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

Hui Hou, Shiwen Yu, Hongbin Wang, Yong Huang, Hao Wu, Yan Xu, Xianqiang Li and Hao Geng
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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

Energies, 2019, vol. 12, issue 2, 1-23

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.

Keywords: typhoon; power tower; risk assessment; visualization; machine learning; intelligent prediction model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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