• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

预测强风作用下动态导体摇摆引起的野火点火。

Predicting wildfire ignition induced by dynamic conductor swaying under strong winds.

机构信息

Department of Civil and Environmental Engineering, Catastrophe Modeling Center, ATLSS Engineering Research Center, Lehigh University, Bethlehem, 18015, USA.

出版信息

Sci Rep. 2023 Mar 10;13(1):3998. doi: 10.1038/s41598-023-30802-w.

DOI:10.1038/s41598-023-30802-w
PMID:36899017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10006223/
Abstract

During high wind events with dry weather conditions, electric power systems can be the cause of catastrophic wildfires. In particular, conductor-vegetation contact has been recognized as the major ignition cause of utility-related wildfires. There is a urgent need for accurate wildfire risk analysis in support of operational decision making, such as vegetation management or preventive power shutoffs. This work studies the ignition mechanism caused by transmission conductor swaying out to nearby vegetation and resulting in flashover. Specifically, the studied limit state is defined as the conductor encroaching into prescribed minimum vegetation clearance. The stochastic characteristics of the dynamic displacement response of a multi-span transmission line are derived through efficient spectral analysis in the frequency domain. The encroachment probability at a specified location is estimated by solving a classical first-excursion problem. These problems are often addressed using static-equivalent models. However, the results show that the contribution of random wind buffeting to the conductor dynamic displacement is appreciable under turbulent strong winds. Neglecting this random and dynamic component can lead to an erroneous estimation of the risk of ignition. The forecast duration of the strong wind event is an important parameter to determine the risk of ignition. In addition, the encroachment probability is found highly sensitive to vegetation clearance and wind intensity, which highlights the need of high resolution data for these quantities. The proposed methodology offers a potential avenue for accurate and efficient ignition probability prediction, which is an important step in wildfire risk analysis.

摘要

在大风干燥天气事件中,电力系统可能是灾难性野火的原因。特别是,导体-植被接触已被认为是与公用事业相关的野火的主要点火原因。需要进行准确的野火风险分析,以支持运营决策,例如植被管理或预防性停电。这项工作研究了输电导线摆动到附近植被并导致闪络的点火机制。具体来说,所研究的极限状态定义为导体侵入规定的最小植被净空。通过在频域中进行有效的谱分析,推导出多跨输电线路动态位移响应的随机特征。通过求解经典的首次穿越问题来估计指定位置的侵入概率。这些问题通常使用静态等效模型来解决。然而,结果表明,在强风湍流中,随机风冲击对导体动态位移的贡献是相当可观的。忽略此随机和动态分量可能会导致点火风险的错误估计。强风事件的预测持续时间是确定点火风险的重要参数。此外,侵入概率对植被净空和风速强度非常敏感,这突出了这些数量的高分辨率数据的必要性。所提出的方法为准确高效的点火概率预测提供了一种潜在途径,这是野火风险分析的重要步骤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/fb7e2f88ee14/41598_2023_30802_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/a58754e9479e/41598_2023_30802_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/93df89b95037/41598_2023_30802_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/2a2d323cd8e0/41598_2023_30802_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/053f9381be94/41598_2023_30802_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/d84e8d58fd3b/41598_2023_30802_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/1e99af7423d9/41598_2023_30802_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/3bf22aed91f3/41598_2023_30802_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/901598bb6177/41598_2023_30802_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/df0f6c520c10/41598_2023_30802_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/879db081717c/41598_2023_30802_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/9f47963f2578/41598_2023_30802_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/14e8ddc0d732/41598_2023_30802_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/7db05d75a158/41598_2023_30802_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/4ad0779b00f9/41598_2023_30802_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/07a05a9ccf17/41598_2023_30802_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/fb7e2f88ee14/41598_2023_30802_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/a58754e9479e/41598_2023_30802_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/93df89b95037/41598_2023_30802_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/2a2d323cd8e0/41598_2023_30802_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/053f9381be94/41598_2023_30802_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/d84e8d58fd3b/41598_2023_30802_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/1e99af7423d9/41598_2023_30802_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/3bf22aed91f3/41598_2023_30802_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/901598bb6177/41598_2023_30802_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/df0f6c520c10/41598_2023_30802_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/879db081717c/41598_2023_30802_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/9f47963f2578/41598_2023_30802_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/14e8ddc0d732/41598_2023_30802_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/7db05d75a158/41598_2023_30802_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/4ad0779b00f9/41598_2023_30802_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/07a05a9ccf17/41598_2023_30802_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c75a/10006223/fb7e2f88ee14/41598_2023_30802_Fig16_HTML.jpg

