Identification of Key Components After Unintentional Failures for Cascading Failure Protection
Identification of Key Components After Unintentional Failures for Cascading Failure Protection
复制标题
连锁故障保护中意外故障后关键部件的识别
DOI：
10.1109/tnse.2022.3225459
复制
发表时间：
2023-03
影响因子：
6.6
通讯作者：
Xiang Li;Tianyi Pan;Kai Pan
中科院分区：
文献类型：
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作者：
Xiang Li;Tianyi Pan;Kai Pan
连锁故障会加剧电网的脆弱性，引起了人们对连锁故障保护研究的关注。现有的工作集中在寻找故障可能导致大规模停电的关键部件，或者在故障发生后缓解故障的方法。然而，它们不能主动防御现实世界中的故障，这种故障可能不仅仅发生在关键组件上。在本文中，我们研究了在意外的初始故障后寻找受影响最大的部件的问题，这适合于实际场景的需要。这个问题具有挑战性，因为模拟大量连锁故障等方法不能扩展，并且必须在电力网络参数变化时重新进行。为了解决这个问题，我们推导了一个基于所有路径的线路重要性度量，并直观地和IEEE测试用例说明了它是如何与意外故障后受高度影响的线路关联的。此外，我们设计了一种路径采样算法，在可证明的保证下估计度量，并实现可伸缩性。我们使用不同的IEEE测试用例对该方法在保护场景中的性能进行了评估，并证明了该方法相对于几种基准方法的优越性。
Cascading failure can aggravate the vulnerability of power grids, which brings attention to cascading failure protection research. Existing works focus on either finding the critical components whose failure can cause large-scale blackouts or methods to mitigate failures after they have happened. However, they are not able to proactively protect against real-world failures, which may not only happen at the critical components. In this paper, we study the problem of finding components that will be impacted the most after unintentional initial failures, which suits the need for practical scenarios. The problem is challenging since approaches like simulating a large number of cascading failures cannot scale and they must be redone when power network parameters change. To tackle the problem, we derive a line importance metric based on all paths and illustrate how it is correlated with highly impacted lines after unintentional failure both intuitively and with an IEEE test case. Further, we design a path sampling algorithm to estimate the metric with provable guarantee and achieve scalability. We evaluate the performance of the proposed method within a protection scenario using various IEEE test cases and demonstrate its superiority against several baseline methods.