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地震灾害模拟及配电网的风险评估 被引量:8

Earthquake Simulation and Risk Assessment of Distribution Network
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摘要 地震对电力系统的损害极为严重,而配电网的抵御能力相对输电网来说更为脆弱,因此有必要对遭受地震灾害的配电网进行风险评估。该文首先根据地震动参数衰减方程估算出配电网区域的地震动强度,结合配电网设备的脆弱性模型计算出受损设备的比例;然后通过蒙特卡洛模拟随机地震,寻找地震中概率最大的配电网故障重数以进行故障场景模拟;综合考虑配电网的运行方式及拓扑结构,以复杂网络理论的网络中心性评估配电网网架的受损性,进行随机地震后配电网的风险评估,以辨识出配电网的薄弱点供灾前预防;最后以四川靠近龙门山断裂带的某10kV配电网为例,进行地震灾害下配电网的风险评估,验证了该文所提方法的可行性。 The power system has been severely damaged in earthquake.The distribution network is more vulnerable to extreme natural disasters than the transmission network.Therefore,it is necessary to analyze the risk of distribution network in earthquake disaster.The ground motion intensity in a distribution network area is calculated by parameter attenuation equation of ground motion.The proportion of damaged equipment is obtained on vulnerability model of distribution network components.Based on Monte Carlo method,stochastic earthquake scenarios are simulated to get the contingency levels of distribution network equipment.Considering the topology and operation of the distribution network,the network centrality of the complex network theory is used to evaluate the damage of the distribution network,and the risk assessment of the distribution network after the random earthquake is carried out to identify the key equipment of the distribution network.Accordingly,the resistance of distribution network to earthquake disasters is improved.Taking the 10kV distribution network in Longmenshan fault zone as an example,the risk is evaluated in a stochastic earthquake simulation,which verifies the feasibility of the proposed method.
作者 郑国鑫 雷霞 王湘 罗小春 Zheng Guoxin;Lei Xia;Wang Xiang;Luo Xiaochun(College of Electrical and Electronic Information Xihua University,Chengdu 610039 China;Aba Power Supply Company State Grid Sichuan Electric Power Company,Aba 602300 China)
出处 《电工技术学报》 EI CSCD 北大核心 2020年第24期5218-5226,共9页 Transactions of China Electrotechnical Society
基金 四川省科技厅项目(2019YFG0153) 四川省教育厅重大培育项目(18CZ0018) 西华大学研究生创新基金项目(ycjj2019053)资助。
关键词 地震 风险分析 脆弱性模型 蒙特卡罗模拟 网络中心性 Seismic risk analysis vulnerability model Monte Carlo stochastic simulating network centrality
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