相似文献

1
Predicting wildfire ignition induced by dynamic conductor swaying under strong winds.预测强风作用下动态导体摇摆引起的野火点火。
Sci Rep. 2023 Mar 10;13(1):3998. doi: 10.1038/s41598-023-30802-w.
2
Short- and long-term wildfire threat when adapting infrastructure for wildlife conservation in the boreal forest.在北方森林为保护野生动物而调整基础设施时,短期和长期的野火威胁。
Ecol Appl. 2022 Sep;32(6):e2606. doi: 10.1002/eap.2606. Epub 2022 May 16.
3
A wildfire growth prediction and evaluation approach using Landsat and MODIS data.利用 Landsat 和 MODIS 数据的野火生长预测和评估方法。
J Environ Manage. 2022 Feb 15;304:114351. doi: 10.1016/j.jenvman.2021.114351. Epub 2021 Dec 21.
4
Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy.分析意大利撒丁岛野火暴露因素的季节性模式。
Environ Monit Assess. 2015 Jan;187(1):4175. doi: 10.1007/s10661-014-4175-x. Epub 2014 Dec 4.
5
Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia.开发和测试澳大利亚东南部人为和闪电引发野火点火源的模型。
J Environ Manage. 2019 Apr 1;235:34-41. doi: 10.1016/j.jenvman.2019.01.055. Epub 2019 Jan 19.
6
Flammability properties of British heathland and moorland vegetation: models for predicting fire ignition.英国石南荒原和高沼地植被的可燃性特征:预测火灾点火的模型。
J Environ Manage. 2014 Jun 15;139:88-96. doi: 10.1016/j.jenvman.2014.02.027. Epub 2014 Mar 27.
7
Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires.在地形、植被和大型、风力驱动的野火背景下的燃料处理效果。
Ecol Appl. 2020 Jul;30(5):e02104. doi: 10.1002/eap.2104. Epub 2020 Apr 2.
8
Some Wildfire Ignition Causes Pose More Risk of Destroying Houses than Others.一些野火起火原因比其他原因更有可能导致房屋被摧毁。
PLoS One. 2016 Sep 6;11(9):e0162083. doi: 10.1371/journal.pone.0162083. eCollection 2016.
9
Quantitative assessment of wildfire risk in oil facilities.量化评估油库的火灾风险。
J Environ Manage. 2018 Oct 1;223:433-443. doi: 10.1016/j.jenvman.2018.06.062. Epub 2018 Jun 26.
10
Exploratory analysis of lightning-ignited wildfires in the Warren Region, Western Australia.探索性分析西澳大利亚州沃伦地区闪电引发的野火。
J Environ Manage. 2018 Nov 1;225:336-345. doi: 10.1016/j.jenvman.2018.07.097. Epub 2018 Aug 1.

引用本文的文献

1
New York State Climate Impacts Assessment Chapter 04: Buildings.纽约州气候影响评估 第04章:建筑
Ann N Y Acad Sci. 2024 Dec;1542(1):214-252. doi: 10.1111/nyas.15200. Epub 2024 Dec 9.

本文引用的文献

1
Towards a comprehensive wildfire management strategy for Mediterranean areas: Framework development and implementation in Catalonia, Spain.迈向地中海地区综合性野火管理策略:西班牙加泰罗尼亚的框架制定与实施。
J Environ Manage. 2019 Feb 1;231:303-320. doi: 10.1016/j.jenvman.2018.10.027. Epub 2018 Oct 22.