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Title: Risk assessment and its visualization of power tower under typhoon disaster based on machine learning algorithms
Authors: Hou, Hui
Yu, Shiwen
Wang, Hongbin
Huang, Yong
Wu, Hao
Xu, Yan
Li, Xianqiang
Geng, Hao
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Power Tower
Issue Date: 2019
Source: Hou, H., Yu, S., Wang, H., Huang, Y., Wu, H., Xu, Y., . . . Geng, H. (2019). Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms. Energies, 12(2), 205-. doi:10.3390/en12020205
Series/Report no.: Energies
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.
ISSN: 1996-1073
DOI: 10.3390/en12020205
Rights: © 2019 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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