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灾害性天气电力系统风险评估算法及试验研究

Research on risk assessment algorithm and experimental investigation for power systems under extreme weather conditions

  • 摘要: 为刻画灾害性天气对电力系统停电风险事故的时空演变特征与叠加效应,提出一种基于数学建模的时空域灾害性天气电力系统风险评估算法并进行模拟试验研究。首先,针对风暴(以台风为代表)、霜冻和暴雨三种典型灾害性天气场景,结合灾害性天气的运动轨迹与影响范围,建立灾害性天气强度算法模型;随后,构建“风暴+暴雨”和“风暴+霜冻”两类复合灾害性天气情景试验,依据灾害性天气强度计算各电力单元故障概率,并分析其时空演变特征;最后,依据故障概率的时空分布特征划分低、中、高风险等级区域。结果表明,配电设备故障概率随灾害性天气中心位置变化呈现明显的时空迁移特征,风险区域沿灾害性天气移动路径动态演变。其中,风暴+暴雨场景下中高风险区域的覆盖范围更广,风险叠加效应更为明显;风暴+霜冻场景下高风险区域主要集中于风暴影响区与霜冻影响区的交汇区域。该算法能有效追踪复合灾害性天气下电力故障高风险区域的动态转移路径与演变趋势,生成直观的时空风险图谱,为灾害性天气来临前优化电力系统应急资源布局、事中实施精准预警与主动调控提供技术支撑。

     

    Abstract: To characterize the spatiotemporal evolution and superposition effects of hazardous weather on power system outage risk, a mathematical-modeling-based spatiotemporal risk assessment algorithm for power systems under hazardous weather conditions is proposed and evaluated through simulation experiments. First, intensity models are established for three typical hazardous weather scenarios—storms represented by typhoons, frost, and rainstorms—by incorporating their movement trajectories and impact ranges. Subsequently, two compound hazardous weather scenarios, namely “storm + rainstorm” and “storm + frost,” are constructed. The failure probability of each power system unit is calculated according to the hazardous weather intensity, and its spatiotemporal evolution characteristics are analyzed. Finally, low-, medium-, and high-risk regions are classified based on the spatiotemporal distribution of failure probabilities. The results show that the failure probability of distribution equipment exhibits pronounced spatiotemporal migration as the centers of hazardous weather events move, with risk regions dynamically evolving along the movement paths of the hazardous weather systems. In the storm + rainstorm scenario, the medium- and high-risk regions cover a wider area and exhibit a more pronounced risk superposition effect. In contrast, under the storm + frost scenario, high-risk regions are mainly concentrated in the overlapping areas affected by both the storm and frost. The proposed algorithm can effectively track the dynamic migration paths and evolution trends of high-risk regions for power equipment failures under compound hazardous weather conditions and generate intuitive spatiotemporal risk maps, thereby providing technical support for optimizing emergency resource allocation in power systems before hazardous weather events and for implementing precise early warning and proactive control during such events..

     

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