极端天气下城市新型电力系统风险评估方法研究

刘畅; 孙秋洁; 张新伟; 李鹏; 周辉; 张思航; 李军辉

doi:10.13637/j.issn.1009-6094.2025.120310.13637/j.issn.1009-6094.2025.1203

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极端天气下城市新型电力系统风险评估方法研究

更新时间：2026-04-17

- 极端天气下城市新型电力系统风险评估方法研究
- Vol. 26, Issue 4, Pages: 1255-1265(2026)
- 作者机构：
  
  1. 国网电力工程研究院有限公司
  2. 国网浙江省电力有限公司经济技术研究院
  3. 清华大学安全科学学院
  4. 国网浙江省电力有限公司金华供电公司
- 作者简介：
- 基金信息：
- DOI：10.13637/j.issn.1009-6094.2025.120310.13637/j.issn.1009-6094.2025.1203
  CLC： TM73;P429
- Published：2026
- 稿件说明：
移动端阅览
[1]刘畅,孙秋洁,张新伟,等.极端天气下城市新型电力系统风险评估方法研究[J].安全与环境学报,2026,26(04):1255-1265.
[1]刘畅,孙秋洁,张新伟,等.极端天气下城市新型电力系统风险评估方法研究[J].安全与环境学报,2026,26(04):1255-1265. DOI： 10.13637/j.issn.1009-6094.2025.120310.13637/j.issn.1009-6094.2025.1203.

DOI：10.13637/j.issn.1009-6094.2025.1203.

摘要

随着新能源占比持续提升，叠加台风、强降雨等极端天气影响，城市新型电力系统面临多重安全风险辨识及量化评估难题，传统的风险评估方法在应对多重风险协同作用及耦合加剧的量化分析方面存在局限性，因此，提出了一种知识图谱与PageRank算法结合的极端天气下城市新型电力系统风险评估方法。首先，以极端天气引发的城市新型电力系统故障事故案例为数据源，基于规则与深度学习的知识抽取方式对事故案例进行知识抽取，并借助Neo4j构建包含“灾害类型、事故类型、故障类型、事故部位、事故后果”五类属性的风险知识图谱。随后，结合PageRank算法与介数中心性指标，分析了城市新型电力系统风险知识图谱中的关键风险节点。结果表明：在案例事故类型中，设备本体损坏的PageRank值为1.025 5、介数中心性为669.07

两项指标均处于高位表明该事故影响范围广泛，且在风险演化过程中处于关键路径；故障类型中，断线的介数中心性相对较高(为32.05)

而PageRank值相对较低(为0.294 3)

表明该故障发生概率较低但具备高传播影响力。同时架空线路是新型电力系统中较容易发生故障的部位，应加强该环节的状态监测并根据极端天气强度提升线路的抗灾能力。

Abstract

As the proportion of new energy sources continues to rise

the power system faces significant uncertainties in generation

transmission

load

and storage. Coupled with the impact of extreme weather events such as typhoons and heavy rainfall

new urban power systems encounter multiple challenges in identifying and quantitatively assessing risks. Traditional risk assessment methods are limited in their ability to analyze multiple risks and their interconnections quantitatively. Therefore

this paper proposes a risk assessment method for new urban power systems under extreme weather conditions by combining knowledge graphs with the PageRank algorithm. First

case studies of power system failures due to extreme weather serve as the data source for knowledge extraction

utilizing a combination of rule-based and deep learning approaches. A risk knowledge graph is then constructed using Neo4j

incorporating five types of attributes: ‘disaster type'

‘failure type'

‘accident type'

‘accident location'

and ‘accident consequences'. Next

the PageRank algorithm and degree centrality metrics are employed to analyze key risk nodes within the urban power system's risk knowledge graph. In the case of accident types

the PageRank value for equipment body damage is 1.025 5

and the betweenness centrality is 669.07. Both indicators being high suggests that the impact of this accident is widespread and that it lies on a critical path in the risk evolution process. Among failure types

the betweenness centrality for disconnection is relatively high at 32.05

while the PageRank value is relatively low at 0.294 3

indicating that this fault has a low probability of occurrence but possesses high propagation influence. Additionally

overhead lines emerge as the most critical failure points in the new power system. Therefore

enhancing the monitoring of transmission line status and improving their disaster resistance capabilities are essential.

关键词

Keywords

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