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Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms

Author

Listed:
  • Hui Hou

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

  • Shiwen Yu

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

  • Hongbin Wang

    (Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China)

  • Yong Huang

    (GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China)

  • Hao Wu

    (GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China)

  • Yan Xu

    (Department, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Xianqiang Li

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

  • Hao Geng

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

Abstract

For power system disaster prevention and mitigation, risk assessment and visualization under typhoon disaster have important scientific significance and engineering value. However, current studies have problems such as incomplete factors, strong subjectivity, complicated calculations, and so on. Therefore, a novel risk assessment and its visualization system consisting of a data layer, knowledge extraction layer, and visualization layer on power towers under typhoon disaster are proposed. On the data layer, a spatial multi-source heterogeneous information database is built based on equipment operation information, meteorological information, and geographic information. On the knowledge extraction layer, six intelligent risk prediction models are established based on machine learning algorithms by hyperparameter optimization. Then the relative optimal model is selected by comparing five evaluation indicators, and the combined model consisting of five relatively superior models is established by goodness of fit method with unequal weight. On the visualization layer, the predicted results are visualized with accuracy of 1 km × 1 km by ArcGIS 10.4. In results, the power tower damage risk assessment is carried out in a Chinese coastal city under the typhoon ‘Mujigae’. By comparing predicted distribution and similarity indicator of the combined model with those of the other models, it is shown that the combined model is superior not only in quality but also in quantity.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:205-:d:196272
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    References listed on IDEAS

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    1. Liu, Haibin & Davidson, Rachel A. & Apanasovich, Tatiyana V., 2008. "Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 897-912.
    2. Seung‐Ryong Han & Seth D. Guikema & Steven M. Quiring, 2009. "Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models," Risk Analysis, John Wiley & Sons, vol. 29(10), pages 1443-1453, October.
    3. Han, Seung-Ryong & Guikema, Seth D. & Quiring, Steven M. & Lee, Kyung-Ho & Rosowsky, David & Davidson, Rachel A., 2009. "Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 199-210.
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    Cited by:

    1. 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.

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