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Energies 2019, 12(2), 205; https://doi.org/10.3390/en12020205

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

1
School of Automation, Wuhan University of Technology, Wuhan 430070, China
2
Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510620, China
3
GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China
4
Department, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Received: 20 November 2018 / Revised: 25 December 2018 / Accepted: 4 January 2019 / Published: 9 January 2019
(This article belongs to the Section Energy Fundamentals and Conversion)
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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. View Full-Text
Keywords: typhoon; power tower; risk assessment; visualization; machine learning; intelligent prediction model typhoon; power tower; risk assessment; visualization; machine learning; intelligent prediction model
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Hou, H.; Yu, S.; Wang, H.; Huang, Y.; Wu, H.; Xu, Y.; Li, X.; Geng, H. Risk Assessment and Its Visualization of Power Tower under Typhoon Disaster Based on Machine Learning Algorithms. Energies 2019, 12, 205.

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