, April.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1447-:d:223100
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/8/1447/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/8/1447/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roshanak Nateghi & Seth D. Guikema & Steven M. Quiring, 2011. "Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes," Risk Analysis, John Wiley & Sons, vol. 31(12), pages 1897-1906, December.
    2. Hui Hou & Shiwen Yu & Hongbin Wang & Yong Huang & Hao Wu & Yan Xu & Xianqiang Li & Hao Geng, 2019. "Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms," Energies, MDPI, vol. 12(2), pages 1-23, January.
    3. Guikema, S.D. & Quiring, S.M., 2012. "Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 178-182.
    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. Xinsheng Dong & Guanru Wen & Mingguan Zhao & Yang Yang & Meng Li & Long Zhao, 2023. "Study of the Prevention Method of ±800 kV Transmission Tower Foundation Deviation," Energies, MDPI, vol. 16(6), pages 1-17, March.
    2. Minhui Qian & Ning Chen & Yuge Chen & Changming Chen & Weiqiang Qiu & Dawei Zhao & Zhenzhi Lin, 2021. "Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment," Energies, MDPI, vol. 14(13), pages 1-35, June.
    3. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(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. Hou, Hui & Liu, Chao & Wei, Ruizeng & He, Huan & Wang, Lei & Li, Weibo, 2023. "Outage duration prediction under typhoon disaster with stacking ensemble learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Berk A. Alpay & David Wanik & Peter Watson & Diego Cerrai & Guannan Liang & Emmanouil Anagnostou, 2020. "Dynamic Modeling of Power Outages Caused by Thunderstorms," Forecasting, MDPI, vol. 2(2), pages 1-12, May.
    3. Jichao He & David W. Wanik & Brian M. Hartman & Emmanouil N. Anagnostou & Marina Astitha & Maria E. B. Frediani, 2017. "Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 441-458, March.
    4. Jin‐Hua Chen & Chun‐Shu Chen & Meng‐Fan Huang & Hung‐Chih Lin, 2016. "Estimating the Probability of Rare Events Occurring Using a Local Model Averaging," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1855-1870, October.
    5. Zhai, Chengwei & Chen, Thomas Ying-jeh & White, Anna Grace & Guikema, Seth David, 2021. "Power outage prediction for natural hazards using synthetic power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Rogerson, Ellen C. & Lambert, James H., 2012. "Prioritizing risks via several expert perspectives with application to runway safety," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 22-34.
    8. Monsalve, Mauricio & de la Llera, Juan Carlos, 2019. "Data-driven estimation of interdependencies and restoration of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 167-180.
    9. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    10. Diana Mitsova & Ann-Margaret Esnard & Alka Sapat & Betty S. Lai, 2018. "Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma," 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. 94(2), pages 689-709, November.
    11. Yi‐Ping Fang & Giovanni Sansavini & Enrico Zio, 2019. "An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1949-1969, September.
    12. Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    13. Roshanak Nateghi & Seth D. Guikema & Yue (Grace) Wu & C. Bayan Bruss, 2016. "Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 4-15, January.
    14. Seung‐Ryong Han & David Rosowsky & Seth Guikema, 2014. "Integrating Models and Data to Estimate the Structural Reliability of Utility Poles During Hurricanes," Risk Analysis, John Wiley & Sons, vol. 34(6), pages 1079-1094, June.
    15. Kairui Feng & Min Ouyang & Ning Lin, 2022. "Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    16. Hossain, Eklas & Roy, Shidhartho & Mohammad, Naeem & Nawar, Nafiu & Dipta, Debopriya Roy, 2021. "Metrics and enhancement strategies for grid resilience and reliability during natural disasters," Applied Energy, Elsevier, vol. 290(C).
    17. Rachunok, Benjamin & Nateghi, Roshanak, 2020. "The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    18. Bao, Minglei & Ding, Yi & Sang, Maosheng & Li, Daqing & Shao, Changzheng & Yan, Jinyue, 2020. "Modeling and evaluating nodal resilience of multi-energy systems under windstorms," Applied Energy, Elsevier, vol. 270(C).
    19. Panagiotis K. Marhavilas & Michael G. Tegas & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2020. "A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study," Sustainability, MDPI, vol. 12(10), pages 1-21, May.
    20. Jennifer S Dargin & Ali Mostafavi, 2020. "Human-centric infrastructure resilience: Uncovering well-being risk disparity due to infrastructure disruptions in disasters," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-29, June.

    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:gam:jeners:v:12:y:2019:i:8:p:1447-:d:223100. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.