Article(id=1281627201061761405, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.1266, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1789056000000, receivedDateStr=2026-09-11, revisedDate=1763136000000, revisedDateStr=2025-11-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493336214, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493336214, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493336214, creator=13701087609, updateTime=1783493336214, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=157, endPage=166, ext={EN=ArticleExt(id=1281627201288253822, articleId=1281627201061761405, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Risk assessment of new urban power systems under extreme weather conditions based on BN-MC, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

In order to address the significant safety risks posed by frequent extreme weather to the source, grid, and load-side equipment of the new power system, a risk assessment model for urban new power systems under extreme weather conditions was proposed. First, risk factors of the urban new power system were identified based on disaster theory, and an ISM was applied to clarify the interrelationships among these risk factors. Subsequently, the topological structure of the disaster chain was mapped into a BN. The prior probabilities of each risk factor node were determined using fuzzy comprehensive evaluation and accident statistics. Sensitivity analysis and scenario analysis were employed to derive key risk nodes for urban new power system accidents and the consequences of multi-hazard coupled accidents. Finally, MC simulation was utilized to conduct operational optimization analysis on "transmission towers" from the perspective of wind resistance level design. The results indicate that the constructed BN-MC coupled model effectively quantifies and enhances the analysis of extreme weather risks in urban new power systems. Under multiple superimposed extreme weather conditions, the failure probability of photovoltaic generators reaches as high as 60%, with strong winds being the key driving factor. Furthermore, improving the wind resistance level of transmission towers significantly reduces their failure probability. At a real-time wind speed of 36 km/h, increasing the wind resistance level from 35 km/h to 40 km/h reduces the failure probability by 59.39%. This effect exhibits a nonlinear characteristic, with a greater reduction in risk probability in the low wind speed range than in the medium wind speed range.

, authors=Kunqi LIU¹, Juan YANG¹, Ziyi LI¹, Peng LI², Jiansong WU¹, Chang LIU^{**, 2}, authorsList=Kunqi LIU, Juan YANG, Ziyi LI, Peng LI, Jiansong WU, Chang LIU, authorCompany=null, correspAuthors=Chang LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281627201883844991, articleId=1281627201061761405, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于BN-MC的极端天气下城市新型电力系统风险评估, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为缓解极端天气频发对新型电力系统的源、网、荷侧设备构成的重大安全风险，提出一种面向极端天气的城市新型电力系统风险评估模型。首先，基于灾害理论辨识城市新型电力系统的风险因素，并借助解释结构模型（ISM）梳理风险因素间的影响关系；然后，将灾害链拓扑结构映射成为贝叶斯网络（BN），并通过模糊综合评价和事故统计确定各风险因素节点的先验概率，运用敏感性分析和情景分析得出城市新型电力系统事故关键风险节点和多灾害耦合事故后果；最后，借助蒙特卡罗（MC）模拟，对敏感性较高的“杆塔”节点开展运行优化分析。结果表明：BN-MC耦合模型可有效实现城市新型电力系统极端天气风险的量化评估与提升分析，多重极端天气叠加时，光伏发电机组故障概率高达60%，且强风是其故障的关键驱动因素；其次，提升杆塔抗风等级对降低其失效概率效果显著，在实时风速36 km/h时，抗风等级从35 km/h提升至40 km/h，可使失效概率下降59.39%，且该效果呈现非线性特征，低风速区段的风险概率降幅大于中风速区段。

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刘坤琦（2000—），女，山西太原人，博士研究生，主要研究方向为安全科学。E-mail: taylorkqi@163.com。

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刘坤琦（2000—），女，山西太原人，博士研究生，主要研究方向为安全科学。E-mail: taylorkqi@163.com。

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