Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/145708
Title: | A Hybrid Prediction Model for Damage Warning ofA hybrid prediction model for damage warning of power transmission line under typhoon disaster Power Transmission Line Under Typhoon Disaster | Authors: | Hou, Hui Yu, Shiwen Wang, Hao Xu, Yan Xiao, Xiang Huang, Yong Wu, Xixiu |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2020 | Source: | Hou, H., Yu, S., Wang, H., Xu, Y., Xiao, X., Huang, Y., & Wu, X. (2020). A hybrid prediction model for damage warning of power transmission line under typhoon disaster. IEEE Access, 8, 85038-85050. doi:10.1109/access.2020.2992528 | Journal: | IEEE Access | Abstract: | To bolster the resilience of power systems against typhoon disasters, this paper develops a holistic framework of wind disaster warning for transmission lines. This paper proposes a hybrid prediction model to quantify the transmission line damage probability under typhoon disaster based on extreme value type I probability distribution, Monte Carlo method, and Random Forest. Specifically, this paper uses the extreme value type I probability distribution and the Monte Carlo method to simulate the random wind field, and predict the damage probability of transmission lines under each wind field using the Random Forest method. This paper takes typhoon “Mangkhut” in 2018 as a case study, and compare the performance of the hybrid model based on random wind field with the Random Forest method under predicted and measured wind field. The results demonstrate that the hybrid model can effectively utilize wind speed data to obtain a more reliable prediction and achieves the best synthetic similarity to the actual damage situations. | URI: | https://hdl.handle.net/10356/145708 | ISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2020.2992528 | Rights: | © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Journal Articles |
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