Research on regional icing risk assessment based on icing influence factors
Authors: 
Qiang Fan, Jianrong Wu, Shuzhou Xiao, Qi Yang, Qili Zhang, Jiyulei Dai, Xiaoqiang ZhengAuthors Info & Claims
Pages 873 - 877
Abstract
Local meteorological factors undergo comprehensive variations within a small range due to terrain and other factors, which significantly impact the safe operation of the transmission line, making them distinct from those in larger areas. The weight index of each icing influence factor in the microtopographic area is calculated using the GBDT algorithm. Simultaneously, an evaluation index for regional icing risk level is constructed based on this weight index. The regional icing risk distribution map, calculated considering the weight index, can better account for the impact of different terrains within the same area.
References
[1]
Wen Yi, WU Jianrong, Jiang Su, Research on scientific evaluation of deployment position of multi-factor icing monitoring terminal based on comprehensive evaluation Algorithm [J]. Electric Power Big Data, 2021, 24(05):1-8.
[2]
Dandan L, Wei H, Hao D, Function design and realization of transmission line icing monitoring and early warning system combined with micro-meteorology and micro-topography in Guizhou [J]. Journal of Physics: Conference Series, 2023, 2584(1).
[3]
Wu Jianrong, Huang Huan, Jiang Su, Liu Dandan, PENG Chi. Based on high-precision micro-topographic factor extraction of tower and rational location evaluation of transmission line icing monitoring terminal [J]. Big Data of Electric Power, 2021, 24(09):25-32.
[4]
Cao Shuanghe, Tan Wei, Jiang Su, Li Yangsong. Research on tension value denoising technology of transmission line icing Monitoring System based on Wavelet Transform [J]. Electrotechnical Engineering, 2021. (13):53-55.
[5]
Fuqi M, Bo W, Min L, Edge Intelligent Perception Method for Power Grid Icing Condition Based on Multi-Scale Feature Fusion Target Detection and Model Quantization [J]. Frontiers in Energy Research, 2021, 9.
[6]
CAI Juan, LAN Yaxun, Liu Yuan. Research on LAN intrusion Location and detection based on GBDT optimization algorithm [J/OL]. Computer Measurement and Control :1-9. 2023.08.30.
[7]
Qiang F, Jianrong W, Shengyong F, Establishment and Application of Management Evaluation System for Transmission Line Icing Monitoring Terminal [J]. Journal of Physics: Conference Series, 2023, 2527(1).
[8]
ZHANG Hanbo, Yang Ji, Jing Wenlong Research on downscaling method of precipitation data with multiple characteristic factors combined with GBDT [J]. China Environmental Science, 2023, 43(04):1867-1882.
[9]
WANG Xueqing, Liu Shuang, Li Qiuyan SVM classification of tunnel surrounding rock based on K-fold cross-validation [J]. Mining and Metallurgy Engineering, 2021, 41(06):126-128+133.
[10]
Wang Yu, ZHAO Xiaoyan, Yang Xingli Confidence intervals for AUC measures based on K-fold cross-validation of Beta distributions [J]. Systems Science and Mathematics, 2019, 40(09): 1564-1577.
Index Terms
- Research on regional icing risk assessment based on icing influence factors
Recommendations
Research on the Influence of Blade Tip Icing on Natural Frequency of Wind Turbine
AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureIn order to study the influence of tip ice on fixed frequency of wind turbine blades, a numerical simulation scheme was proposed with S825 airfoil blades as the research object. By changing the length and thickness of the blade tip ice, the fixed ...
The development of audit detection risk assessment system: Using the fuzzy theory and audit risk model
The result of audit designation is significantly influenced by the audit evidence collected when planning the audit and the amount of audit evidence depends on the degree of detection risk. Therefore, when the assessment factors of detection risk are ...
Research on Risk Influencing Factors of Logistics Information Ecosystem Based on Interpretive Structural Modeling
EBIMCS '21: Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer ScienceBased on the research results of logistics information ecosystem risk, the author identifies 26 factors that affect logistics information ecosystem risk. The adjacency matrix of 26 influencing factors is constructed according to the relationship between ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 April 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
Hangzhou, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 4Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)1
Reflects downloads up to 24 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format