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Risk of Cascading Blackouts Given Correlated Component Outages

机译:级联停电的风险给定相关组件中断

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Cascading blackouts typically occur when nearly simultaneous outages occur in k out of N components in a power system, triggering subsequent failures that propagate through the network and cause significant load shedding. While large cascades are rare, their impact can be catastrophic, so quantifying their risk is important for grid planning and operation. A common assumption in previous approaches to quantifying such risk is that the k initiating component outages are statistically independent events. However, when triggered by a common exogenous cause, initiating outages may actually be correlated. Here, copula analysis is used to quantify the impact of correlation of initiating outages on the risk of cascading failure. The method is demonstrated on two test cases; a 2383-bus model of the Polish grid under varying load conditions and a synthetic 10 000-bus model based on the geography of the Western US. The large size of the Western US test case required development of new approaches for bounding an estimate of the total number of N-3 blackout-causing contingencies. The results suggest that both risk of cascading failure, and the relative contribution of higher order contingencies, increase as a function of spatial correlation in component failures.
机译:当在电力系统中的N个组件中发生几乎同时停电时,通常会发生级联的停电,触发通过网络传播的后续故障并导致显着的负载脱落。虽然大型瀑布很少见,但它们的影响可能是灾难性的,因此量化其风险对于电网规划和运营很重要。在以前的量化这些风险的方法中的一个共同假设是K启动组件中断是统计上独立的事件。然而,当通过常见的外源性原因引发时,启动中断实际上可以相关。这里,Copula分析用于量化引发中断对级联失败风险的影响。该方法在两个测试用例上进行了说明;不同负载条件下波兰网格的2383总线模型及基于美国西部地理的合成10 000总线模型。西部美国的大尺寸测试案件需要开发新方法,以估算N-3导致造成的突发事件的总数。结果表明,级联失败的风险以及高阶突发事件的相对贡献,随着组件故障中的空间相关性而增加。

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